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REVIEW 4 major objections 5 minor 257 references

Towards General Language-Conditioned Latent Safety Filters

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Language-conditioned safety filters can reduce constraint violations and transfer partially to unseen constraint instances.

desk verdict Honest and well-scoped, but the VL filter still leans on a privileged failure function and every comparison rests on single-seed 50-episode runs. read the letter →

arxiv 2608.00315 v1 pith:WREBLMOW submitted 2026-07-31 cs.RO cs.LGcs.SYeess.SY

Ihab Tabbara , Yuxuan Yang , Hussein Sibai This is my paper · ORCID
classification cs.ROcs.LGcs.SYeess.SY MSC 68T4093C85
keywords language-conditionedsafetyfilterHamilton-Jacobireachabilitylatentvision-language-actionmodelconstraintgeneralizationrobotmanipulationout-of-distributionvision-languagefailuredetection
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that safety filters for robots can be made general in the same way robot task policies are becoming general: instead of training a separate filter for each safety rule, a single filter reads the rule as natural-language text. Its proposal is a Hamilton-Jacobi reachability critic and actor that take as input a visual observation plus an embedded language constraint, and decide whether the nominal policy's proposed action should be replaced by a safety-preserving one. Evaluated on three simulated manipulation tasks (pick-and-place, table wiping, block stacking), the language-conditioned filter lowers collision and order-violation rates relative to the unfiltered policy, performs comparably to separately trained per-constraint filters, and shows partial generalization to objects, colors, and shapes not seen during filter training. The paper also reports that current vision-language models, when directly asked to judge whether a state violates a language constraint, are not yet reliable enough to serve as the failure signal; in the vision-language experiments the failure function still comes from simulator state. If the central claim holds, safety filtering can move from specification-specific monitors toward pretrained, text-conditioned safety components that are reused and fine-tuned across deployments.

What carries the argument

The load-bearing object is the language-conditioned Hamilton-Jacobi safety critic, a value function over actions, robot observation, and an embedded constraint, trained with the fixed-point Bellman backup approximated by a discounted Q-learning objective. Its zero sublevel set approximates the backward reachable set—states from which violation is unavoidable—so the same critic represents a different failure set whenever the text changes. A learned actor approximates the safety-maximizing action, and at runtime the filter intervenes only when the critic's value for the nominal action is below a margin, choosing the safe candidate closest to the nominal action. The phrase that carries the arch

What would settle it

Run the three tasks with a constraint outside the training families, e.g., 'keep the grasped object above the table' or 'do not tilt the milk box', supplying only camera views and text with no privileged failure function. If a filter trained with a vision-language-model-labeled failure function does not drive violation rates below the nominal policy, or if performance collapses on reworded unseen constraints, the generality claim fails. An immediate check within the paper's setup: on out-of-distribution scenes, repeat the obstacle-versus-benign two-rollout protocol and test whether the filter

Watch

Extended reading notes

Core claim

The central claim is that a single Hamilton-Jacobi safety critic, conditioned on a latent representation that jointly encodes visual observations and a natural-language safety constraint, can enforce multiple constraints, match the performance of one-filter-per-constraint systems, and extend partly to constraint instances unseen during training. The critic estimates the worst-case safety value of a candidate action under the language-specified failure set; when the nominal action's value falls below a margin, the filter substitutes the closest sampled action whose value is safe. In the paper's three manipulation tasks this design reduced collision rates and improved order correctness relativ

Load-bearing premise

The load-bearing premise is that every language constraint has a correct failure function computed from the simulator's privileged state and the known constraint—in the vision-language experiments this function still comes from ground truth, not from the image and text; if a user offers only open-ended language with no such oracle, the paper's evidence for generality does not cover that case.

Editorial extensions

If this is right

  • Deployment-time safety rules can be changed by changing text alone; a single trained filter covers the constraint family instead of requiring retraining per rule.
  • The same critic can represent multiple simultaneously active constraints and sequentially ordered constraints, with results comparable to or better than composing independent per-constraint filters, notably higher order-correctness in block stacking.
  • Pretrained vision-language representations supply enough object semantics for the critic to partly transfer to unseen objects, colors, and shapes within a constraint family.
  • Direct vision-language-model failure judgment is currently the bottleneck: the best model tested catches only about 56% of failures, so practical filters still require a separate source for the failure function.
  • The sampling-based safe-action selection costs about 220 ms per step, over four times the 50 ms control budget; executing the safety actor directly fits the budget and gives comparable safety metrics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: the architecture would be tested at its limit by replacing the privileged failure function with one learned from vision-language-model single-frame labels; if that closed the gap, the filter could be trained from pixels and text alone, which the paper does not yet claim.
  • Inference: the cue-disentangling results (color used when shape name is absent, shape used when color is ambiguous) suggest the model uses object attributes rather than a single shortcut; a follow-up with adversarial attribute correlations could reveal whether this is robust or driven by spurious features.
  • Inference: the same Bellman backup supports richer failure sets than geometric distances; composing critics for temporal-logic or multi-stage specifications is a natural extension that the paper gestures toward but does not demonstrate.
  • Inference: the honest scope of the claim is constraints of the form 'avoid named object' and 'grasp next named block'; extending to open-ended user constraints with no privileged failure function would require solving the vision-language-model reliability problem the paper documents.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a language-conditioned Hamilton-Jacobi safety filter: a single learned safety critic and actor are conditioned on a vision-language embedding of the observation and a natural-language safety constraint, and are used to intervene on a nominal policy. The approach is evaluated in three RoboSuite manipulation environments (Safe Grab, Safe Wipe, Stack Blocks-3/4) under both a privileged ground-truth state representation and a vision-language (VL) representation built on the pi0.5 VLM backbone. The main empirical claims are that the general filter reduces constraint violations relative to the unfiltered nominal policy, that it is competitive with per-constraint filters, that it partially transfers to unseen objects/colors/shapes, and that current VLMs are not yet reliable as failure functions. The paper also contributes a 5,000-trajectory safety-critical manipulation dataset and VLM evaluation protocols.

Significance. If the empirical claims are statistically secure, the paper makes a useful step toward safety filters that can be reused across multiple language-specified constraints, an important direction as VLA policies become more general. The work's strengths include a clearly formulated extension of latent HJ reachability to language-conditioned inputs, a comparison against per-constraint filters, explicit OOD evaluation, a large released dataset, and a careful latency analysis. The discussion of VLM failure-function limitations is honest and appropriately cautious. However, the central comparisons — General vs. Single and OOD transfer — are not yet statistically supported, and the VL experiments rely on a privileged failure function rather than language-derived supervision. These issues need to be addressed before the paper's claims can be considered established.

major comments (4)
  1. [Table 1 and Table 2; Appendix A.9] All reported metrics are from a single training seed (seed 7) and 50 evaluation scenes per setting. With n=50, the binomial standard error is up to 7 percentage points, so differences such as Safe Grab VL CR 46% (General) vs. 56% (Single), Stack Blocks-4 VL OC 64% vs. 50%, and OOD shifts (Safe Wipe CR 38%→56%; Stack Blocks-3 OC 82%→72% in Geom-Color) are within or near one standard error. No confidence intervals, significance tests, or multiple seeds are reported. The large safety gains over the unfiltered nominal policy are robust, but the novel claims of General-vs-Single competitiveness and partial OOD transfer are load-bearing and currently not statistically secured. Please report multiple seeds and confidence intervals, or at least a significance test on the pooled episodes.
  2. [Experimental Setup, VL failure function (Appendix A.1)] In the VL setting, the failure function used to train the critic is h(z_t)=h(s_t,c), computed from the simulator's privileged state s_t and the known constraint c, not from the language/image input. This means the language condition enters only through the VLM representation that encodes the observation and text; the safety supervision itself is not derived from language or vision. The paper therefore does not evaluate the scenario in which the constraint text is the only specification and the failure set must be inferred from language and images. This is a limitation on the claimed generality of 'language-conditioned' safety filtering, and should be stated more prominently and tested, or the claims should be softened.
  3. [Results, 'The General filter respects the specified constraint'] The two-rollout protocol measures contact with the benign object versus the designated obstacle, but these numbers are also from 50 episodes per condition and no error bars. For example, Safe Grab benign-contact 74% vs. obstacle-contact 26% and Safe Wipe 56% vs. 38% are presented as supporting the claim that the filter is not merely avoiding everything, but the statistical separation is not established. Please provide confidence intervals or additional episodes for this protocol.
  4. [Table 3 and VLM evaluation] The VLM evaluation correctly shows that the best model (Gemini Robotics-ER 1.6) detects only 56.4% of failures while having 95.2% non-failure accuracy, and the paper wisely concludes that raw VLM predictions are not yet usable as a runtime failure oracle. This is an important negative result, but it also reinforces the point that the main experiments' safety signal comes from privileged ground-truth h(s_t,c), not from language understanding. The paper should clarify this relationship in the main text rather than only in the appendix.
minor comments (5)
  1. [References] Several references are duplicated (Ames et al. 2017a and 2017b; Bansal et al. 2017a and 2017b). Please consolidate.
  2. [Appendix A.1, Stack Blocks failure function] The failure set is defined as {s_t | h(s_t,c) ≤ 0} in the appendix, while the main text defines the zero-sublevel set as {s | V(s) < 0} and h(s)<0. Please make the boundary convention consistent.
  3. [Table 2] The SR and CR columns for Safe Grab and Safe Wipe OOD come from two separate rollouts per scene (one driving toward the obstacle, one toward the benign object). This is stated in the text but could be clarified in the table caption to avoid misinterpretation.
  4. [Appendix A.9] Hyperparameter tuning is described qualitatively ('selected by evaluating the resulting filters... and inspecting recorded rollout videos'). Please provide the actual values tried and the selection criterion for the final configuration, especially for d_thresh, r, and epsilon, since these directly affect the safety behavior.
  5. [Appendix A.7] The mean-pooling vs. attention-pooling ablation shows mixed results, and the attention variant increases memory to about 740 GB per replay buffer. This is useful context, but the main text should reference this trade-off when discussing the VL performance gap.

Circularity Check

0 steps flagged · score 2.0 of 10

No circularity: central claims rest on held-out evaluation; only a minor, non-load-bearing self-citation.

full rationale

After walking the derivation chain, I find no circular step. The central object is the HJ critic trained with the fixed-point Bellman loss (Eq. 1) using targets built from the privileged failure function h; nothing in Eq. 1 or the runtime filter (Eq. 3 and the sampling procedure in A.4) is defined in terms of the reported CR/SR/OC metrics, so the results are not true by construction. The VL experiments set h(z_t)=h(s_t,c) because current VLMs are too unreliable as failure oracles; that is an external-supervision assumption and a stated limitation, not a circular reduction. The critic still has to learn a mapping from VLM embeddings to the safety value, and on OOD scenes it is evaluated on objects, colors, and shapes that were neither used to set constants nor seen during filter training, so Table 2 is a genuine transfer measurement. The single-seed, 50-scene evaluation makes some reported differences statistically fragile, but that is a sampling limitation, not circularity. The one self-citation (Tabbara et al. 2025) appears in related work and as a pointer to previous latent-filter/classifier ideas; it is not load-bearing, and no uniqueness theorem or ansatz is imported from it to force the present contribution. Hyperparameter selection by inspecting success/collision rates is model selection, not a fitted parameter being renamed as a prediction. Score 2 reflects only the minor, non-load-bearing self-citation; the central derivation is self-contained against its own benchmarks.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard HJ reachability formalism, a privileged failure-function oracle even in the VL setting, and the pretraining coverage of the frozen VLM. No new physical entities are postulated. The main free parameters are the failure margins, the intervention threshold, and the hyperparameters selected by rollout inspection.

free parameters (5)
  • Failure margin d_thresh (Safe Grab, Safe Wipe) = 0.02
    Minimum allowed distance to the obstacle; chosen by hand and defines the failure set that the critic is trained and evaluated against (Appendix A.1, Table 7).
  • Stack Blocks slackness margin r = 0.0495 (1.3 × block half-width)
    Defines h(s_t,c)=r-d_cube; hand-set slack for the 'grasp the target block' constraint (Table 7).
  • Intervention threshold epsilon = not reported
    Runtime margin in the filter's switching rule Q(z_t,a_nom) >= epsilon; value not given in text, yet it directly controls intervention rate and the safety/task trade-off (Methodology, Runtime HJ reachability-based safety filtering).
  • Discount schedule and SAC hyperparameters = gamma 0.5→0.99, cost scale 0.1, etc.
    Selected by evaluating success/collision rates and inspecting rollout videos (Appendix A.9); these choices affect the learned Q and therefore the filter's decisions.
  • Number of candidate actions sampled = 100 actor + 100 noise-perturbed nominal actions
    Design choice in the minimum-deviation safe-action selection (Appendix A.4); affects task performance and safety.
assumptions (5)
  • standard math The Bellman fixed-point equation V(s)=min{h(s), max_a V(f(s,a))} characterizes the optimal safety value function for the HJ reachability problem.
    Used as the foundation for the learned HJ critic (Preliminaries, Bellman equation).
  • domain assumption The environment is a known, fully observable Markov decision process in simulation, with a fixed task-achieving nominal policy.
    All experiments run in ROBOSUITE with scripted nominal controllers; the filter is evaluated in this closed-world setting (Experimental Setup; Appendix A.2).
  • domain assumption A correct constraint-conditioned failure function h(s_t,c) is available from simulator state and the known constraint for training the critic in both GT and VL settings.
    In the VL setting the paper explicitly sets h(z_t)=h(s_t,c) because VLMs are not reliable; without this privileged h the language-conditioned training signal would not exist (Experimental Setup, Tasks).
  • domain assumption The frozen VLM encoder pi0.5 produces embeddings z_t that preserve enough visual-language-proprioceptive structure for the critic to regress the privileged h; OOD objects and colors are assumed to fall inside the VLM's pretraining distribution.
    The paper states that OOD scenes 'likely fall within the pretraining distribution of the VLM backbone,' which is the mechanism proposed for transfer (Results, The General filter transfers to unseen constraints).
  • domain assumption In Stack Blocks, stage completion is observable to the environment ('We assume stage completion is observable').
    The filter switches constraints per stage; this oracle is assumed rather than learned (Appendix A.1).

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Cite this review

Pith. "Pith review of Towards General Language-Conditioned Latent Safety Filters." pith.science (2026). https://pith.science/paper/WREBLMOW

@misc{pith2026260800315,
  author       = {Pith},
  title        = {Pith review of: Towards General Language-Conditioned Latent Safety Filters},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WREBLMOW}},
  note         = {Machine review of arXiv:2608.00315}
}
read the original abstract

Robot policies are becoming increasingly general, with vision-language-action (VLA) models enabling a single policy to execute diverse tasks specified in natural language. Safe deployment, however, requires adapting not only to new tasks but also to varying safety requirements across users, environments, and applications. Existing safety filters remain largely constraint-specific and thus must be redesigned or relearned when safety requirements change. In this paper, we investigate language-conditioned safety filtering, in which a Hamilton-Jacobi safety actor and critic are conditioned on language-specified constraints. We evaluate this formulation across pick-and-place, table-wiping, and block-stacking tasks in the vision-based setting, examining its ability to enforce language-specified constraints and transfer to unseen constraint instances within the evaluated constraint families. Our experiments provide evidence that language-conditioned safety filters reduce constraint violations and exhibit partial transfer to unseen constraint instances.

Figures

Figures reproduced from arXiv: 2608.00315 by the authors.

Figure 1
Figure 1. Overview of the three benchmark environments. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Visual examples of the out-of-distribution settings. (a) and (b) show the object-substitution settings for Safe Grab and [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. A failure episode in Safe Grab. The manipulator and the designated obstacle overlap in the agent view, so contact [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: A failure episode in Safe Wipe. The arm jitters in front of the designated obstacles and makes no progress on the [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: A failure episode in Stack Blocks-3. The gripper is driven into a pose from which the target block cannot be grasped [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: A failure episode in Stack Blocks-4. With three blocks already placed, the arm collides with the existing stack while [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: Examples from the safety-critical manipulation trajectory dataset. [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Sample vision input and output response of single [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Sample vision input and output response of pair [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]
Figure 10
Figure 10. Figure 10: Sample vision input and output response of short [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: A nominal VLA (π0.5) may fail to modify its behavior when an explicit safety constraint is appended to the task prompt. In both examples, the policy disregards the constraint and acts on the visual observation alone [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]

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Reference graph

Works this paper leans on

257 extracted references · 4 canonical work pages

  1. [1]

    Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

    Clancey, William J. Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83)

  2. [2]

    Classification Problem Solving

    Clancey, William J. Classification Problem Solving. Proceedings of the Fourth National Conference on Artificial Intelligence

  3. [3]

    , title =

    Robinson, Arthur L. , title =. 1980 , doi =. https://science.sciencemag.org/content/208/4447/1019.full.pdf , journal =

    1980

  4. [4]

    New Ways to Make Microcircuits Smaller---Duplicate Entry

    Robinson, Arthur L. New Ways to Make Microcircuits Smaller---Duplicate Entry. Science

  5. [5]

    Clancey and Glenn Rennels , abstract =

    Diane Warner Hasling and William J. Clancey and Glenn Rennels , abstract =. Strategic explanations for a diagnostic consultation system , journal =. 1984 , issn =. doi:https://doi.org/10.1016/S0020-7373(84)80003-6 , url =

    doi:10.1016/s0020-7373(84)80003-6 1984

  6. [6]

    and Rennels, Glenn R

    Hasling, Diane Warner and Clancey, William J. and Rennels, Glenn R. and Test, Thomas. Strategic Explanations in Consultation---Duplicate. The International Journal of Man-Machine Studies

  7. [7]

    Poligon: A System for Parallel Problem Solving

    Rice, James. Poligon: A System for Parallel Problem Solving

  8. [8]

    Transfer of Rule-Based Expertise through a Tutorial Dialogue

    Clancey, William J. Transfer of Rule-Based Expertise through a Tutorial Dialogue

Show all 257 references
  1. [9]

    The Engineering of Qualitative Models

    Clancey, William J. The Engineering of Qualitative Models

  2. [10]

    2023 , eprint=

    Attention Is All You Need , author=. 2023 , eprint=

    2023

  3. [11]

    Pluto: The 'Other' Red Planet

    NASA. Pluto: The 'Other' Red Planet

  4. [12]

    Proceedings of The 7th Conference on Robot Learning , pages =

    RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control , author =. Proceedings of The 7th Conference on Robot Learning , pages =. 2023 , editor =

    2023

  5. [13]

    arXiv preprint arXiv:2406.09246 , year=

    OpenVLA: An Open-Source Vision-Language-Action Model , author=. arXiv preprint arXiv:2406.09246 , year=

    arXiv

  6. [14]

    Proceedings of Robotics: Science and Systems , address =

    Octo: An Open-Source Generalist Robot Policy , author =. Proceedings of Robotics: Science and Systems , address =

  7. [15]

    arXiv preprint arXiv:2502.19645 , year=

    Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success , author=. arXiv preprint arXiv:2502.19645 , year=

    arXiv

  8. [16]

    2024 , eprint=

    _0 : A Vision-Language-Action Flow Model for General Robot Control , author=. 2024 , eprint=

    2024

  9. [17]

    Black, Kevin and Brown, Noah and Darpinian, James and Dhabalia, Karan and Driess, Danny and Esmail, Adnan and Equi, Michael Robert and Finn, Chelsea and Fusai, Niccolo and Galliker, Manuel Y. and Ghosh, Dibya and Groom, Lachy and Hausman, Karol and ichter, brian and Jakubczak,...

    2025

  10. [18]

    2025 , eprint=

    FAST: Efficient Action Tokenization for Vision-Language-Action Models , author=. 2025 , eprint=

    2025

  11. [19]

    2025 , eprint=

    MolmoAct: Action Reasoning Models that can Reason in Space , author=. 2025 , eprint=

    2025

  12. [20]

    2026 , eprint=

    Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Control , author=. 2026 , eprint=

    2026

  13. [21]

    2026 , eprint=

    AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots , author=. 2026 , eprint=

    2026

  14. [22]

    2022 , eprint=

    Do As I Can, Not As I Say: Grounding Language in Robotic Affordances , author=. 2022 , eprint=

    2022

  15. [23]

    2025 , eprint=

    CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models , author=. 2025 , eprint=

    2025

  16. [24]

    NeurIPS 2023 Datasets and Benchmarks , year=

    LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning , author=. NeurIPS 2023 Datasets and Benchmarks , year=

    2023

  17. [25]

    2025 , eprint=

    SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Safe Reinforcement Learning , author=. 2025 , eprint=

    2025

  18. [26]

    2026 , eprint=

    HazardArena: Evaluating Semantic Safety in Vision--Language--Action Models , author=. 2026 , eprint=

    2026

  19. [27]

    Proceedings of The 9th Conference on Robot Learning , pages =

    RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models , author =. Proceedings of The 9th Conference on Robot Learning , pages =. 2025 , editor =

    2025

  20. [28]

    2026 , eprint=

    Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment , author=. 2026 , eprint=

    2026

  21. [29]

    2026 , eprint=

    When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs , author=. 2026 , eprint=

    2026

  22. [30]

    2019 International Conference on Robotics and Automation (ICRA) , pages=

    Bridging hamilton-jacobi safety analysis and reinforcement learning , author=. 2019 International Conference on Robotics and Automation (ICRA) , pages=. 2019 , organization=

    2019

  23. [31]

    Advances in neural information processing systems , volume=

    Scaling proprioceptive-visual learning with heterogeneous pre-trained transformers , author=. Advances in neural information processing systems , volume=

  24. [32]

    Conference on robot learning , pages=

    Daydreamer: World models for physical robot learning , author=. Conference on robot learning , pages=. 2023 , organization=

    2023

  25. [33]

    2024 , eprint=

    Mastering Diverse Domains through World Models , author=. 2024 , eprint=

    2024

  26. [34]

    arXiv preprint arXiv:2411.04983 , year=

    Dino-wm: World models on pre-trained visual features enable zero-shot planning , author=. arXiv preprint arXiv:2411.04983 , year=

    arXiv

  27. [35]

    Advances in Neural Information Processing Systems , volume=

    Where are we in the search for an artificial visual cortex for embodied intelligence? , author=. Advances in Neural Information Processing Systems , volume=

  28. [36]

    2024 , eprint=

    Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions , author=. 2024 , eprint=

    2024

  29. [37]

    2023 , eprint=

    Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts , author=. 2023 , eprint=

    2023

  30. [38]

    Trust-based Rate-Tunable Control Barrier Functions for Non-Cooperative Multi-Agent Systems , year=

    Parwana, Hardik and Mustafa, Aquib and Panagou, Dimitra , booktitle=. Trust-based Rate-Tunable Control Barrier Functions for Non-Cooperative Multi-Agent Systems , year=

  31. [39]

    2022 , eprint=

    Adaptive Conformal Prediction for Motion Planning among Dynamic Agents , author=. 2022 , eprint=

    2022

  32. [40]

    2022 , eprint=

    Targeted Adversarial Attacks against Neural Network Trajectory Predictors , author=. 2022 , eprint=

    2022

  33. [41]

    2022 , eprint=

    Adaptation for Validation of a Consolidated Control Barrier Function based Control Synthesis , author=. 2022 , eprint=

    2022

  34. [42]

    2021 , eprint=

    Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates , author=. 2021 , eprint=

    2021

  35. [43]

    Wu, Haoze and Barrett, Clark and Sharif, Mahmood and Narodytska, Nina and Singh, Gagandeep , title =. Proc. ACM Program. Lang. , month =. 2022 , issue_date =. doi:10.1145/3563325 , abstract =

    2022 doi

  36. [44]

    arXiv preprint arXiv:2511.07899 , year=

    Statistically Assuring Safety of Control Systems using Ensembles of Safety Filters and Conformal Prediction , author=. arXiv preprint arXiv:2511.07899 , year=

  37. [45]

    arXiv preprint arXiv:2505.00908 , year=

    Learning Conservative Neural Control Barrier Functions from Offline Data , author=. arXiv preprint arXiv:2505.00908 , year=

  38. [46]

    Proceedings of the AAAI Conference on Artificial Intelligence , author=

    Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks , volume=. Proceedings of the AAAI Conference on Artificial Intelligence , author=. 2021 , month=. doi:10.1609/aaai.v35i13.17421 , abstractNote=

    2021 doi

  39. [47]

    Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems , pages =

    Li, Qingbiao and Gama, Fernando and Ribeiro, Alejandro and Prorok, Amanda , title =. Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems , pages =. 2020 , isbn =

    2020

  40. [48]

    6th Annual Conference on Robot Learning , year=

    Learning Multi-Object Dynamics with Compositional Neural Radiance Fields , author=. 6th Annual Conference on Robot Learning , year=

  41. [49]

    Graph Neural Networks: Adversarial Robustness

    G \"u nnemann, Stephan. Graph Neural Networks: Adversarial Robustness. Graph Neural Networks: Foundations, Frontiers, and Applications. 2022. doi:10.1007/978-981-16-6054-2_8

    2022 doi

  42. [50]

    International Conference on Learning Representations , year=

    Fundamental Limits in Formal Verification of Message-Passing Neural Networks , author=. International Conference on Learning Representations , year=

  43. [51]

    Advances in Neural Information Processing Systems , editor=

    Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks , author=. Advances in Neural Information Processing Systems , editor=. 2022 , url=

    2022

  44. [52]

    Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning , year=

    Li, Qingbiao and Lin, Weizhe and Liu, Zhe and Prorok, Amanda , journal=. Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning , year=

  45. [53]

    Learning-based Motion Planning in Dynamic Environments Using

    Ruipeng Zhang and Chenning Yu and Jingkai Chen and Chuchu Fan and Sicun Gao , booktitle=. Learning-based Motion Planning in Dynamic Environments Using. 2022 , url=

    2022

  46. [54]

    Are socially-aware trajectory prediction models really socially-aware? , journal =

    Saeed Saadatnejad and Mohammadhossein Bahari and Pedram Khorsandi and Mohammad Saneian and Seyed-Mohsen Moosavi-Dezfooli and Alexandre Alahi , keywords =. Are socially-aware trajectory prediction models really socially-aware? , journal =. 2022 , issn =. doi:https://doi.org/10....

    2022

  47. [55]

    arXiv preprint arXiv:2206.03669 , year=

    Toward certified robustness against real-world distribution shifts , author=. arXiv preprint arXiv:2206.03669 , year=

    arXiv

  48. [56]

    , booktitle=

    Rober, Nicholas and Everett, Michael and How, Jonathan P. , booktitle=. Backward Reachability Analysis for Neural Feedback Loops , year=

  49. [57]

    and Xu, Xiangru and Grizzle, Jessy W

    Ames, Aaron D. and Xu, Xiangru and Grizzle, Jessy W. and Tabuada, Paulo , journal=. Control Barrier Function Based Quadratic Programs for Safety Critical Systems , year=

  50. [58]

    2018 Annual American Control Conference (ACC) , year=

    Permissive Barrier Certificates for Safe Stabilization Using Sum-of-squares , author=. 2018 Annual American Control Conference (ACC) , year=

    2018

  51. [59]

    5th Annual Conference on Robot Learning , year=

    Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions , author=. 5th Annual Conference on Robot Learning , year=

  52. [60]

    IEEE Control Systems Letters , year=

    Robust Control Barrier Functions With Sector-Bounded Uncertainties , author=. IEEE Control Systems Letters , year=

  53. [61]

    2017 IEEE International Conference on Robotics and Automation (ICRA) , year=

    Safe certificate-based maneuvers for teams of quadrotors using differential flatness , author=. 2017 IEEE International Conference on Robotics and Automation (ICRA) , year=

    2017

  54. [62]

    , journal=

    Prajna, Stephen and Jadbabaie, Ali and Pappas, George J. , journal=. A Framework for Worst-Case and Stochastic Safety Verification Using Barrier Certificates , year=

  55. [63]

    2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , year=

    Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance , author=. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , year=

    2021

  56. [64]

    BarrierNet: Differentiable Control Barrier Functions for Learning of Safe Robot Control , year=

    Xiao, Wei and Wang, Tsun-Hsuan and Hasani, Ramin and Chahine, Makram and Amini, Alexander and Li, Xiao and Rus, Daniela , journal=. BarrierNet: Differentiable Control Barrier Functions for Learning of Safe Robot Control , year=

  57. [65]

    and Ames, Aaron D

    Mehra, Aakar and Ma, Wen-Loong and Berg, Forrest and Tabuada, Paulo and Grizzle, Jessy W. and Ames, Aaron D. , booktitle=. Adaptive cruise control: Experimental validation of advanced controllers on scale-model cars , year=

  58. [66]

    2023 , eprint=

    Verification and Synthesis of Robust Control Barrier Functions: Multilevel Polynomial Optimization and Semidefinite Relaxation , author=. 2023 , eprint=

    2023

  59. [67]

    A Distributed Simplex Architecture for Multi-Agent Systems , author=. J. Syst. Archit. , year=

  60. [68]

    ArXiv , year=

    Control Barrier Function Contracts for Vehicular Mission Planning Under Signal Temporal Logic Specifications , author=. ArXiv , year=

  61. [69]

    2018 IEEE Conference on Decision and Control (CDC) , year=

    Control of Multi-Agent Systems with Finite Time Control Barrier Certificates and Temporal Logic , author=. 2018 IEEE Conference on Decision and Control (CDC) , year=

    2018

  62. [70]

    , journal=

    Chen, Yuxiao and Singletary, Andrew and Ames, Aaron D. , journal=. Guaranteed Obstacle Avoidance for Multi-Robot Operations With Limited Actuation: A Control Barrier Function Approach , year=

  63. [71]

    , journal=

    Tan, Xiao and Dimarogonas, Dimos V. , journal=. Distributed Implementation of Control Barrier Functions for Multi-agent Systems , year=

  64. [72]

    International Conference on Learning Representations , year=

    Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates , author=. International Conference on Learning Representations , year=

  65. [73]

    Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control , articleno =

    Jagtap, Pushpak and Swikir, Abdalla and Zamani, Majid , title =. Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control , articleno =. 2020 , isbn =. doi:10.1145/3365365.3382196 , abstract =

    2020

  66. [74]

    , journal=

    Lindemann, Lars and Dimarogonas, Dimos V. , journal=. Control Barrier Functions for Multi-Agent Systems Under Conflicting Local Signal Temporal Logic Tasks , year=

  67. [75]

    ArXiv , year=

    Learning Environment-Aware Control Barrier Functions for Safe and Feasible Multi-Robot Navigation , author=. ArXiv , year=

  68. [76]

    2023 , eprint=

    Efficient LQR-CBF-RRT*: Safe and Optimal Motion Planning , author=. 2023 , eprint=

    2023

  69. [77]

    Implicit Graph Neural Networks , volume =

    Gu, Fangda and Chang, Heng and Zhu, Wenwu and Sojoudi, Somayeh and El Ghaoui, Laurent , booktitle =. Implicit Graph Neural Networks , volume =

  70. [78]

    IEEE Robotics and Automation Letters , volume=

    Learning safe, generalizable perception-based hybrid control with certificates , author=. IEEE Robotics and Automation Letters , volume=. 2022 , publisher=

    2022

  71. [79]

    Enforcing safety for vision-based controllers via Control Barrier Functions and Neural Radiance Fields , year=

    Tong, Mukun and Dawson, Charles and Fan, Chuchu , booktitle=. Enforcing safety for vision-based controllers via Control Barrier Functions and Neural Radiance Fields , year=

  72. [80]

    2022 , eprint=

    Control-oriented meta-learning , author=. 2022 , eprint=

    2022

  73. [81]

    Learning for Dynamics and Control Conference , pages=

    Learning Stability Attention in Vision-based End-to-end Driving Policies , author=. Learning for Dynamics and Control Conference , pages=. 2023 , organization=

    2023

  74. [82]

    2023 , eprint=

    Contrastive Learning as Goal-Conditioned Reinforcement Learning , author=. 2023 , eprint=

    2023

  75. [83]

    CoRR , volume =

    Chelsea Finn and Xin Yu Tan and Yan Duan and Trevor Darrell and Sergey Levine and Pieter Abbeel , title =. CoRR , volume =. 2015 , url =. 1509.06113 , timestamp =

    2015 arXiv

  76. [84]

    2019 , eprint=

    Neural Predictive Belief Representations , author=. 2019 , eprint=

    2019

  77. [85]

    International Conference on Learning Representations , year=

    Near-Optimal Representation Learning for Hierarchical Reinforcement Learning , author=. International Conference on Learning Representations , year=

  78. [86]

    2018 , eprint=

    Visual Reinforcement Learning with Imagined Goals , author=. 2018 , eprint=

    2018

  79. [87]

    Advances in Neural Information Processing Systems , volume=

    Which Mutual-Information Representation Learning Objectives are Sufficient for Control? , author=. Advances in Neural Information Processing Systems , volume=

  80. [88]

    2023 , eprint=

    Value Functions are Control Barrier Functions: Verification of Safe Policies using Control Theory , author=. 2023 , eprint=

    2023

  81. [89]

    5th Annual Learning for Dynamics

    In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States , author=. 5th Annual Learning for Dynamics. 2023 , url=

    2023

  82. [90]

    2022 , eprint=

    SABLAS: Learning Safe Control for Black-box Dynamical Systems , author=. 2022 , eprint=

    2022

  83. [91]

    Ensemble deep learning: A review , volume =

    M A Ganaie and Minghui Hu and A K Malik and M Tanveer and P N Suganthan , doi =. Ensemble deep learning: A review , volume =. Engineering Applications of Artificial Intelligence , keywords =

  84. [92]

    Robust Guarantees for Perception-Based Control , volume =

    Sarah Dean and Nikolai Matni and Benjamin Recht and Vickie Ye , editor =. Robust Guarantees for Perception-Based Control , volume =. Proceedings of the 2nd Conference on Learning for Dynamics and Control , month =

  85. [93]

    International Conference on Learning Representations 2023 Workshop on Scene Representations for Autonomous Driving , year=

    SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning , author=. International Conference on Learning Representations 2023 Workshop on Scene Representations for Autonomous Driving , year=

    2023

  86. [94]

    Tan, Daniel and Acero, Fernando and McCarthy, Robert and Kanoulas, Dimitrios and Li, Zhibin , year =

  87. [95]

    2022 , eprint=

    COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation , author=. 2022 , eprint=

    2022

  88. [96]

    2023 , eprint=

    Constrained Decision Transformer for Offline Safe Reinforcement Learning , author=. 2023 , eprint=

    2023

  89. [97]

    Model-Free Safe Reinforcement Learning Through Neural Barrier Certificate , year=

    Yang, Yujie and Jiang, Yuxuan and Liu, Yichen and Chen, Jianyu and Li, Shengbo Eben , journal=. Model-Free Safe Reinforcement Learning Through Neural Barrier Certificate , year=

  90. [98]

    2024 , eprint=

    Safety Filters for Black-Box Dynamical Systems by Learning Discriminating Hyperplanes , author=. 2024 , eprint=

    2024

  91. [99]

    Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? , url =

    Majumdar, Arjun and Yadav, Karmesh and Arnaud, Sergio and Ma, Jason and Chen, Claire and Silwal, Sneha and Jain, Aryan and Berges, Vincent-Pierre and Wu, Tingfan and Vakil, Jay and Abbeel, Pieter and Malik, Jitendra and Batra, Dhruv and Lin, Yixin and Maksymets, Oleksandr and ...

  92. [100]

    Zhao and Ken Goldberg and Ryan Hoque and Lawrence Yunliang Chen and Simeon Adebola and Gaurav S

    Quan Vuong and Sergey Levine and Homer Rich Walke and Karl Pertsch and Anikait Singh and Ria Doshi and Charles Xu and Jianlan Luo and Liam Tan and Dhruv Shah and Chelsea Finn and Max Du and Moo Jin Kim and Alexander Khazatsky and Jonathan Heewon Yang and Tony Z. Zhao and Ken G...

  93. [101]

    2024 , eprint=

    Octo: An Open-Source Generalist Robot Policy , author=. 2024 , eprint=

    2024

  94. [102]

    6th Annual Conference on Robot Learning , year=

    R3M: A Universal Visual Representation for Robot Manipulation , author=. 6th Annual Conference on Robot Learning , year=

  95. [103]

    International Conference on Robotics and Automation (ICRA) , year =

    Dhruv Shah and Ajay Sridhar and Arjun Bhorkar and Noriaki Hirose and Sergey Levine , title =. International Conference on Robotics and Automation (ICRA) , year =

  96. [104]

    Dhruv Shah and Ajay Sridhar and Nitish Dashora and Kyle Stachowicz and Kevin Black and Noriaki Hirose and Sergey Levine , booktitle =. Vi. 2023 , url =

    2023

  97. [105]

    arXiv pre-print , year =

    Ajay Sridhar and Dhruv Shah and Catherine Glossop and Sergey Levine , title =. arXiv pre-print , year =

  98. [106]

    2023 , eprint =

    Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? , author =. 2023 , eprint =

    2023

  99. [107]

    2022 , eprint=

    Real-World Robot Learning with Masked Visual Pre-training , author=. 2022 , eprint=

    2022

  100. [108]

    International conference on machine learning , pages=

    Learning transferable visual models from natural language supervision , author=. International conference on machine learning , pages=. 2021 , organization=

    2021

  101. [109]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    You only look once: Unified, real-time object detection , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  102. [110]

    Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , url =

    Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian , booktitle =. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , url =

  103. [111]

    2023 , eprint=

    DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving , author=. 2023 , eprint=

    2023

  104. [112]

    Learning Control Barrier Functions from Expert Demonstrations , doi =

    Robey, Alexander and Hu, Haimin and Lindemann, Lars and Zhang, Hanwen and Dimarogonas, Dimos and Tu, Stephen and Matni, Nikolai , year =. Learning Control Barrier Functions from Expert Demonstrations , doi =

  105. [113]

    Alexey Dosovitskiy and German Ros and Felipe Codevilla and Antonio Lopez and Vladlen Koltun , booktitle =

  106. [114]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , month =

    Lindstr\"om, Carl and Hess, Georg and Lilja, Adam and Fatemi, Maryam and Hammarstrand, Lars and Petersson, Christoffer and Svensson, Lennart , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , month =. 2024 , pages =

    2024

  107. [115]

    , journal=

    Lechner, Mathias and Amini, Alexander and Rus, Daniela and Henzinger, Thomas A. , journal=. Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning , year=

  108. [116]

    Machine Learning , author =

    Bagging predictors , volume =. Machine Learning , author =. 1996 , pages =. doi:10.1007/BF00058655 , abstract =

    1996 doi

  109. [117]

    Schapire , title =

    Peter Bartlett and Yoav Freund and Wee Sun Lee and Robert E. Schapire , title =. The Annals of Statistics , number =. 1998 , doi =

    1998

  110. [118]

    Machine Learning , author =

    Random. Machine Learning , author =. 2001 , pages =. doi:10.1023/A:1010933404324 , abstract =

    2001 doi

  111. [119]

    International Conference on Machine Learning , pages=

    Adversarial vulnerability of randomized ensembles , author=. International Conference on Machine Learning , pages=. 2022 , organization=

    2022

  112. [120]

    Advances in neural information processing systems , volume=

    Simple and scalable predictive uncertainty estimation using deep ensembles , author=. Advances in neural information processing systems , volume=

  113. [121]

    Learning for Dynamics and Control Conference , pages=

    Learning-enhanced nonlinear model predictive control using knowledge-based neural ordinary differential equations and deep ensembles , author=. Learning for Dynamics and Control Conference , pages=. 2023 , organization=

    2023

  114. [122]

    and Vovk, V

    Gammerman, A. and Vovk, V. and Vapnik, V. , title =. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence , pages =. 1998 , isbn =

    1998

  115. [123]

    Conformal and Probabilistic Prediction and Applications , pages=

    Conformal predictive decision making , author=. Conformal and Probabilistic Prediction and Applications , pages=. 2018 , organization=

    2018

  116. [124]

    Advances in Neural Information Processing Systems , editor=

    Adaptive Conformal Inference Under Distribution Shift , author=. Advances in Neural Information Processing Systems , editor=. 2021 , url=

    2021

  117. [125]

    Chen, Ricky T. Q. and Rubanova, Yulia and Bettencourt, Jesse and Duvenaud, David , title =. Proceedings of the 32nd International Conference on Neural Information Processing Systems , pages =. 2018 , publisher =

    2018

  118. [126]

    2024 , howpublished =

    Tesla , title =. 2024 , howpublished =

    2024

  119. [127]

    2020 , howpublished =

    Waymo , title =. 2020 , howpublished =

    2020

  120. [128]

    arXiv preprint arXiv:2405.18554 , year=

    Scalable Surrogate Verification of Image-based Neural Network Control Systems using Composition and Unrolling , author=. arXiv preprint arXiv:2405.18554 , year=

    arXiv

  121. [129]

    Verifying Controllers With Vision-Based Perception Using Safe Approximate Abstractions , year=

    Hsieh, Chiao and Li, Yangge and Sun, Dawei and Joshi, Keyur and Misailovic, Sasa and Mitra, Sayan , journal=. Verifying Controllers With Vision-Based Perception Using Safe Approximate Abstractions , year=

  122. [130]

    NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing

    Santa Cruz, Ulices and Shoukry, Yasser. NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing. NASA Formal Methods. 2022

    2022

  123. [131]

    Closed-Loop Analysis of Vision-Based Autonomous Systems: A Case Study , year =

    P. Closed-Loop Analysis of Vision-Based Autonomous Systems: A Case Study , year =. Computer Aided Verification: 35th International Conference, CAV 2023, Paris, France, July 17–22, 2023, Proceedings, Part I , pages =

    2023

  124. [132]

    Journal of Aerospace Information Systems , volume=

    Verification of image-based neural network controllers using generative models , author=. Journal of Aerospace Information Systems , volume=. 2022 , publisher=

    2022

  125. [133]

    2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) , pages=

    Safe reinforcement learning for autonomous vehicles through parallel constrained policy optimization , author=. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) , pages=. 2020 , organization=

    2020

  126. [134]

    International conference on machine learning , pages=

    Constrained policy optimization , author=. International conference on machine learning , pages=. 2017 , organization=

    2017

  127. [135]

    CoRR , volume =

    John Schulman and Filip Wolski and Prafulla Dhariwal and Alec Radford and Oleg Klimov , title =. CoRR , volume =. 2017 , url =. 1707.06347 , timestamp =

    2017 arXiv

  128. [136]

    and Chakraborty, Supratik and Seshia, Sanjit A

    Torfah, Hazem and Joshi, Aniruddha and Shah, Shetal and Akshay, S. and Chakraborty, Supratik and Seshia, Sanjit A. Learning Monitor Ensembles for Operational Design Domains. Runtime Verification. 2023

    2023

  129. [137]

    Stochastic Model Predictive Control With a Safety Guarantee for Automated Driving , year=

    Brüdigam, Tim and Olbrich, Michael and Wollherr, Dirk and Leibold, Marion , journal=. Stochastic Model Predictive Control With a Safety Guarantee for Automated Driving , year=

  130. [138]

    arXiv preprint arXiv:2407.08735 , year=

    Real-Time Anomaly Detection and Reactive Planning with Large Language Models , author=. arXiv preprint arXiv:2407.08735 , year=

    arXiv

  131. [139]

    and He, Chaozhe R

    Alan, Anil and Taylor, Andrew J. and He, Chaozhe R. and Ames, Aaron D. and Orosz, Gábor , journal=. Control Barrier Functions and Input-to-State Safety With Application to Automated Vehicles , year=

  132. [140]

    A Comprehensive Survey on Safe Reinforcement Learning , journal =

    Javier Garc. A Comprehensive Survey on Safe Reinforcement Learning , journal =. 2015 , volume =

    2015

  133. [141]

    and Coogan, Samuel and Egerstedt, Magnus and Notomista, Gennaro and Sreenath, Koushil and Tabuada, Paulo , booktitle=

    Ames, Aaron D. and Coogan, Samuel and Egerstedt, Magnus and Notomista, Gennaro and Sreenath, Koushil and Tabuada, Paulo , booktitle=. Control Barrier Functions: Theory and Applications , year=

  134. [142]

    Verification and Synthesis of Control Barrier Functions , year=

    Clark, Andrew , booktitle=. Verification and Synthesis of Control Barrier Functions , year=

  135. [143]

    Refining Control Barrier Functions through Hamilton-Jacobi Reachability , year=

    Tonkens, Sander and Herbert, Sylvia , booktitle=. Refining Control Barrier Functions through Hamilton-Jacobi Reachability , year=

  136. [144]

    and Lee, Donggun and Sreenath, Koushil and Tomlin, Claire J

    Choi, Jason J. and Lee, Donggun and Sreenath, Koushil and Tomlin, Claire J. and Herbert, Sylvia L. , title =. 2021 60th IEEE Conference on Decision and Control (CDC) , pages =. 2021 , publisher =. doi:10.1109/CDC45484.2021.9683085 , abstract =

    2021

  137. [145]

    IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

    End-to-end autonomous driving: Challenges and frontiers , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

  138. [146]

    Applied Sciences , VOLUME =

    Pavel, Monirul Islam and Tan, Siok Yee and Abdullah, Azizi , TITLE =. Applied Sciences , VOLUME =. 2022 , NUMBER =

    2022

  139. [147]

    Proceedings of the 2020 Conference on Robot Learning , pages =

    Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions , author =. Proceedings of the 2020 Conference on Robot Learning , pages =. 2021 , editor =

    2020

  140. [148]

    GRIP: Graph-based Interaction-aware Trajectory Prediction , year =

    Li, Xin and Ying, Xiaowen and Chuah, Mooi Choo , booktitle =. GRIP: Graph-based Interaction-aware Trajectory Prediction , year =

  141. [149]

    arXiv preprint arXiv:1907.07792 , year=

    GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving , author=. arXiv preprint arXiv:1907.07792 , year=

    1907 arXiv

  142. [150]

    arXiv preprint arXiv:1903.11027 , year=

    nuScenes: A multimodal dataset for autonomous driving , author=. arXiv preprint arXiv:1903.11027 , year=

    1903 arXiv

  143. [151]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Trafficpredict: Trajectory prediction for heterogeneous traffic-agents , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  144. [152]

    Online Control Barrier Functions for Decentralized Multi-Agent Navigation , year=

    Gao, Zhan and Yang, Guang and Prorok, Amanda , booktitle=. Online Control Barrier Functions for Decentralized Multi-Agent Navigation , year=

  145. [153]

    Grizzle and Aaron D

    Xiangru Xu and Paulo Tabuada and Jessy W. Grizzle and Aaron D. Ames , keywords =. Robustness of Control Barrier Functions for Safety Critical Control , journal =. 2015 , note =. doi:https://doi.org/10.1016/j.ifacol.2015.11.152 , url =

    2015 doi

  146. [154]

    1942 , publisher=

    Nagumo, Mitio , journal=. 1942 , publisher=

    1942

  147. [155]

    Freeman and Petar V

    Randy A. Freeman and Petar V. Kototovic , year=. , title=

  148. [156]

    Xu and Y

    C. Xu and Y. Xie , journal =. Conformal Prediction for Time Series , year =. doi:10.1109/TPAMI.2023.3272339 , publisher =

    2023

  149. [157]

    Conformal PID Control for Time Series Prediction , url =

    Angelopoulos, Anastasios and Candes, Emmanuel and Tibshirani, Ryan J , booktitle =. Conformal PID Control for Time Series Prediction , url =

  150. [158]

    2005 , publisher=

    Algorithmic learning in a random world , author=. 2005 , publisher=

    2005

  151. [159]

    Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control , journal =

    Milan Korda and Igor Mezić , keywords =. Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control , journal =. 2018 , issn =. doi:https://doi.org/10.1016/j.automatica.2018.03.046 , url =

    2018 doi

  152. [160]

    2024 , eprint=

    DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning , author=. 2024 , eprint=

    2024

  153. [161]

    arXiv preprint arXiv:2004.01339 , year=

    Multi-agent reinforcement learning for networked system control , author=. arXiv preprint arXiv:2004.01339 , year=

    2004 arXiv

  154. [162]

    ArXiv , year=

    LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction , author=. ArXiv , year=

  155. [163]

    Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control , volume =

    Gonçalves, Luís and Schoellig, Angela , year =. Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control , volume =. IEEE Robotics and Automation Letters , doi =

  156. [164]

    IFAC-PapersOnLine , volume=

    Robustness of control barrier functions for safety critical control , author=. IFAC-PapersOnLine , volume=. 2015 , publisher=

    2015

  157. [165]

    , license =

    Caron and al. , license =

  158. [166]

    Disability and Rehabilitation: Assistive Technology , volume=

    A review of computer vision for semi-autonomous control of assistive robotic manipulators (ARMs) , author=. Disability and Rehabilitation: Assistive Technology , volume=. 2020 , publisher=

    2020

  159. [167]

    Journal of guidance, control, and dynamics , volume=

    Relative computer vision-based navigation for small inspection spacecraft , author=. Journal of guidance, control, and dynamics , volume=. 2015 , publisher=

    2015

  160. [168]

    2017 IEEE 56th Annual Conference on Decision and Control (CDC) , pages=

    Hamilton-jacobi reachability: A brief overview and recent advances , author=. 2017 IEEE 56th Annual Conference on Decision and Control (CDC) , pages=. 2017 , organization=

    2017

  161. [169]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  162. [170]

    CoRR , volume =

    Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever , title =. CoRR , volume =

  163. [171]

    2020 , eprint=

    Visual Transformers: Token-based Image Representation and Processing for Computer Vision , author=. 2020 , eprint=

    2020

  164. [172]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    Deep residual learning for image recognition , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  165. [173]

    Proceedings of The 5th Annual Learning for Dynamics and Control Conference , pages =

    In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States , author =. Proceedings of The 5th Annual Learning for Dynamics and Control Conference , pages =

  166. [174]

    IEEE Robotics and Automation Letters , volume=

    Sablas: Learning safe control for black-box dynamical systems , author=. IEEE Robotics and Automation Letters , volume=. 2022 , publisher=

    2022

  167. [175]

    6th Annual Learning for Dynamics & Control Conference , pages=

    Safety filters for black-box dynamical systems by learning discriminating hyperplanes , author=. 6th Annual Learning for Dynamics & Control Conference , pages=. 2024 , organization=

    2024

  168. [176]

    Advances in neural information processing systems , volume=

    Pytorch: An imperative style, high-performance deep learning library , author=. Advances in neural information processing systems , volume=

  169. [177]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Masked autoencoders are scalable vision learners , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  170. [178]

    2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    Drive anywhere: Generalizable end-to-end autonomous driving with multi-modal foundation models , author=. 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2024 , organization=

    2024

  171. [179]

    international conference on machine learning , pages=

    The unsurprising effectiveness of pre-trained vision models for control , author=. international conference on machine learning , pages=. 2022 , organization=

    2022

  172. [180]

    IEEE Transactions on Robotics , volume=

    Barriernet: Differentiable control barrier functions for learning of safe robot control , author=. IEEE Transactions on Robotics , volume=. 2023 , publisher=

    2023

  173. [181]

    2022 International Conference on Robotics and Automation (ICRA) , pages=

    Learning interactive driving policies via data-driven simulation , author=. 2022 International Conference on Robotics and Automation (ICRA) , pages=. 2022 , organization=

    2022

  174. [182]

    Conference on Robot Learning , pages=

    Safe nonlinear control using robust neural lyapunov-barrier functions , author=. Conference on Robot Learning , pages=. 2022 , organization=

    2022

  175. [183]

    2023 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    Safe control using vision-based control barrier function (v-cbf) , author=. 2023 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2023 , organization=

    2023

  176. [184]

    2023 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    Gnm: A general navigation model to drive any robot , author=. 2023 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2023 , organization=

    2023

  177. [185]

    arXiv preprint arXiv:2203.12601 , year=

    R3m: A universal visual representation for robot manipulation , author=. arXiv preprint arXiv:2203.12601 , year=

    arXiv

  178. [186]

    Conference on Robot Learning , pages=

    Real-world robot learning with masked visual pre-training , author=. Conference on Robot Learning , pages=. 2023 , organization=

    2023

  179. [187]

    arXiv preprint arXiv:2502.00935 , year=

    Generalizing safety beyond collision-avoidance via latent-space reachability analysis , author=. arXiv preprint arXiv:2502.00935 , year=

  180. [188]

    arXiv preprint arXiv:2505.00779 , year=

    Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures , author=. arXiv preprint arXiv:2505.00779 , year=

  181. [189]

    Advances in Neural Information Processing Systems , volume=

    Safety gymnasium: A unified safe reinforcement learning benchmark , author=. Advances in Neural Information Processing Systems , volume=

  182. [190]

    arXiv preprint arXiv:1509.02971 , year=

    Continuous control with deep reinforcement learning , author=. arXiv preprint arXiv:1509.02971 , year=

    arXiv

  183. [191]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Deep reinforcement learning with double q-learning , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  184. [192]

    arXiv preprint arXiv:2304.07193 , year=

    Dinov2: Learning robust visual features without supervision , author=. arXiv preprint arXiv:2304.07193 , year=

    arXiv

  185. [193]

    International Conference on Learning Representations , year=

    ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills , author=. International Conference on Learning Representations , year=

  186. [194]

    and Georgiou, T.T

    Fialho, I.J. and Georgiou, T.T. , journal=. Worst case analysis of nonlinear systems , year=

  187. [195]

    2019 18th European control conference (ECC) , pages=

    Control barrier functions: Theory and applications , author=. 2019 18th European control conference (ECC) , pages=. 2019 , organization=

    2019

  188. [196]

    International conference on machine learning , pages=

    Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor , author=. International conference on machine learning , pages=. 2018 , organization=

    2018

  189. [197]

    ImageNet: A large-scale hierarchical image database , year=

    Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Kai Li and Li Fei-Fei , booktitle=. ImageNet: A large-scale hierarchical image database , year=

  190. [199]

    arXiv preprint arXiv:2505.04769 , year=

    Vision-language-action models: Concepts, progress, applications and challenges , author=. arXiv preprint arXiv:2505.04769 , year=

  191. [200]

    Intelligence, Physical and Black, Kevin and Brown, Noah and Darpinian, James and Dhabalia, Karan and Driess, Danny and Esmail, Adnan and Equi, Michael and Finn, Chelsea and Fusai, Niccolo and others , year=. _. 2504.16054 , archivePrefix=

    arXiv

  192. [201]

    European conference on computer vision , pages=

    Grounding dino: Marrying dino with grounded pre-training for open-set object detection , author=. European conference on computer vision , pages=. 2024 , organization=

    2024

  193. [202]

    arXiv preprint arXiv:2503.20020 , year=

    Gemini robotics: Bringing ai into the physical world , author=. arXiv preprint arXiv:2503.20020 , year=

    arXiv

  194. [203]

    arXiv preprint arXiv:2507.06261 , year=

    Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities , author=. arXiv preprint arXiv:2507.06261 , year=

    arXiv

  195. [204]

    arXiv preprint arXiv:2601.14352 , year=

    RoboBrain 2.5: Depth in Sight, Time in Mind , author=. arXiv preprint arXiv:2601.14352 , year=

  196. [205]

    arXiv preprint arXiv:2511.16518 , year=

    MiMo-Embodied: X-Embodied Foundation Model Technical Report , author=. arXiv preprint arXiv:2511.16518 , year=

    arXiv

  197. [206]

    2026 , howpublished=

    2026

  198. [207]

    arXiv preprint arXiv:2511.05889 , year=

    From Words to Safety: Language-Conditioned Safety Filtering for Robot Navigation , author=. arXiv preprint arXiv:2511.05889 , year=

  199. [208]

    UBC Department of Computer Science Technical Report TR-2007-11 , volume=

    A toolbox of level set methods , author=. UBC Department of Computer Science Technical Report TR-2007-11 , volume=

    2007

  200. [209]

    2025 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    Updating robot safety representations online from natural language feedback , author=. 2025 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2025 , organization=

    2025

  201. [210]

    Findings of the Association for Computational Linguistics: ACL 2025 , pages=

    Do vision-language models have internal world models? towards an atomic evaluation , author=. Findings of the Association for Computational Linguistics: ACL 2025 , pages=

    2025

  202. [211]

    arXiv preprint arXiv:2603.25887 , year=

    World Reasoning Arena , author=. arXiv preprint arXiv:2603.25887 , year=

  203. [212]

    2024 , publisher=

    Wang, Yufei and Sun, Zhanyi and Zhang, Jesse and Xian, Zhou and Biyik, Erdem and Held, David and Erickson, Zackory , booktitle=. 2024 , publisher=

    2024

  204. [213]

    arXiv preprint arXiv:2506.12822 , year=

    Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models , author=. arXiv preprint arXiv:2506.12822 , year=

    arXiv

  205. [214]

    2023 , url=

    Xie, Tianbao and Zhao, Siheng and Wu, Chen Henry and Liu, Yitao and Luo, Qian and Zhong, Victor and Yang, Yanchao and Yu, Tao , journal=. 2023 , url=

    2023

  206. [215]

    2025 , url=

    Gu, Qiao and Ju, Yuanliang and Sun, Shengxiang and Gilitschenski, Igor and Nishimura, Haruki and Itkina, Masha and Shkurti, Florian , journal=. 2025 , url=

    2025

  207. [216]

    2024 , url=

    Duan, Jiafei and Pumacay, Wilbert and Kumar, Nishanth and Wang, Yi Ru and Tian, Shulin and Yuan, Wentao and Krishna, Ranjay and Fox, Dieter and Mandlekar, Ajay and Guo, Yijie , journal=. 2024 , url=

    2024

  208. [217]

    arXiv preprint arXiv:2512.01946 , year=

    Scaling Cross-Environment Failure Reasoning Data for Vision-Language Robotic Manipulation , author=. arXiv preprint arXiv:2512.01946 , year=

  209. [218]

    arXiv preprint arXiv:2412.04835 , year=

    Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards for Visuomotor Robot Policy Alignment , author=. arXiv preprint arXiv:2412.04835 , year=

    arXiv

  210. [219]

    arXiv preprint arXiv:2509.15937 , year=

    A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning , author=. arXiv preprint arXiv:2509.15937 , year=

  211. [220]

    2025 , url=

    Zhao, Zhengyue and Ma, Yingzi and Jha, Somesh and Pavone, Marco and McDaniel, Patrick and Xiao, Chaowei , journal=. 2025 , url=

    2025

  212. [221]

    Proceedings of Robotics: Science and Systems , year=

    Generalizing Safety Beyond Collision-Avoidance via Latent-Space Reachability Analysis , author=. Proceedings of Robotics: Science and Systems , year=. doi:10.15607/RSS.2025.XXI.113 , url=

    2025 doi

  213. [222]

    arXiv preprint arXiv:2509.14758 , year=

    Designing latent safety filters using pre-trained vision models , author=. arXiv preprint arXiv:2509.14758 , year=

  214. [223]

    Advances in neural information processing systems , volume=

    Inverse reward design , author=. Advances in neural information processing systems , volume=

  215. [224]

    Advances in neural information processing systems , volume=

    Deep reinforcement learning from human preferences , author=. Advances in neural information processing systems , volume=

  216. [225]

    arXiv preprint arXiv:2601.00675 , year=

    RoboReward: General-Purpose Vision-Language Reward Models for Robotics , author=. arXiv preprint arXiv:2601.00675 , year=

  217. [226]

    Reinforcement learning algorithms: Analysis and Applications , pages=

    Reward function design in reinforcement learning , author=. Reinforcement learning algorithms: Analysis and Applications , pages=. 2021 , publisher=

    2021

  218. [227]

    arXiv preprint arXiv:2509.19555 , year=

    AnySafe: Adapting Latent Safety Filters at Runtime via Safety Constraint Parameterization in the Latent Space , author=. arXiv preprint arXiv:2509.19555 , year=

  219. [228]

    arXiv preprint arXiv:2512.11891 , year=

    VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer , author=. arXiv preprint arXiv:2512.11891 , year=

    arXiv

  220. [229]

    arXiv preprint arXiv:2504.19989 , year=

    Hjrno: Hamilton-Jacobi reachability with neural operators , author=. arXiv preprint arXiv:2504.19989 , year=

    arXiv

  221. [230]

    Neurocomputing , volume=

    Hamilton--Jacobi based policy-iteration via deep operator learning , author=. Neurocomputing , volume=. 2025 , publisher=

    2025

  222. [231]

    IEEE Robotics and Automation Letters , year=

    Open-World Task and Motion Planning Via Vision-Language Model Generated Constraints , author=. IEEE Robotics and Automation Letters , year=

  223. [232]

    2023 , eprint =

    Brohan, Anthony and Brown, Noah and Carbajal, Justice and Chebotar, Yevgen and others , booktitle =. 2023 , eprint =

    2023

  224. [233]

    Proceedings of the IEEE International Conference on Robotics and Automation , pages =

    Open. Proceedings of the IEEE International Conference on Robotics and Automation , pages =. 2024 , doi =. 2310.08864 , archivePrefix =

    2024 arXiv

  225. [234]

    Proceedings of Robotics: Science and Systems , year =

    Octo: An Open-Source Generalist Robot Policy , author =. Proceedings of Robotics: Science and Systems , year =. 2405.12213 , archivePrefix =

    arXiv

  226. [235]

    2024 , eprint =

    _0 : A Vision-Language-Action Flow Model for General Robot Control , author =. 2024 , eprint =

    2024

  227. [236]

    arXiv preprint arXiv:2505.06794 , year=

    Dynamic Safety in Complex Environments: Synthesizing Safety Filters with Poisson's Equation , author=. arXiv preprint arXiv:2505.06794 , year=

    arXiv

  228. [237]

    arXiv preprint arXiv:2508.11129 , year=

    Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC , author=. arXiv preprint arXiv:2508.11129 , year=

    arXiv

  229. [238]

    arXiv preprint arXiv:2510.25913 , year=

    Risk-Aware Safety Filters with Poisson Safety Functions and Laplace Guidance Fields , author=. arXiv preprint arXiv:2510.25913 , year=

  230. [239]

    arXiv preprint arXiv:2603.05497 , year=

    Safe-SAGE: Social-Semantic Adaptive Guidance for Safe Engagement through Laplace-Modulated Poisson Safety Functions , author=. arXiv preprint arXiv:2603.05497 , year=

    arXiv

  231. [240]

    arXiv preprint arXiv:2604.21189 , year=

    Full-Body Dynamic Safety for Robot Manipulators: 3D Poisson Safety Functions for CBF-Based Safety Filters , author=. arXiv preprint arXiv:2604.21189 , year=

    arXiv

  232. [241]

    Journal of Machine Learning Research , volume=

    A survey of preference-based reinforcement learning methods , author=. Journal of Machine Learning Research , volume=

  233. [242]

    arXiv preprint arXiv:2602.19532 , year=

    Bellman value decomposition for task logic in safe optimal control , author=. arXiv preprint arXiv:2602.19532 , year=

    arXiv

  234. [243]

    arXiv preprint arXiv:2605.01051 , year=

    Value Functions for Temporal Logic: Optimal Policies and Safety Filters , author=. arXiv preprint arXiv:2605.01051 , year=

    arXiv

  235. [244]

    2025 , eprint=

    Gemini Robotics: Bringing. 2025 , eprint=

    2025

  236. [245]

    2025 , eprint=

    Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer , author=. 2025 , eprint=

    2025

  237. [246]

    arXiv preprint arXiv:2507.02029 , year=

    RoboBrain 2.0 Technical Report , author=. arXiv preprint arXiv:2507.02029 , year=

    arXiv

  238. [247]

    arXiv preprint arXiv:2503.15558 , year=

    Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning , author=. arXiv preprint arXiv:2503.15558 , year=

    arXiv

  239. [248]

    Cosmos-Reason2: Physical

    NVIDIA , year=. Cosmos-Reason2: Physical

  240. [249]

    Cosmos 3: Omnimodal World Models for Physical

    NVIDIA , journal=. Cosmos 3: Omnimodal World Models for Physical

  241. [250]

    Qwen2.5-

    Bai, Shuai and Chen, Keqin and Liu, Xuejing and others , journal=. Qwen2.5-

  242. [251]

    Bai, Shuai and Cai, Yuxuan and Chen, Ruizhe and others , journal=. Qwen3-

  243. [252]

    arXiv preprint arXiv:2009.12293 , year=

    robosuite: A modular simulation framework and benchmark for robot learning , author=. arXiv preprint arXiv:2009.12293 , year=

    2009 arXiv

  244. [253]

    arXiv preprint arXiv:1812.05905 , year=

    Soft actor-critic algorithms and applications , author=. arXiv preprint arXiv:1812.05905 , year=

    arXiv

  245. [254]

    arXiv preprint arXiv:2302.12766 , year=

    Language-driven representation learning for robotics , author=. arXiv preprint arXiv:2302.12766 , year=

    arXiv

  246. [255]

    arXiv preprint arXiv:2602.13476 , year=

    Asyncvla: An asynchronous vla for fast and robust navigation on the edge , author=. arXiv preprint arXiv:2602.13476 , year=

  247. [256]

    arXiv preprint arXiv:2511.00088 , year=

    Alpamayo-r1: Bridging reasoning and action prediction for generalizable autonomous driving in the long tail , author=. arXiv preprint arXiv:2511.00088 , year=

    arXiv

  248. [257]

    Forty-second International Conference on Machine Learning , year=

    Grammar-forced translation of natural language to temporal logic using LLMs , author=. Forty-second International Conference on Machine Learning , year=

  249. [258]

    arXiv preprint arXiv:2504.21022 , year=

    Conformalnl2ltl: Translating natural language instructions into temporal logic formulas with conformal correctness guarantees , author=. arXiv preprint arXiv:2504.21022 , year=

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.