Pith. sign in

REVIEW 3 major objections 4 minor 33 references

Towards Safe Robot Foundation Models Using Inductive Biases

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read By projecting robot foundation-model actions onto the tangent space of a constraint manifold, a modular safety layer provides formal safety guarantees without safety-specific fine-tuning.

desk verdict A useful empirical recipe for adding a safety filter to robot foundation models, but the formal guarantee is narrower than the abstract claims. read the letter →

arxiv 2505.10219 v1 pith:WN33JUDR submitted 2025-05-15 cs.RO

Maximilian Tölle , Theo Gruner , Daniel Palenicek , Tim Schneider , Jonas Günster , Joe Watson , Davide Tateo , Puze Liu
show 1 more author
Jan Peters
This is my paper · ORCID
classification cs.RO
keywords robotfoundationmodelsgeneralistpoliciessafetyATACOMconstraintmanifoldvision-language-actioninductivebiases
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 tries to establish that robot foundation models do not need to learn safety from demonstrations. A modular safety layer placed after the policy can enforce formal safety guarantees by projecting actions onto the tangent space of a constraint manifold. If this is right, generalist policies such as π0 and OCTO can be deployed in cluttered or dynamic environments without safety-specific fine-tuning, and safety holds even under disturbances. This matters because current behavior-cloned policies provide no formal safety guarantee and would need impractically large amounts of safe data to handle novel obstacles. The experiments support the claim on a quasi-static pick-and-place task and a dynamic air-hockey hitting task.

What carries the argument

The central object is the ATACOM decomposition $a_{\text{safe}} = a_{\text{drift}}(s) + a_{\text{err}}(s) + B(s)a_{\text{RFM}}$, built from constraints $0 \ge g(x)$ with $g \in C^1$. The constraint manifold is the set where all constraints are active; the tangent-space basis $B(s)$ maps the foundation model's action into a direction that keeps the robot on that manifold, the drift term compensates the change of $g$ from the system's affine dynamics, and the error term reactively returns the state to the safe set after disturbances. This decomposition is what lets the paper claim forward invariance and input-to-state stability while leaving the policy weights untouched.

What would settle it

Run the pick-and-place setup with a distractor that the instance-segmentation-based bounding-box generator does not segment, and check whether the robot arm collides with it; if it does, the guarantee applies only to the declared constraints. A second check is to shift a detected bounding box a few centimeters from the true obstacle and observe whether the arm enters the real object, showing that the formal guarantee assumes exact constraint evaluation.

Watch

Extended reading notes

Core claim

The paper's central claim is that coupling any robot foundation model with ATACOM, a safety layer that acts on the tangent space of a known constraint manifold, converts a purely behavior-cloned policy into one that is safe by construction. The layer takes the policy's action $a_{\text{RFM}}$ and outputs $a_{\text{safe}} = a_{\text{drift}}(s) + a_{\text{err}}(s) + B(s)a_{\text{RFM}}$, where the drift term compensates the system's own dynamics, the error term corrects constraint violations under disturbances, and $B(s)$ morphs the action onto the tangent space. Under mild assumptions this yields forward invariance of the safe set and input-to-state stability, so safety is guaranteed rather than learned. The experiments with π0 on Franka pick-and-place and OCTO on air hockey show constraint violations drop to zero without a meaningful loss in success rate, and the safety layer runs at a higher frequency than the policy itself.

Load-bearing premise

Safety constraints must be known in advance, written down as smooth functions, and perceived correctly; if a hazard is missing from those constraints, the guarantee protects only the modeled safe set, not the actual danger.

Editorial extensions

If this is right

  • A generalist policy that has never been fine-tuned for safety can be deployed with formal guarantees, as long as its actions are filtered by the tangent-space projection.
  • Safety enforcement can run at 50–60 Hz even when the foundation policy acts at 12.5–15 Hz, so the layer does not add latency to the policy's decisions.
  • Across both studied tasks, the safety layer keeps task success rates comparable to the unfiltered policy while eliminating constraint violations.
  • Because the layer requires no safety demonstrations, expert data collection can remain safe; the existing demonstration data only needs to teach task competence.

Reading between the lines

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

  • The paper leaves implicit that safety data become unnecessary in principle: any future RFM that outputs actions for a control-affine system could be wrapped with the same filter, so safe deployment stops scaling with the policy's dataset.
  • A testable extension is to replace the hand-drawn bounding boxes with open-vocabulary grounding so the filter can handle new object classes at deployment; the paper's formal guarantee would then depend on perception accuracy, which it does not quantify.
  • The architecture suggests a broader trade-off: safety as an architectural projection may be more data-efficient than safety as a training objective, but the cost is that every hazard must be declared explicitly as a constraint.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. This paper proposes a modular safety layer based on ATACOM that is placed after an arbitrary robot foundation model (RFM) to enforce known safety constraints. The safety layer projects actions onto the tangent space of a constraint manifold, providing forward invariance and input-to-state stability guarantees inherited from prior work [6]. The authors evaluate the approach with two RFMs: π0 on a Franka pick-and-place task with visual bounding-box constraints, and OCTO on a dynamic air hockey hitting task. Experiments show that the safety filter reduces constraint violations while maintaining task success. The paper also describes a semi-automated visual constraint generation pipeline using SAM 2, though bounding boxes are hand-drawn in the experiments.

Significance. If the formal guarantees are valid and the implementation matches the theory, this is a meaningful contribution to safe deployment of robot foundation models: it offers a way to obtain safety without safety-specific fine-tuning or large safe datasets. The paper demonstrates flexibility by integrating with two different foundation models and two task types, including a real-world air hockey deployment. Strengths include the modular architecture, the explicit statement of assumptions, and the empirical reduction of collisions. However, the formal guarantee is imported from prior work and is not re-established for the specific implemented constraints, which is the main limitation addressed below.

major comments (3)
  1. [Section 2.1 and Appendix A.2] The claimed formal safety guarantee applies to forward invariance of the constraint manifold defined by g(x), but the implemented collision-avoidance constraints use a finite set of spheres that "cover the robot's hull at critical areas" without stating or proving that the union of these spheres is a superset of the robot's physical volume. If any link or segment protrudes outside the spheres, the safety layer can declare a state safe while the physical robot is in contact with an obstacle. The manuscript does not provide the sphere parameters or a coverage check, so the experimental demonstrations do not establish the stated formal guarantee for physical collision avoidance.
  2. [Section 2.2, Eq. (2), and Appendix A.2, Eq. (5)] Assumption 2 in Section 2 requires the constraint functions g(x) to be C1. The bounding-box constraint g(x) = -||α bbx - p||, with p = clip(...), is not continuously differentiable on the boundaries of the clipping operation and where the argument of the norm is zero. The manuscript does not explain how ATACOM handles this non-smoothness or why the differentiability assumption can be relaxed. As the forward-invariance guarantee in [6] relies on differentiability of the constraints, this gap undermines the formal claim for the visual constraints.
  3. [Section 2.1] The abstract and Section 2.1 assert formal safety guarantees for generalist policies, but the relevant theorem from [6] is never stated and its hypotheses are not verified for the concrete constraints (Eqs. 3-6 and 7-14). In particular, the regularity of the constraint manifold and the consistency of Assumption 1 with the actual robot dynamics are taken for granted. The authors should state the theorem and confirm that each implemented constraint satisfies its conditions.
minor comments (4)
  1. [Section 2.2] The sentence 'We draw the bounding boxes ourselves to obtain reliable bounding boxes for every evaluation run' contradicts the earlier description of an automatic constraint generation pipeline; if the boxes are manually drawn, the 'semi-automated' claim should be clarified.
  2. [Section 3.1, Figure 3] The empirical results are reported without the number of evaluation runs, error bars, or statistical tests; for example, Figure 3 does not specify the number of trajectories used for the success-rate and safety-rate histograms. The authors should report these details.
  3. [Section 2.2, Eq. (2)] Equation (2) uses mismatched norm symbols ('∥' and 'p∥') and the notation pi vs p is inconsistent with Appendix A.2's pj; this should be corrected.
  4. [Section B.3] The real-world air hockey deployment is described only qualitatively ('reasonable performance'); the manuscript would benefit from quantitative metrics, such as number of episodes, violation rate, and success rate, for the real-world experiments.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the formal safety guarantees are inherited from an independently stated theorem with explicit assumptions, and the experimental evaluation measures task success and constraint satisfaction rather than fitting the claimed result.

full rationale

The paper's derivation chain is modular rather than circular. It adopts the ATACOM safety layer from prior work [5,6], states the assumptions needed for the guarantee (Assumption 1: control-affine system; Assumption 2: known analytic C1 constraints), and then empirically evaluates the combined system with two foundation policies, π0 and OCTO. The cited ATACOM theorem is parameter-free, is stated under assumptions that do not include the paper's empirical results, and is used as a building block rather than as a conclusion derived from the present experiments; therefore the author overlap in the citation does not make the argument circular. The constraint functions are constructed from robot spheres, bounding boxes, workspace limits, and joint limits, and the paper explicitly acknowledges in the Limitations section that safety constraints must be known in advance. The empirical safety metrics measure satisfaction of exactly the constraints the layer enforces, which is a direct verification of the filter's deterministic behavior rather than a fitted prediction. Success rates, execution times, and task-completion measurements are external to the safety-layer construction and are not forced by any fitted parameter. Skeptical concerns about sphere coverage of the full robot volume, non-smoothness of the bounding-box constraint, and perception errors are validity or robustness gaps, not circularity, and they are at least partly acknowledged by the stated assumptions and limitations. No equation in the paper reduces to a fitted input, no prediction is renamed from a fit, and no uniqueness claim is imported to force the choice of ATACOM. Overall, the central claim is honestly conditional on the stated assumptions and is not equivalent to its inputs by construction.

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

The paper's contribution rests on two explicit assumptions (control-affine system, known analytic constraints) plus the prior ATACOM theory. Hand-tuned parameters and manual bounding boxes are the price paid for the formal guarantee; no new entities are introduced.

free parameters (5)
  • Robot hull covering spheres (positions and radii) = hand-tuned, not reported
    Spheres approximate the robot body for distance constraints; radii and placements are chosen by the authors (Section 2.2, Figure 2) and affect how much free space the robot keeps.
  • ATACOM slack beta = 10 (Franka), 2 (air hockey)
    Controls slack function steepness in the safety filter (Tables 2 and 3); chosen per environment.
  • ATACOM slack tolerance = 0.001 (Franka), 1e-6 (air hockey)
    Tolerance for constraint satisfaction in slack-based formulation (Tables 2 and 3); hand-picked.
  • Control frequencies for RFM and ATACOM layer = 15/60 Hz (Franka), 12.5/50 Hz (air hockey)
    RFM action rate and safety-layer update rate are hand-selected (Tables 2 and 3); they affect stability and reactivity.
  • Bounding box annotations = manual per evaluation run
    The abstract calls the pipeline semi-automated, but Section 2.2 says 'We draw the bounding boxes ourselves'; these manual annotations determine the constraints.
assumptions (5)
  • domain assumption System is control-affine with full state available: x_dot = f(x) + G(x)u (Assumption 1)
    Stated in Section 2; this form is needed for the ATACOM tangent-space construction and drift/error compensation.
  • domain assumption Safety constraints are known analytically and C1: 0 >= g(x), g in C1 (Assumption 2)
    Stated in Section 2; the paper explicitly acknowledges this is a strong assumption and leaves learning constraints to future work.
  • domain assumption ATACOM forward-invariance and ISS guarantees from Liu et al. [6] apply under the given conditions
    The paper cites [6] for formal guarantees and does not re-derive them; this is prior work by overlapping authors.
  • domain assumption Perception pipeline yields accurate 3D object bounds from SAM 2 segmentation and depth
    In Section 2.2, constraints are built from bounding boxes; errors in segmentation or camera calibration would invalidate the collision-avoidance constraint.
  • domain assumption Robot kinematic model matches the real robot well enough for distance constraints
    Constraints are computed in the kinematic model; the ISS guarantee in [6] only covers bounded disturbances, so unbounded model error would break the guarantee.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Towards Safe Robot Foundation Models Using Inductive Biases." pith.science (2026). https://pith.science/paper/WN33JUDR

@misc{pith2026250510219,
  author       = {Pith},
  title        = {Pith review of: Towards Safe Robot Foundation Models Using Inductive Biases},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WN33JUDR}},
  note         = {Machine review of arXiv:2505.10219}
}
read the original abstract

Safety is a critical requirement for the real-world deployment of robotic systems. Unfortunately, while current robot foundation models show promising generalization capabilities across a wide variety of tasks, they fail to address safety, an important aspect for ensuring long-term operation. Current robot foundation models assume that safe behavior should emerge by learning from a sufficiently large dataset of demonstrations. However, this approach has two clear major drawbacks. Firstly, there are no formal safety guarantees for a behavior cloning policy trained using supervised learning. Secondly, without explicit knowledge of any safety constraints, the policy may require an unreasonable number of additional demonstrations to even approximate the desired constrained behavior. To solve these key issues, we show how we can instead combine robot foundation models with geometric inductive biases using ATACOM, a safety layer placed after the foundation policy that ensures safe state transitions by enforcing action constraints. With this approach, we can ensure formal safety guarantees for generalist policies without providing extensive demonstrations of safe behavior, and without requiring any specific fine-tuning for safety. Our experiments show that our approach can be beneficial both for classical manipulation tasks, where we avoid unwanted collisions with irrelevant objects, and for dynamic tasks, such as the robot air hockey environment, where we can generate fast trajectories respecting complex tasks and joint space constraints.

Figures

Figures reproduced from arXiv: 2505.10219 by the authors.

Figure 1
Figure 1. Our proposed safety layer can be added to the output of an arbitrary [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (left) Spheres cover the robot’s hull at critical areas to formulate distance-based constraints [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Results in the manipulation tasks. Dashed histograms indicate the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Video frame extracts from a rollout on three different tasks. Difficulty is dictated by the number of obstacles in the scene. The results for all of these experiments are pre￾sented in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Safety violations of the OCTO policy w/o the safety module on the air hockey hitting task for different checkpoints during the training phase. We report the maximum constraint violation and the success rate of the robot hitting the puck into the goal over 500 episodes …

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

33 extracted references · 24 canonical work pages

  1. [6]

    P. Liu, H. Bou-Ammar, J. Peters, and D. Tateo. Safe reinforcement learning on the constraint manifold: Theory and applications. arXiv preprint arXiv:2404.09080, 2024

    arXiv 2024

  2. [1]

    Open X-Embodiment: Robotic learning datasets and RT- X models

    Open X-Embodiment Collaboration. Open X-Embodiment: Robotic learning datasets and RT- X models. In International Conference on Robotics and Automation (ICRA), 2024

    work page 2024

  3. [2]

    M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. Fos- ter, G. Lam, P. Sanketi, Q. Vuong, T. Kollar, B. Burchfiel, R. Tedrake, D. Sadigh, S. Levine, P. Liang, and C. Finn. OpenVLA: An Open-Source Vision-Language-Action Model. In Con- ference on Robot Learning (CoRL), 2024

    work page 2024

  4. [3]

    Ghosh, H

    Octo Model Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, C. Xu, J. Luo, T. Kreiman, Y . Tan, L. Y . Chen, P. Sanketi, Q. Vuong, T. Xiao, D. Sadigh, C. Finn, and S. Levine. Octo: An open-source generalist robot policy. In Robotics: Science and Systems, 2024

    work page 2024

  5. [4]

    Black, N

    K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Haus- man, B. Ichter, S. Jakubczak, T. Jones, L. Ke, S. Levine, A. Li-Bell, M. Mothukuri, S. Nair, K. Pertsch, L. X. Shi, J. Tanner, Q. Vuong, A. Walling, H. Wang, and U. Zhilinsky. π0: A vision-language-action flow model for general robot control, 2024

    work page 2024

  6. [5]

    P. Liu, D. Tateo, H. B. Ammar, and J. Peters. Robot reinforcement learning on the constraint manifold. In 5th Conference on Robot Learning (CoRL). PMLR, 2021

    work page 2021

  7. [7]

    N. Ravi, V . Gabeur, Y .-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R ¨adle, C. Rolland, L. Gustafson, E. Mintun, J. Pan, K. V . Alwala, N. Carion, C.-Y . Wu, R. Girshick, P. Dol- lar, and C. Feichtenhofer. SAM 2: Segment anything in images and videos. In The Thirteenth International Conference on Learning Representations, 2025

    work page 2025

  8. [8]

    Zhang, Y

    B. Zhang, Y . Zhang, J. Ji, Y . Lei, J. Dai, Y . Chen, and Y . Yang. SafeVLA: Towards safety alignment of vision-language-action model via safe reinforcement learning. arXiv preprint arXiv:2503.03480, 2025

    arXiv 2025

Show all 33 references
  1. [9]

    Taylor, A

    A. Taylor, A. Singletary, Y . Yue, and A. Ames. Learning for safety-critical control with control barrier functions. In Learning for Dynamics and Control, pages 708–717. PMLR, 2020. 9

    2020

  2. [10]

    D. C. Tan, F. Acero, R. McCarthy, D. Kanoulas, and Z. A. Li. Your value function is a control barrier function: Verification of learned policies using control theory. arXiv preprint arXiv:2306.04026, 2023

    2023 arXiv

  3. [11]

    Y . Yang, Y . Jiang, Y . Liu, J. Chen, and S. E. Li. Model-free safe reinforcement learning through neural barrier certificate. IEEE Robotics and Automation Letters, 8(3):1295–1302, 2023

    2023

  4. [12]

    Q. Yang, T. D. Sim ˜ao, S. H. Tindemans, and M. T. Spaan. Wcsac: Worst-case soft actor critic for safety-constrained reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35 (12), pages 10639–10646, 2021

    2021

  5. [13]

    G ¨unster, P

    J. G ¨unster, P. Liu, J. Peters, and D. Tateo. Handling long-term safety and uncertainty in safe reinforcement learning. In Conference on Robot Learning (CoRL), 2024

    2024

  6. [14]

    P. Liu, K. Zhang, D. Tateo, S. Jauhri, J. Peters, and G. Chalvatzaki. Regularized deep signed distance fields for reactive motion generation. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6673–6680. IEEE, 2022

    2022

  7. [15]

    P. Liu, K. Zhang, D. Tateo, S. Jauhri, Z. Hu, J. Peters, and G. Chalvatzaki. Safe reinforcement learning of dynamic high-dimensional robotic tasks: navigation, manipulation, interaction. In 2023 IEEE International Conference on Robotics and Automation (ICRA) , pages 9449–9456....

    2023

  8. [16]

    S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In European Conference on Computer Vision (ECCV), 2024

    2024

  9. [17]

    T. Ren, Q. Jiang, S. Liu, Z. Zeng, W. Liu, H. Gao, H. Huang, Z. Ma, X. Jiang, Y . Chen, Y . Xiong, H. Zhang, F. Li, P. Tang, K. Yu, and L. Zhang. Grounding dino 1.5: Advance the ”edge” of open-set object detection, 2024

    2024

  10. [18]

    Khazatsky, K

    A. Khazatsky, K. Pertsch, S. Nair, A. Balakrishna, S. Dasari, S. Karamcheti, S. Nasiriany, M. K. Srirama, L. Y . Chen, K. Ellis, P. D. Fagan, J. Hejna, M. Itkina, M. Lepert, Y . J. Ma, P. T. Miller, J. Wu, S. Belkhale, S. Dass, H. Ha, A. Jain, A. Lee, Y . Lee, M. Memmel, S. Pa...

    2024

  11. [19]

    P. Liu, J. G ¨unster, N. Funk, S. Gr ¨oger, D. Chen, H. Bou-Ammar, J. Jankowski, A. Mari ´c, S. Calinon, A. Orsula, M. Olivares-Mendez, H. Zhou, R. Lioutikov, G. Neumann, A. Likmeta, A. Zhalehmehrabi, T. Bonenfant, M. Restelli, D. Tateo, Z. Liu, and J. Peters. A retrospective ...

    2024

  12. [20]

    Todorov, T

    E. Todorov, T. Erez, and Y . Tassa. Mujoco: A physics engine for model-based control. In International Conference on Intelligent Robots and Systems, 2012

    2012

  13. [21]

    Firoozi, J

    R. Firoozi, J. Tucker, S. Tian, A. Majumdar, J. Sun, W. Liu, Y . Zhu, S. Song, A. Kapoor, K. Hausman, et al. Foundation models in robotics: Applications, challenges, and the future. The International Journal of Robotics Research, 2023. 10

    2023

  14. [22]

    Majumdar, M

    A. Majumdar, M. Sharma, D. Kalashnikov, S. Singh, P. Sermanet, and V . Sindhwani. Predictive red teaming: Breaking policies without breaking robots. arXiv preprint arXiv:2502.06575 , 2025

    2025 arXiv

  15. [23]

    A. J. Hancock, A. Z. Ren, and A. Majumdar. Run-time observation interventions make vision- language-action models more visually robust. arXiv preprint arXiv:2410.01971, 2024

    2024 arXiv

  16. [24]

    Ravichandran, A

    Z. Ravichandran, A. Robey, V . Kumar, G. J. Pappas, and H. Hassani. Safety guardrails for LLM-Enabled robots. arXiv preprint arXiv:2503.07885, 2025

    2025

  17. [25]

    Brunke, Y

    L. Brunke, Y . Zhang, R. R¨omer, J. Naimer, N. Staykov, S. Zhou, and A. P. Schoellig. Semanti- cally safe robot manipulation: From semantic scene understanding to motion safeguards.IEEE Robotics and Automation Letters, 2025

    2025

  18. [26]

    Y . Ling, K. Owalekar, O. Adesanya, E. Bıyık, and D. Seita. IMPACT: Intelligent motion planning with acceptable contact trajectories via vision-language models. arXiv preprint arXiv:2503.10110, 2025

    2025

  19. [27]

    Santos, Z

    L. Santos, Z. Li, L. Peters, S. Bansal, and A. Bajcsy. Updating robot safety representations online from natural language feedback, 2024

    2024

  20. [28]

    Liang, W

    J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng. Code as policies: Language model programs for embodied control. In IEEE International Conference on Robotics and Automation (ICRA), 2023

    2023

  21. [29]

    Calli, A

    B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar. The ycb object and model set: Towards common benchmarks for manipulation research. In International Conference on Advanced Robotics (ICAR), pages 510–517, 2015

    2015

  22. [30]

    Y . Lin, A. S. Wang, G. Sutanto, A. Rai, and F. Meier. Polymetis. https:// facebookresearch.github.io/fairo/polymetis/, 2021

    2021

  23. [31]

    Schneider

    T. Schneider. Franky: High-level control library for franka robots with python and c++ support,

  24. [32]

    Cadene, S

    R. Cadene, S. Alibert, A. Soare, Q. Gallouedec, A. Zouitine, and T. Wolf. Lerobot: State- of-the-art machine learning for real-world robotics in pytorch. https://github.com/ huggingface/lerobot, 2024. 11 A Experimental Details: Pick-and-Place Tasks with Franka Robot We perform...

    2024

  25. [2025]

    URL https://github.com/TimSchneider42/franky

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.