Pith. sign in

REVIEW 2 major objections 5 minor 10 cited by

Hume: Introducing System-2 Thinking in Visual-Language-Action Model

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Hume is a dual-system VLA that adds value-guided slow thinking to a fast denoising controller, and it reports higher success rates than prior VLA models on simulation benchmarks and real robots.

desk verdict Hume has strong empirical results and a genuinely new dual-system VLA architecture, but the key 'value-guided thinking' claim is not isolated because the ablation compares against random selection rather than always using the fully denoised candidate. read the letter →

arxiv 2505.21432 v4 pith:B426X3KH submitted 2025-05-27 cs.RO cs.AI

Haoming Song , Delin Qu , Yuanqi Yao , Qizhi Chen , Qi Lv , Yiwen Tang , Modi Shi , Guanghui Ren
show 4 more authors
Maoqing Yao Bin Zhao Dong Wang Xuelong Li
This is my paper
classification cs.ROcs.AI
keywords vision-language-actionmodeldual-systemarchitecturesystem-2thinkingvalue-guidedselectionbest-of-Nflowmatchingcascadeddenoisingrobotmanipulation
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

Hume is a two-part robot-learning model that tries to give a vision-language-action policy the equivalent of slow, deliberative thinking before it acts. The slow part samples several candidate action trajectories, scores each one with a learned value estimate, and keeps the best; the fast part then cleans up and executes that trajectory in real time. The paper argues that this division lets a robot handle complex, long-horizon tasks without sacrificing control speed, and it reports higher success rates than earlier VLA models on the LIBERO and SimplerEnv benchmarks and on real WidowX, Franka, and humanoid robots.

What carries the argument

The load-bearing mechanism is the asynchronous dual-system loop. System 2's flow-matching head, a generative model that learns a vector field turning noise into actions, produces $N$ candidate action chunks at different noise levels; a value-query head with two critic networks, trained by calibrated Q-learning, estimates $Q(q_t, A_t)$ for each candidate. Best-of-$N$ selection picks the highest-value chunk. System 1 then splits that chunk into sub-chunks and applies cascaded denoising, integrating its own vector field starting from the selected partially denoised action rather than from random noise, which is what lets a small fast network finish the action at 90 Hz while System 2 thinks at 4 Hz.

What would settle it

Run System 1 from its deployed input, a partially denoised System-2 chunk, and compare against running it from Gaussian noise at the same noise level on the same held-out tasks; if the Gaussian-initialized runs match or beat the deployed ones, cascaded denoising is not providing the claimed refinement and the reported advantage would not survive that test.

Watch

Extended reading notes

Core claim

The paper's central claim is that a robot policy can get the benefits of slow deliberation and fast reaction by splitting them across two systems. System 2, built on a vision-language model with a flow-matching head, generates $N$ candidate action chunks that stop at different noise levels; a value-query head trained with calibrated offline Q-learning estimates $Q(q_t, A_t)$ for each candidate, and the chunk with the highest value is selected. System 1, a lightweight visuomotor denoiser, starts from a segment of that selected chunk and integrates its own learned vector field in a cascade of denoising steps, producing the final high-frequency actions. The paper reports this arrangement achieves 98.6% average success on LIBERO, 72.6% and 74.1% on SimplerEnv's WidowX and Google-robot suites, and a 12.9% average improvement over π0 in real-world deployments.

Load-bearing premise

The design assumes the fast System 1 can refine partially denoised candidates from System 2 even though it was trained only on ground-truth actions corrupted with Gaussian noise, and that mismatch is never analyzed.

Editorial extensions

If this is right

  • If Hume's central claim holds, a robot policy can reason about candidate futures at a slow timescale and still control a robot at a fast timescale, resolving the usual trade-off between thinking and reactivity.
  • The value-guided best-of-$N$ selection is a direct way to turn any flow-matching action generator into a policy that can recover from unexpected states, because it re-samples and re-scores candidates when the robot observes something new.
  • The cascaded denoising design implies the fast controller can be a small network that only removes the remaining noise from a good starting point, keeping deployment compute low relative to having the slow system produce fully denoised actions.
  • The reported gains on long-horizon and deformable-object tasks suggest that System-2-style selection helps most where a single greedy action prediction is most likely to drift into an unrecoverable state.

Reading between the lines

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

  • Editorial extension: the paper does not sweep the number of candidates $N$; a testable prediction from its own logic is that success should rise with $N$ up to a plateau set by the quality of the value estimates, and then flatten or drop.
  • Editorial inference: because System 1 is trained on Gaussian-corrupted ground-truth actions but deployed on partially denoised solver outputs, part of the reported gain may depend on the denoiser tolerating that distribution shift; matching the training noise to the deployment distribution is a natural next experiment.
  • Editorial connection: the same value-guided best-of-$N$ ranking could be applied to other generative robot policies, including navigation or bimanual controllers, whenever an offline value head can be trained on demonstration data.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The paper proposes Hume, a dual-system vision-language-action (VLA) model that combines a low-frequency System 2 performing value-guided best-of-N action-candidate selection with a high-frequency System 1 that refines the selected candidate via cascaded action denoising. System 2 is built on a VLM backbone with a flow-matching action head and a value-query head trained with Cal-QL; System 1 is a lightweight visuomotor policy trained with the same flow-matching objective. The paper reports state-of-the-art results on LIBERO and SimplerEnv benchmarks and on real WidowX, Franka, and AgiBot G-1 robots, and includes ablations of the main components.

Significance. If the empirical claims hold, the paper makes a useful contribution to the growing area of dual-system VLA models: it demonstrates a concrete way to add test-time 'thinking' through value-guided candidate selection and shows strong performance across multiple simulation benchmarks and real-robot platforms. The manuscript is commendable for reporting standard errors on the LIBERO simulation results, for evaluating across three distinct robot platforms, and for providing ablations that mostly show consistent drops when components are removed. The main significance hinges on whether the value-guided mechanism is truly responsible for the gains, and on whether the cascaded denoising module is trained and deployed under matching input distributions; both points require additional evidence.

major comments (2)
  1. [§4.3, Table 3; Eq. (1)] The ablation labeled 'w/o Value-Query Head' (#5) selects one of the five candidates uniformly at random, but the candidates by construction have different noise levels: n=1 is fully denoised while n>1 are partially denoised (Eq. (1)). A random selector will usually pass a partially denoised action to System 1, so the comparison #1 vs #5 conflates the effect of value-guided selection with the effect of not choosing randomly. The paper needs a control that always selects the fully denoised candidate (n=1) among the N=5 candidates and passes it to System 1. The 'w/o Repeat Sampling' ablation (#3) generates only one candidate and therefore also does not isolate the value-selection rule. Without this baseline, the claim that System-2 value-guided thinking improves performance is not cleanly supported; the observed gains could simply reflect avoiding the noisy candidates that random selection frequently picks.
  2. [§3.2, Eqs. (2)-(3)] There is a train-inference distribution mismatch in the cascaded denoising module. System 1 is trained on ground-truth actions corrupted by a flow-matching forward process q(·|A), as in Eq. (2). At deployment, Eq. (3) starts the ODE integration from a partially denoised action chunk produced by System 2's truncated Euler integration from an independent Gaussian sample. That starting distribution is not the same as the training corruption distribution, and the paper does not analyze or correct the mismatch. Since cascaded denoising is a central contribution (and ablations #1 vs #2 and #1 vs #4 show large effects), the authors should either provide evidence that the learned vector field transfers to this input distribution, retrain System 1 on actual System 2 outputs, or add an experiment that directly quantifies the sensitivity to this mismatch.
minor comments (5)
  1. [Figure 7 and §4.2] The real-world ablation results, including the large reported drops (e.g., -78% for 'w/o Value-Query Head'), are presented without the number of trials or any confidence interval; the simulation tables include standard errors, and the real-world results should be reported with comparable uncertainty information.
  2. [Appendix B.1] The hyperparameters α and γ used for the value-query head are not reported; since the behavior of Cal-QL depends on both, please specify their values.
  3. [Figures 5 and 6] The word 'success' is misspelled as 'sucess' in the captions of Figures 5 and 6.
  4. [Appendix A.2] The sentence stating that ground-truth actions 'proves' the value head is not overfitted uses too strong a claim; the visualization is suggestive but does not constitute a proof, so I recommend rewording to 'indicates'.
  5. [§3.1] There is a grammatical issue in 'the value-query head estimate the state-action value' that should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the value-guided selection and cascaded denoising are defined by explicit equations, and the reported benchmark and real-robot results come from external evaluation rather than from the fitted value head or reward.

full rationale

After walking the paper's derivation chain, I find no step where a claimed prediction or first-principles result is equivalent to its inputs by construction. The value-query head is trained with offline RL (Cal-QL) on a hand-specified reward, and its outputs are used only to rank sampled candidate action chunks; the reported success rates come from LIBERO, SimplerEnv, and real-robot trials, none of which are generated by the value head or by the fitted reward. The System-2/System-1 cascade is defined by explicit equations (Eq. 1-3), and although there is a genuine train-inference distribution mismatch (System 1 is trained on Gaussian-corrupted ground-truth actions but at inference starts from partially denoised ODE-solver outputs), that is a correctness or robustness concern, not a circular definition. The ablation of value-guided thinking (#1 vs. #5 in Table 3) compares against random candidate selection, which is a weak control: the meaningful control would be always passing the fully denoised candidate, ignoring the value head. However, an incomplete ablation is not circularity: the value-query head is not defined in terms of the downstream success rate, and the baseline numbers are measured externally. Self-citations appear (e.g., SpatialVLA as a baseline), but they are not load-bearing justifications of Hume's mechanism, and no uniqueness theorem or ansatz is imported from the authors' prior work. I therefore find no significant circularity.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim relies on several hand-chosen hyperparameters (N, ξ, α, reward horizon) and unvalidated distribution assumptions about System 1 and the value head. No new physical entities are introduced.

free parameters (4)
  • N (number of System-2 candidates) = 5
    Hand-chosen; no sensitivity analysis is shown in the paper.
  • ξ (noise gap between candidates) = not reported
    Controls the spread of noise levels in candidate generation (Eq. 1), but its value is never stated.
  • α (Cal-QL conservative penalty) = not reported
    Hyperparameter in Eq. (4) that weights the conservative regularizer; value not stated in the paper.
  • reward horizon (last 3 transitions) = 3
    The reward function gives +1 only to the last 3 transitions of each episode; this hand-chosen constant defines the success proxy used to train the value head.
assumptions (3)
  • domain assumption The hand-crafted reward of +1 on the last 3 transitions of each episode is a valid proxy for task success.
    Used to train the value-query head (Section 3.1). If the last steps do not correspond to task success, the Q estimates will be misaligned.
  • domain assumption System 1, trained only on ground-truth actions corrupted with Gaussian noise, can denoise partially denoised outputs from System 2 at inference.
    Eq. (2) trains with ground-truth actions, while Eq. (3) starts from the output of System 2's ODE solver. This distribution shift is not analyzed or corrected.
  • domain assumption The value-query head's Q estimates on partially denoised candidate actions are accurate enough to rank them, despite training on ground-truth actions only.
    At inference the value head scores candidate actions that are not fully denoised, but it was trained only on ground-truth actions, making this an out-of-distribution input.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Hume: Introducing System-2 Thinking in Visual-Language-Action Model." pith.science (2026). https://pith.science/paper/B426X3KH

@misc{pith2026250521432,
  author       = {Pith},
  title        = {Pith review of: Hume: Introducing System-2 Thinking in Visual-Language-Action Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B426X3KH}},
  note         = {Machine review of arXiv:2505.21432}
}
read the original abstract

Humans practice slow thinking before performing actual actions when handling complex tasks in the physical world. This thinking paradigm, recently, has achieved remarkable advancement in boosting Large Language Models (LLMs) to solve complex tasks in digital domains. However, the potential of slow thinking remains largely unexplored for robotic foundation models interacting with the physical world. In this work, we propose Hume: a dual-system Vision-Language-Action (VLA) model with value-guided System-2 thinking and cascaded action denoising, exploring human-like thinking capabilities of Vision-Language-Action models for dexterous robot control. System 2 of Hume implements value-Guided thinking by extending a Vision-Language-Action Model backbone with a novel value-query head to estimate the state-action value of predicted actions. The value-guided thinking is conducted by repeat sampling multiple action candidates and selecting one according to state-action value. System 1 of Hume is a lightweight reactive visuomotor policy that takes System 2 selected action and performs cascaded action denoising for dexterous robot control. At deployment time, System 2 performs value-guided thinking at a low frequency while System 1 asynchronously receives the System 2 selected action candidate and predicts fluid actions in real time. We show that Hume outperforms the existing state-of-the-art Vision-Language-Action models across multiple simulation benchmark and real-robot deployments.

Figures

Figures reproduced from arXiv: 2505.21432 by the authors.

Figure 1
Figure 1. We present Hume, a dual-system vision-language-action model exploring human-like [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of Hume. Hume contains two systems working asynchronously. Given the observation, System 2 of Hume first generates N candidate action chunks with different noise level, and the best-of-N candidate with the highest Q value will be selected as the optimal candidate Aτ ∗ t , which is segmented and conveyed to System 1 for continuous action denoising. 3.1 Value-Guided System-2 Thinking As shown in [PITH_FULL_I… view at source ↗
Figure 3
Figure 3. Value Map of Candidate Actions. The candidate actions A τn t sampled from System 2 and ground-truth actions AGT t are projected into the same two-dimensional space through Principal Component Analysis (PCA). The intensity of colors indicates the magnitude of state-action values Q(qt, A τn t ) of candidate actions. and aggregates necessary information from the VLM inputs, i.e., current RGB images it = [I 1 t , ..., I… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Experiments setup on WidowX, AgiBot G-1 and Franka Robot. We evaluate Hume across 3 simulation environments and 3 different real-world robotic platforms, covering 15 robot learning scenarios and 21 real-world manipulation tasks. 1. How is Hume’s capability to learn mul…
Figure 5
Figure 5. Figure 5: Real-world evaluation on WidowX Robot tasks. We evaluate Hume across 10 tasks with varying backgrounds, poses, and motion distractors. Hume achieves the highest average success rate, surpassing π0 and all other generalist manipulation policies in comparative evaluation…
Figure 6
Figure 6. Figure 6: Evaluation on Franka and Agibot G-1 Robot. We evaluate Hume across 11 real-world common tasks on Franka and Agibot G-1 robot. (#Pour Water), Hume achieves success rate of 82%, significantly improving by +20% over π0, and +60% over GR00T. Additionally, Hume also achieve…
Figure 7
Figure 7. Figure 7: Real-world Ablations on WidowX, Franka and Agibot G-1 Robot. We conducted ablation studies of Hume across 3 different real-world robotic platforms, covering 15 robot learning scenarios and 21 real-world manipulation tasks. 9 [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Visualization of Hume in Push-T. We visualize the candidate actions A τi t sampled from System 2 with dashed lines and the final executed action A˜ 1 t+kh from System 1 with solid line. The intensity of colors of lines indicates the magnitude of state-action values Q(q…
Figure 9
Figure 9. Figure 9: visualizes the cascaded action denoising process in LIBERO-OBJECT. For the 7 dimensions in action space (X, Y, Z, Roll, Pitch, Yaw, Gripper), we pair them into combinations for illustration, i.e., X-Y, X-Z, Y-Z, and R-P. The drew points is down-sampled from the actual …
Figure 10
Figure 10. Figure 10: Evaluation Setup of WidowX 250s. We evaluated models with 9 tasks on WidowX 250s to verify the model’s learning ability on a large multi-task manipulation dataset. • Restock the hanging basket area: This task requires the robot to grab snacks from a cart and place the…
Figure 11
Figure 11. Figure 11: Evaluation Setup of Frank-Emika-Panda. We evaluated policies on Fnraka robot with 7 tasks, including instruction following, articulated manipulation, and pick and place tasks. language instructions and hand it to the human. We used different types of bottles in the te…
Figure 12
Figure 12. Figure 12: Evaluation Setup of AgiBot G-1. We evaluated policies on four challenging tasks on AgiBot G-1 to test ability of controlling a humanoid robot completing dexterous and long-horizon tasks. As shown in [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: Failure Recovery of Hume. When a failure occurs, such as missing the grasping position, other policies fall into a failure state, and Hume selects the correct action through value-guided thinking to help it recover from the failure state and successfully complete the …

Discussion (0). Sign in to comment.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Q-VGM: Q-Value-Gradient Matching for Off-Policy Reinforcement Learning of Flow-Matching VLA

    cs.RO 2026-06 unverdicted novelty 7.0 of 10

    Q-VGM offline RL fine-tuning converts critic Q-gradients into residual velocity targets for flow-matching VLAs, raising LIBERO success from 75.0% to 92.5%.

  2. Token-Wise Latent Streaming from Slow Reasoners to Fast Planners for Dynamic Vision Language Navigation

    cs.RO 2026-07 conditional novelty 6.0 of 10

    Streaming intermediate hidden states from a slow VLM to a fast flow-matching planner, token by token, improves dynamic social VLN success and reduces observation staleness.

  3. CosFly-VLA: A Spatially Aware Vision-Language-Action Model for UAV Tracking

    cs.RO 2026-07 conditional novelty 6.0 of 10

    CosFly-VLA, a spatially grounded VLA trained with CPT, curriculum SFT, CoT, and RL, reports 34–35% lower open-loop waypoint error and higher closed-loop success than OpenVLA in simulated UAV occlusion tracking.

  4. ABot-N1: Toward a General Visual Language Navigation Foundation Model

    cs.CV 2026-07 unverdicted novelty 6.0 of 10

    A slow–fast VLN model that routes five navigation tasks through CoT plus image-space pixel goals reaches SOTA on established and new urban benchmarks, including 77.3% POI arrival.

  5. Look Before You Leap: Distilling Tree Search into Action Evaluation for Frozen VLA Models

    cs.RO 2026-07 conditional novelty 6.0 of 10

    Distilling MCTS returns into a lightweight Q-model lets frozen VLAs select higher-return actions at test time, lifting success rates and letting a 9B model beat a 27B baseline at lower latency.

  6. ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data

    cs.RO 2026-03 conditional novelty 5.0 of 10

    An open-loop generation-then-tracking system maps one egocentric image plus language into Unitree G1 whole-body interactions using only human egocentric motion data.

  7. Skill-Nav: Enhanced Navigation with Versatile Quadrupedal Locomotion via Waypoint Interface

    cs.RO 2025-06 conditional novelty 5.0 of 10

    A waypoint-based interface between planners and a trained quadrupedal locomotion policy enables navigation over diverse obstacles in simulation and on a real robot.

  8. StemVLA:An Open-Source Vision-Language-Action Model with Future 3D Spatial Geometry Knowledge and 4D Historical Representation

    cs.RO 2026-02 reject novelty 4.0 of 10

    StemVLA supervises a GPT-2-based VLA with predicted future 3D-geometry features (VGGT) and temporally aggregated history, reporting 86.0% on LIBERO-Long - but its CALVIN results and equations are placeholders.

  9. Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Survey

    cs.RO 2025-10 conditional novelty 4.0 of 10

    A survey that groups VLA efficiency techniques into four categories: model architecture, perception features, action generation, and training/inference strategies.

  10. Spatial navigation in preclinical Alzheimer's disease: A review

    q-bio.NC 2026-03 unverdicted novelty 3.0 of 10

    Spatial navigation performance, particularly path integration and wayfinding, correlates with AD biomarkers such as p-tau in cognitively unimpaired at-risk individuals and may enable earlier detection than episodic me...

Reference graph

Works this paper leans on

76 extracted references · 26 canonical work pages · cited by 10 Pith papers

  1. [1]

    Vision-language foundation models as effective robot imitators

    Xinghang Li, Minghuan Liu, Hanbo Zhang, Cunjun Yu, Jie Xu, Hongtao Wu, Chilam Cheang, Ya Jing, Weinan Zhang, Huaping Liu, et al. Vision-language foundation models as effective robot imitators. In Proceedings of International Conference on Learning Representations (ICLR), 2024

    2024

  2. [2]

    Rt-2: Vision-language-action models transfer web knowledge to robotic control

    Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.15818, 2023

    arXiv 2023

  3. [3]

    Fastumi: A scalable and hardware-independent universal manipulation interface with dataset

    Kehui Liu, Chuyue Guan, Zhongjie Jia, Ziniu Wu, Xin Liu, Tianyu Wang, Shuai Liang, Pengan Chen, Pingrui Zhang, Haoming Song, et al. Fastumi: A scalable and hardware-independent universal manipulation interface with dataset. arXiv e-prints, pages arXiv–2409, 2024

    work page 2024

  4. [4]

    Openvla: An open-source vision-language-action model

    Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source vision-language-action model. arXiv preprint arXiv:2406.09246, 2024

    arXiv 2024

  5. [5]

    Learning 2d invariant affordance knowledge for 3d affordance grounding

    Xianqiang Gao, Pingrui Zhang, Delin Qu, Dong Wang, Zhigang Wang, Yan Ding, and Bin Zhao. Learning 2d invariant affordance knowledge for 3d affordance grounding. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 3095–3103, 2025

    work page 2025

  6. [6]

    Fast: Efficient action tokenization for vision-language-action models

    Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for vision-language-action models. arXiv preprint arXiv:2501.09747, 2025

    arXiv 2025

  7. [7]

    Improving domain generalization in self-supervised monocular depth estimation via stabilized adversarial training

    Yuanqi Yao, Gang Wu, Kui Jiang, Siao Liu, Jian Kuai, Xianming Liu, and Junjun Jiang. Improving domain generalization in self-supervised monocular depth estimation via stabilized adversarial training. In European Conference on Computer Vision, pages 183–201. Springer, 2024

    work page 2024

  8. [8]

    Moma-kitchen: A 100k+ benchmark for affordance-grounded last-mile navigation in mobile manipulation

    Pingrui Zhang, Xianqiang Gao, Yuhan Wu, Kehui Liu, Dong Wang, Zhigang Wang, Bin Zhao, Yan Ding, and Xuelong Li. Moma-kitchen: A 100k+ benchmark for affordance-grounded last-mile navigation in mobile manipulation. arXiv preprint arXiv:2503.11081, 2025

    arXiv 2025

Show all 76 references
  1. [9]

    Orla*: Mobile manipulator-based object rearrangement with lazy a star

    Kai Gao, Zhaxizhuoma, Yan Ding, Shiqi Zhang, and Jingjin Yu. Orla*: Mobile manipulator-based object rearrangement with lazy a star. arXiv preprint arXiv:2309.13707, 2023

    2023 arXiv

  2. [10]

    Decentralized transformers with centralized aggregation are sample-efficient multi-agent world models

    Yang Zhang, Chenjia Bai, Bin Zhao, Junchi Yan, Xiu Li, and Xuelong Li. Decentralized transformers with centralized aggregation are sample-efficient multi-agent world models. arXiv preprint arXiv:2406.15836, 2024

    2024 arXiv

  3. [11]

    Gemini robotics: Bringing ai into the physical world

    Gemini Robotics Team, Saminda Abeyruwan, Joshua Ainslie, Jean-Baptiste Alayrac, Montserrat Gonzalez Arenas, Travis Armstrong, Ashwin Balakrishna, Robert Baruch, Maria Bauza, Michiel Blokzijl, et al. Gemini robotics: Bringing ai into the physical world. arXiv preprint arXiv:250...

    2025 arXiv

  4. [12]

    A vision-language-action flow model for general robot control

    Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. A vision-language-action flow model for general robot control. arXiv preprint arXiv:2410.24164, 2024

    arXiv

  5. [13]

    Thinking, fast and slow

    Daniel Kahneman. Thinking, fast and slow. Farrar, Straus and Giroux, 2011

    2011

  6. [14]

    Coherent: Collaboration of heterogeneous multi-robot system with large language models

    Kehui Liu, Zixin Tang, Dong Wang, Zhigang Wang, Xuelong Li, and Bin Zhao. Coherent: Collaboration of heterogeneous multi-robot system with large language models. arXiv preprint arXiv:2409.15146, 2024

    2024 arXiv

  7. [15]

    Kinematic- aware prompting for generalizable articulated object manipulation with llms

    Wenke Xia, Dong Wang, Xincheng Pang, Zhigang Wang, Bin Zhao, Di Hu, and Xuelong Li. Kinematic- aware prompting for generalizable articulated object manipulation with llms. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 2073–2080. IEEE, 2024

    2024

  8. [16]

    More: Mixture of residual experts for humanoid lifelike gaits learning on complex terrains

    Dewei Wang, Xinmiao Wang, Xinzhe Liu, Jiyuan Shi, Yingnan Zhao, Chenjia Bai, and Xuelong Li. More: Mixture of residual experts for humanoid lifelike gaits learning on complex terrains. arXiv preprint arXiv:2506.08840, 2025

    2025 arXiv

  9. [17]

    Skill-nav: Enhanced navigation with versatile quadrupedal locomotion via waypoint interface

    Dewei Wang, Chenjia Bai, Chenhui Li, Jiyuan Shi, Yan Ding, Chi Zhang, and Bin Zhao. Skill-nav: Enhanced navigation with versatile quadrupedal locomotion via waypoint interface. arXiv preprint arXiv:2506.21853, 2025

    2025 arXiv

  10. [18]

    Robotic policy learning via human-assisted action preference optimization

    Wenke Xia, Yichu Yang, Hongtao Wu, Xiao Ma, Tao Kong, and Di Hu. Robotic policy learning via human-assisted action preference optimization. arXiv preprint arXiv:2506.07127, 2025. 19

    2025

  11. [19]

    Phoenix: A motion-based self-reflection framework for fine-grained robotic action correction

    Wenke Xia, Ruoxuan Feng, Dong Wang, and Di Hu. Phoenix: A motion-based self-reflection framework for fine-grained robotic action correction. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 6981–6990, 2025

    2025

  12. [20]

    Think small, act big: Primitive prompt learning for lifelong robot manipulation

    Yuanqi Yao, Siao Liu, Haoming Song, Delin Qu, Qizhi Chen, Yan Ding, Bin Zhao, Zhigang Wang, Xuelong Li, and Dong Wang. Think small, act big: Primitive prompt learning for lifelong robot manipulation. arXiv preprint arXiv:2504.00420, 2025

    2025 arXiv

  13. [21]

    Spatial-temporal graph diffusion policy with kinematic modeling for bimanual robotic manipulation

    Qi Lv, Hao Li, Xiang Deng, Rui Shao, Yinchuan Li, Jianye Hao, Longxiang Gao, Michael Yu Wang, and Liqiang Nie. Spatial-temporal graph diffusion policy with kinematic modeling for bimanual robotic manipulation. In Proceedings of the Computer Vision and Pattern Recognition Confe...

    2025

  14. [22]

    Chain-of-thought prompting elicits reasoning in large language models

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Inform...

    2022

  15. [23]

    Robotic control via embodied chain-of-thought reasoning

    Michał Zawalski, William Chen, Karl Pertsch, Oier Mees, Chelsea Finn, and Sergey Levine. Robotic control via embodied chain-of-thought reasoning. arXiv preprint arXiv:2407.08693, 2024

    2024 arXiv

  16. [24]

    A dual process vla: Efficient robotic manipulation leveraging vlm

    ByungOk Han, Jaehong Kim, and Jinhyeok Jang. A dual process vla: Efficient robotic manipulation leveraging vlm. In Conference on Robot Learning (CoRL), 2024

    2024

  17. [25]

    Hirt: Enhancing robotic control with hierarchical robot transformers

    Jianke Zhang, Yanjiang Guo, Xiaoyu Chen, Yen-Jen Wang, Yucheng Hu, Chengming Shi, and Jianyu Chen. Hirt: Enhancing robotic control with hierarchical robot transformers. arXiv preprint arXiv:2410.05273, 2024

    2024 arXiv

  18. [26]

    Towards synergistic, generalized, and efficient dual-system for robotic manipulation

    Qingwen Bu, Hongyang Li, Li Chen, Jisong Cai, Jia Zeng, Heming Cui, Maoqing Yao, and Yu Qiao. Towards synergistic, generalized, and efficient dual-system for robotic manipulation. arXiv preprint arXiv:2410.08001, 2024

    2024 arXiv

  19. [27]

    Dexvla: Vision- language model with plug-in diffusion expert for general robot control

    Junjie Wen, Yichen Zhu, Jinming Li, Zhibin Tang, Chaomin Shen, and Feifei Feng. Dexvla: Vision- language model with plug-in diffusion expert for general robot control. arXiv preprint arXiv:2502.05855, 2025

    2025 arXiv

  20. [28]

    Gr00t n1: An open foundation model for generalist humanoid robots

    Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al. Gr00t n1: An open foundation model for generalist humanoid robots. arXiv preprint arXiv:2503.14734, 2025

    2025 arXiv

  21. [29]

    Hi robot: Open-ended instruction following with hierarchical vision-language-action models

    Lucy Xiaoyang Shi, Brian Ichter, Michael Equi, Liyiming Ke, Karl Pertsch, Quan Vuong, James Tanner, Anna Walling, Haohuan Wang, Niccolo Fusai, et al. Hi robot: Open-ended instruction following with hierarchical vision-language-action models. arXiv preprint arXiv:2502.19417, 2025

    2025 arXiv

  22. [30]

    Helix: A vision-language-action model for generalist humanoid control, 2025

    Figure. Helix: A vision-language-action model for generalist humanoid control, 2025

    2025

  23. [31]

    Flow matching for generative modeling

    Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022

    2022 arXiv

  24. [32]

    Beyond optimal transport: Model-aligned coupling for flow matching

    Yexiong Lin, Yu Yao, and Tongliang Liu. Beyond optimal transport: Model-aligned coupling for flow matching. arXiv preprint arXiv:2505.23346, 2025

    2025 arXiv

  25. [33]

    Libero: Benchmarking knowledge transfer for lifelong robot learning

    Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning. arXiv preprint arXiv:2306.03310, 2023

    2023 arXiv

  26. [34]

    Evaluating real-world robot manipulation policies in simulation

    Xuanlin Li, Kyle Hsu, Jiayuan Gu, Karl Pertsch, Oier Mees, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, and Ted Xiao. Evaluating real-world robot manipulation policies in simulation. In Pr...

    2024

  27. [35]

    Cogact: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation

    Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, Yu Deng, Sicheng Xu, Yizhong Zhang, et al. Cogact: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation. arXiv preprint arXiv:2411.19650, 2024

    2024 arXiv

  28. [36]

    Futuresightdrive: Thinking visually with spatio-temporal cot for autonomous driving

    Shuang Zeng, Xinyuan Chang, Mengwei Xie, Xinran Liu, Yifan Bai, Zheng Pan, Mu Xu, and Xing Wei. Futuresightdrive: Thinking visually with spatio-temporal cot for autonomous driving. arXiv preprint arXiv:2505.17685, 2025. 20

    2025 arXiv

  29. [37]

    Tracevla: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies

    Ruijie Zheng, Yongyuan Liang, Shuaiyi Huang, Jianfeng Gao, Hal Daumé III, Andrey Kolobov, Furong Huang, and Jianwei Yang. Tracevla: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies. arXiv preprint arXiv:2412.10345, 2024

    2024 arXiv

  30. [38]

    RoboMP2: A robotic multimodal perception-planning framework with multimodal large language models

    Qi Lv, Hao Li, Xiang Deng, Rui Shao, Michael Y Wang, and Liqiang Nie. RoboMP2: A robotic multimodal perception-planning framework with multimodal large language models. In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and ...

    2024

  31. [40]

    Universal actions for enhanced embodied foundation models

    Jinliang Zheng, Jianxiong Li, Dongxiu Liu, Yinan Zheng, Zhihao Wang, Zhonghong Ou, Yu Liu, Jingjing Liu, Ya-Qin Zhang, and Xianyuan Zhan. Universal actions for enhanced embodied foundation models. arXiv preprint arXiv:2501.10105, 2025

    2025 arXiv

  32. [41]

    Learning causality-inspired representation consistency for video anomaly detection

    Yang Liu, Zhaoyang Xia, Mengyang Zhao, Donglai Wei, Yuzheng Wang, Siao Liu, Bobo Ju, Gaoyun Fang, Jing Liu, and Liang Song. Learning causality-inspired representation consistency for video anomaly detection. In Proceedings of the 31st ACM international conference on multimedia...

    2023

  33. [42]

    Pali-x: On scaling up a multilingual vision and language model

    Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, et al. Pali-x: On scaling up a multilingual vision and language model. In Proceedings of the IEEE/CVF Conference on Computer Visi...

    2024

  34. [43]

    Prismatic vlms: Investigating the design space of visually-conditioned language models

    Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar, and Dorsa Sadigh. Prismatic vlms: Investigating the design space of visually-conditioned language models. In Proceedings of the International Conference on Machine Learning (ICML), 2024

    2024

  35. [44]

    Open x-embodiment: Robotic learning datasets and rt-x models

    Open X-Embodiment Collaboration, Abby O’Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al. Open x-embodiment: Robotic learning datasets and rt-x models. In Proceedings of the I...

    2024

  36. [45]

    Reflexion: Language agents with verbal reinforcement learning

    Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36:8634–8652, 2023

    2023

  37. [46]

    Reasoning with language model is planning with world model

    Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. Reasoning with language model is planning with world model. arXiv preprint arXiv:2305.14992, 2023

    2023 arXiv

  38. [47]

    Deepseekmath: Pushing the limits of mathematical reasoning in open language models

    Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024

    2024 arXiv

  39. [48]

    Cross from left to right brain: Adaptive text dreamer for vision-and-language navigation

    Pingrui Zhang, Yifei Su, Pengyuan Wu, Dong An, Li Zhang, Zhigang Wang, Dong Wang, Yan Ding, Bin Zhao, and Xuelong Li. Cross from left to right brain: Adaptive text dreamer for vision-and-language navigation. arXiv preprint arXiv:2505.20897, 2025

    2025 arXiv

  40. [49]

    Mllmguard: A multi- dimensional safety evaluation suite for multimodal large language models

    Tianle Gu, Zeyang Zhou, Kexin Huang, Dandan Liang, Yixu Wang, Haiquan Zhao, Yuanqi Yao, Xingge Qiao, Keqing Wang, Yujiu Yang, Yan Teng, Yu Qiao, and Yingchun Wang. Mllmguard: A multi- dimensional safety evaluation suite for multimodal large language models. In A. Globerson, L....

    2024

  41. [50]

    Selfcheck: Using llms to zero-shot check their own step-by-step reasoning

    Ning Miao, Yee Whye Teh, and Tom Rainforth. Selfcheck: Using llms to zero-shot check their own step-by-step reasoning. arXiv preprint arXiv:2308.00436, 2023

    2023 arXiv

  42. [51]

    Morphmark: Flexible adaptive watermarking for large language models

    Zongqi Wang, Tianle Gu, Baoyuan Wu, and Yujiu Yang. Morphmark: Flexible adaptive watermarking for large language models. arXiv preprint arXiv:2505.11541, 2025

    2025 arXiv

  43. [52]

    Tree of thoughts: Deliberate problem solving with large language models

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. Advances in neural information processing systems, 36:11809–11822, 2023. 21

    2023

  44. [53]

    Alignbot: Aligning vlm-powered customized task planning with user reminders through fine-tuning for household robots

    Zhaxizhuoma Zhaxizhuoma, Pengan Chen, Ziniu Wu, Jiawei Sun, Dong Wang, Peng Zhou, Nieqing Cao, Yan Ding, Bin Zhao, and Xuelong Li. Alignbot: Aligning vlm-powered customized task planning with user reminders through fine-tuning for household robots. arXiv preprint arXiv:2409.11...

    2024 arXiv

  45. [54]

    Sets: Leveraging self-verification and self-correction for improved test-time scaling

    Jiefeng Chen, Jie Ren, Xinyun Chen, Chengrun Yang, Ruoxi Sun, and Sercan Ö Arık. Sets: Leveraging self-verification and self-correction for improved test-time scaling. arXiv preprint arXiv:2501.19306, 2025

    2025

  46. [55]

    Interpretable contrastive monte carlo tree search reasoning

    Zitian Gao, Boye Niu, Xuzheng He, Haotian Xu, Hongzhang Liu, Aiwei Liu, Xuming Hu, and Lijie Wen. Interpretable contrastive monte carlo tree search reasoning. arXiv preprint arXiv:2410.01707, 2024

    2024 arXiv

  47. [56]

    O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning

    Haotian Luo, Li Shen, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Naiqiang Tan, Xiaochun Cao, and Dacheng Tao. O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning. arXiv preprint arXiv:2501.12570, 2025

    2025 arXiv

  48. [57]

    Cascaded diffusion models for high fidelity image generation

    Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. arXiv preprint arXiv:2106.15282, 2021

    2021 arXiv

  49. [58]

    Revis- iting multi-agent world modeling from a diffusion-inspired perspective

    Yang Zhang, Xinran Li, Jianing Ye, Delin Qu, Shuang Qiu, Chongjie Zhang, Xiu Li, and Chenjia Bai. Revis- iting multi-agent world modeling from a diffusion-inspired perspective. arXiv preprint arXiv:2505.20922, 2025

    2025

  50. [59]

    f-dm: A multi-stage diffusion model via progressive signal transformation

    Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Miguel Angel Bautista, and Josh Susskind. f-dm: A multi-stage diffusion model via progressive signal transformation. arXiv preprint arXiv:2210.04955, 2022

    2022 arXiv

  51. [60]

    Bring metric functions into diffusion models

    Jie An, Zhengyuan Yang, Jianfeng Wang, Linjie Li, Zicheng Liu, Lijuan Wang, and Jiebo Luo. Bring metric functions into diffusion models. arXiv preprint arXiv:2401.02414, 2024

    2024 arXiv

  52. [61]

    Spectral-cascaded diffusion model for remote sensing image spectral super-resolution

    Bowen Chen, Liqin Liu, Chenyang Liu, Zhengxia Zou, and Zhenwei Shi. Spectral-cascaded diffusion model for remote sensing image spectral super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 2024

    2024

  53. [62]

    High-resolution frame interpolation with patch-based cascaded diffusion

    Junhwa Hur, Charles Herrmann, Saurabh Saxena, Janne Kontkanen, Wei-Sheng Lai, Yichang Shih, Michael Rubinstein, David J Fleet, and Deqing Sun. High-resolution frame interpolation with patch-based cascaded diffusion. In Proceedings of the AAAI Conference on Artificial Intellige...

    2025

  54. [63]

    Cascaded diffusion models for virtual try-on: Improving control and resolution

    Guangyuan Li, Yongkang Wang, Junsheng Luan, Lei Zhao, Wei Xing, Huaizhong Lin, and Binkai Ou. Cascaded diffusion models for virtual try-on: Improving control and resolution. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 4689–4697, 2025

    2025

  55. [64]

    Offline reinforcement learning: Tutorial, review, and perspectives on open problems

    Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu. Offline reinforcement learning: Tutorial, review, and perspectives on open problems. arXiv preprint arXiv:2005.01643, 2020

    2005 arXiv

  56. [65]

    Pre-training for robots: Offline rl enables learning new tasks from a handful of trials

    Aviral Kumar, Anikait Singh, Frederik Ebert, Mitsuhiko Nakamoto, Yanlai Yang, Chelsea Finn, and Sergey Levine. Pre-training for robots: Offline rl enables learning new tasks from a handful of trials. In Proceedings of Robotics: Science and Systems, Daegu, Republic of Korea, 2023

    2023

  57. [66]

    Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning

    Mitsuhiko Nakamoto, Simon Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, and Sergey Levine. Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning. Advances in Neural Information Processing Systems, 36:62244–62269, 2023

    2023

  58. [67]

    Rt-1: Robotics transformer for real-world control at scale

    Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al. Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817, 2022

    2022 arXiv

  59. [68]

    Octo: An open-source generalist robot policy

    Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Charles Xu, Jianlan Luo, Tobias Kreiman, You Liang Tan, Lawrence Yunliang Chen, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, and Sergey Levine. Octo...

    2024

  60. [69]

    Scaling proprioceptive-visual learning with het- erogeneous pre-trained transformers

    Lirui Wang, Xinlei Chen, Jialiang Zhao, and Kaiming He. Scaling proprioceptive-visual learning with het- erogeneous pre-trained transformers. In Proceedings of the Conference on Neural Information Processing System (NeurIPS), 2024

    2024

  61. [70]

    Towards generalist robot policies: What matters in building vision-language- action models

    Xinghang Li, Peiyan Li, Minghuan Liu, Dong Wang, Jirong Liu, Bingyi Kang, Xiao Ma, Tao Kong, Hanbo Zhang, and Huaping Liu. Towards generalist robot policies: What matters in building vision-language- action models. arXiv preprint arXiv:2412.14058, 2024. 22

    2024 arXiv

  62. [71]

    Spatialvla: Exploring spatial representations for visual-language-action model

    Delin Qu, Haoming Song, Qizhi Chen, Yuanqi Yao, Xinyi Ye, Yan Ding, Zhigang Wang, JiaYuan Gu, Bin Zhao, Dong Wang, et al. Spatialvla: Exploring spatial representations for visual-language-action model. arXiv preprint arXiv:2501.15830, 2025

    2025 arXiv

  63. [72]

    Diffusion policy: Visuomotor policy learning via action diffusion

    Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. In Proceedings of Robotics: Science and Systems (RSS), 2023

    2023

  64. [73]

    Fine-tuning vision-language-action models: Optimizing speed and success

    Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision-language-action models: Optimizing speed and success. arXiv preprint arXiv:2502.19645, 2025

    2025 arXiv

  65. [74]

    Improving large language model fine-tuning for solving math problems

    Yixin Liu, Avi Singh, C Daniel Freeman, John D Co-Reyes, and Peter J Liu. Improving large language model fine-tuning for solving math problems. arXiv preprint arXiv:2310.10047, 2023

    2023 arXiv

  66. [75]

    Continuous control with deep reinforcement learning

    Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015

    2015 arXiv

  67. [76]

    Addressing function approximation error in actor-critic methods

    Scott Fujimoto, Herke van Hoof, and David Meger. Addressing function approximation error in actor-critic methods. In International Conference on Machine Learning (ICML), pages 1587–1596, 2018

    2018

  68. [77]

    Soft actor-critic algorithms and applications

    Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905, 2018. 23

    2018 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.