Pith. sign in

REVIEW 4 major objections 4 minor 46 references

H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies

T0 review · 4 major / 4 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 Wrist forces alone can guide a humanoid robot to carry loads alongside a human partner.

desk verdict A real humanoid co-manipulation demo with a clean hierarchical design, but the 'on par' claim outruns the evidence and the payload envelope mismatch needs addressing. read the letter →

arxiv 2505.17627 v1 pith:M6J4LB2M submitted 2025-05-23 cs.RO

Geeta Chandra Raju Bethala , Hao Huang , Niraj Pudasaini , Abdullah Mohamed Ali , Shuaihang Yuan , Congcong Wen , Anthony Tzes , Yi Fang This is my paper
classification cs.RO
keywords human-robotco-manipulationhapticintentinferencehumanoidlocomotionreinforcementlearningdiffusionpolicysim-to-realtransferpayloadadaptationforce/torquesensing
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

This paper aims to show that a humanoid robot can act as a cooperative load-carrying partner using only haptic cues, namely six-axis force and torque readings from its wrists, to infer where the human wants to go. The authors build a two-tier learning system: a behavior-cloning network turns wrist forces into whole-body velocity commands, and a reinforcement-learned walking policy turns those commands into stable joint motions under variable payloads. Training data come from human–human dyads captured with ordinary RGB video and force sensors, avoiding the need for motion-capture infrastructure. In real trials, the robot followed a human leader carrying a box with trajectory deviation, velocity synchrony, and follower force comparable to a blindfolded human follower, though slower due to the robot's speed limit. If correct, this is the first demonstration of learned haptic guidance fused with full-body legged control for fluid human–humanoid co-manipulation.

What carries the argument

The load-bearing mechanism is the decoupling of intent interpretation from locomotion control. The upper tier is a compact haptic intent model: multi-resolution stationary wavelet coefficients of the six-axis force/torque streams are fed, through learned keys and values, into a multiscale conditional diffusion policy whose Transformer encoder attends across wavelet levels, and deterministic DDIM sampling produces future velocity sequences; the first horizon token is the reference command. The lower tier is a PPO-trained asymmetric actor-critic locomotion policy that takes the command, joint states, and gait phase and outputs target joint angles, with random per-wrist forces applied during training to force load adaptation. The decoupling means the same velocity-command interface could, in principle, be reused with any walking controller.

What would settle it

A controlled trial in which a 5 kg box is held so that one wrist bears the entire load, pushing per-wrist vertical force above the 15 N training range, and the robot is expected to follow the leader; if it falls or fails to track, the claim of zero-shot load-adaptive co-manipulation is falsified.

Watch

Extended reading notes

Core claim

The central claim is that a hierarchical policy can make a legged humanoid cooperate with a human at a physical task using force feedback as the sole communication channel. At the upper level, a diffusion-based behavior-cloning model maps the last fraction of a second of dual-wrist force/torque data to planar linear velocity and yaw rate commands, effectively translating the leader's pushes and pulls into motion intentions. At the lower level, a PPO-trained locomotion policy, exposed during training to randomized downward and upward wrist forces up to 15 newtons plus varied friction and mass, converts those commands into joint angles that keep the robot balanced while carrying a load. The authors report that on a real humanoid, this zero-shot deployed pipeline matches a blindfolded human follower on trajectory deviation (0.129 m vs 0.111 m), velocity difference (0.143 m/s vs 0.165 m/s), and follower force (16.23 N vs 17.36 N), while taking longer to finish because of the robot's 0.8 m/s speed cap. The paper's own framing is that this is the first successful fusion of learned haptic intent inference with whole-body legged locomotion for human–humanoid co-manipulation.

Load-bearing premise

The locomotion policy was trained with random per-wrist forces only up to 15 newtons (roughly 3 kilograms of payload total), while the real-world trials carried boxes weighing up to 5 kilograms, so the reported zero-shot success presupposes the policy generalizes to forces it never experienced in training.

Editorial extensions

If this is right

  • Humanoid robots could serve as load-carrying assistants in homes or warehouses guided only by a human's physical lead, with no voice, vision, or joystick commands.
  • Because the intent model outputs platform-agnostic velocity commands, the haptic inference tier could transfer to other legged or wheeled platforms equipped with the same wrist sensors.
  • The vision-only training data pipeline removes the need for motion-capture studios when collecting human–human demonstration data for co-manipulation.
  • Zero-shot deployment from simulation to the real robot works for payloads and speeds within the trained envelope, as shown by stable tracking and forces close to the human–human baseline.
  • The completion-time gap is attributed to the robot's speed cap rather than haptic miscommunication, so faster humanoids should close that gap without changing the method.

Reading between the lines

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

  • The same force-to-velocity mapping could be trained for other physical collaboration tasks, such as pushing a cart or guiding an upper-body assist, since it never sees the robot's own dynamics and only the low-level policy would need retraining.
  • The real trials carried payloads up to 5 kg while training randomized only about 3 kg total across the two wrists, so a direct test at the edge of the payload envelope would clarify whether the zero-shot success reflects true extrapolation or a favorable load distribution.
  • The inverse mapping, where the robot signals intent to a human through the same wrist sensors, is a natural next direction not addressed by the paper, and the hierarchical design suggests it could be implemented by reversing the direction of the force-to-velocity supervision.
  • The eight motion primitives used for data collection are planar; testing on slopes or stairs would determine whether the intent inference generalizes beyond flat-ground co-manipulation.
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

4 major / 4 minor

Summary. The paper proposes H2-COMPACT, a hierarchical framework for human-humanoid co-manipulation. A diffusion-based behavior-cloning network maps six-axis force/torque signals from dual wrist sensors into planar velocity and yaw-rate commands, and a PPO-trained locomotion policy maps those high-level twists to leg joint targets. The locomotion policy is trained in Isaac Gym with randomized payloads, friction, and external forces, then validated in MuJoCo and deployed zero-shot on a Unitree G1. Training data for the intent model come from dyadic human-human trials collected with only RGB video and F/T streams, processed by SAM2 and WHAM. The paper reports real-world human-humanoid trials with payloads from 0 to 5 kg and compares four metrics (completion time, trajectory deviation, velocity difference, follower force) against a blindfolded human-human baseline, claiming on-par or better performance and the first demonstration of learned haptic guidance fused with full-body legged control.

Significance. If the claims hold, the paper is a meaningful advance: the decoupling of haptic intent inference from whole-body locomotion is a sensible and reusable architecture, the vision-only data-collection pipeline is practical, the real-robot deployment is a genuine effort, and the public release of code and videos supports reproducibility. The contribution would be the first integration of learned wrist F/T-based intent inference with dynamic legged humanoid control for cooperative carrying. However, the central 'on par' and 'load-adaptive over 0-5 kg' claims are currently stronger than the evidence, for reasons detailed in the major comments.

major comments (4)
  1. [§III-C2, Eq. (16); §IV-C] The force-randomization envelope used to train the low-level policy does not cover the payload range on which the load-adaptive claim is evaluated. Equation (16) samples a per-wrist z-force from U([-Fmax, Fmax]) with Fmax = 15 N; the accompanying text says the lower bound is -3 N, which contradicts the symmetric distribution written in the equation, and in either reading the downward force is capped at 15 N per wrist, i.e. at most about 3 kg total when both wrists saturate. Section IV-C reports real trials with payloads varying from 0 to 5 kg, which would require roughly 24.5 N per wrist under an even load split and more under uneven loading. The paper reports no measured wrist F/T data from the real deployment and no per-payload performance breakdown, so it is not possible to verify whether the robot experienced forces above the trained envelope or whether the human partner carried the excess load. This is an internal inconsistency between the training distribution and the evaluation range, not merely a stylistic issue; either the training distribution must be extended to cover 5 kg, or the real trials must be accompanied by wrist-force logs and per-payload results to support the load-adaptive claim.
  2. [§V, Table I] Table I reports only means for each metric, with no standard deviations, confidence intervals, significance tests, or per-trial data, despite the experimental section describing multiple participants and repetitions. The human-human and human-humanoid means for trajectory deviation, velocity difference, and follower force are close (0.1109 vs 0.1294 m, 0.165 vs 0.143 m/s, and 17.355 vs 16.230 N), but without dispersion or a statistical test it is not possible to conclude that these differences are meaningful or that the robot performs 'on par.' The paper should report per-trial distributions and a statistical comparison, or explicitly weaken the on-par claim to a qualitative observation.
  3. [§V-A, Table I] Completion time is not on par by the paper's own reported means: the human-humanoid dyad took 51.47 s versus 23.78 s for the human-human dyad, a factor of 2.16. The authors attribute this to the G1's 0.8 m/s speed cap, which is plausible, but completion time is still one of the four headline metrics listed in Table I and referenced in the abstract. The paper should either report a speed-cap-normalized completion time, exclude completion time from the on-par statement, or revise the claim to state explicitly that the humanoid is slower while matching the other metrics.
  4. [§V-B, §IV-C] The simulation experiment intended to demonstrate load adaptation uses a single constant 30 N payload in MuJoCo and does not include a real-hardware comparison between the baseline and the load-adaptive policy. Section V-B shows that the baseline policy falls under a 30 N payload in simulation, but the real-world evaluation does not isolate the load-adaptive component: there is no ablation on the physical robot and no per-payload stability or tracking metrics. As a result, the real-world evidence supports the combined hierarchical system but does not specifically verify the claim that the low-level policy is load-adaptive over the full 0-5 kg range tested in Section IV-C.
minor comments (4)
  1. [§III-B2, Eq. (4)] The noise term in the forward diffusion is written as epsilon ~ N(0, I4), but y is a velocity sequence in R^{H x 3}; the identity matrix dimension should be consistent with the flattened velocity dimension (e.g., I_{18} for H = 6).
  2. [§III-B2, Eq. (6)] The notation A_iell is used without defining i or the dimension of the attention matrix, and the word 'matrics' should be 'matrices.'
  3. [§III-A] There is a typo in the sentence describing the decoupling: 'orce to high-level whole-body velocities' should read 'force to high-level whole-body velocities.'
  4. [References] References [7] and [28] are the same PPO paper and should be consolidated; this duplication also appears in the related-work citation style.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the intent-inference and locomotion stages are trained on independent empirical labels and evaluated with external metrics.

full rationale

The paper's derivation chain is self-contained and non-circular. The upper-tier intent model is a behavior-cloning regressor trained on measured human-human pairs of six-axis force/torque readings and follower velocities; the output mapping is an empirical function learned from data, not a quantity reinserted from the evaluation metrics. The lower-tier locomotion policy is trained with PPO under randomized external wrist forces and commanded velocity references, and the reported metrics (completion time, trajectory deviation, velocity difference, average follower force) are computed from independent Vicon motion capture and ATI sensor readings, not from the training rewards. The reward components in Table IV are training objectives and are not fitted to the evaluation metrics. No load-bearing conclusion rests on a self-citation: references [3] and [46] are incidental, while the core method builds on external baselines such as PPO, Diffusion Policy, Humanoid-Gym, Isaac Gym, MuJoCo, WHAM, and SAM2. The discrepancy between the 15 N per-wrist training force envelope and the 0-5 kg real payload range is a potential simulation-to-real generalization concern and an internal consistency issue, but it is not an instance of the paper's predictions reducing to its inputs by construction. Therefore the circularity score is 0.

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

No new physical entities are posited. The free parameters are the hand-chosen force randomization range, the reward scale factors, and the diffusion model hyperparameters. The axioms are the simulator-transfer assumption, the sufficiency of human-human force data for intent training, the reliability of SAM2/WHAM pose labels, and the validity of the Unitree base framework.

free parameters (3)
  • per_wrist_z_force_range = U([-3, 15]) N
    Hand-chosen in Section III-C2 to emulate nominal payload capacity, but it does not cover the 5 kg real-world payloads used in Section IV-C.
  • reward_scale_factors = see Table IV (e.g., -2.0, -0.05, -1.0, -10.0, 1.0, 0.5, -2.5e-7, -1e-3, -0.01, -5.0, -0.2, -20.0, 0.15, -1.0)
    Hand-tuned reward weights in the PPO objective that shape the learned locomotion policy; no principled derivation is provided.
  • diffusion_hyperparameters = H=6, S=33, lambda_KL=1e-2, K=20, lr=1e-3, batch_size=32
    Set by the authors; no hyperparameter sensitivity analysis is presented.
assumptions (4)
  • domain assumption Isaac Gym simulation with randomized forces is a faithful substitute for real-world payload interaction for zero-shot sim2real transfer.
    The locomotion policy is trained exclusively in Isaac Gym (Section III-C2) and deployed without fine-tuning; this assumption underlies the sim2real claim.
  • domain assumption Force/torque signals recorded from blindfolded human-human dyads contain sufficient information to infer a leader's movement intent for a robot partner.
    The intent inference model is trained on human-human demonstration data (Section IV-B) and then used to drive the robot.
  • domain assumption SAM2 and WHAM provide accurate enough 3D human pose and velocity labels from a single fixed RGB camera for supervised training.
    The training labels for the velocity output come from WHAM pose estimates plus finite differences (Section III-D).
  • domain assumption The Unitree official RL framework, following Humanoid-Gym [23], provides a valid base locomotion prior for load-adaptive training.
    The policy is built on Unitree's official framework (Section III-C2) and its stability is not independently benchmarked against other locomotion priors.

how reviews work

0 comments
Cite this review

Pith. "Pith review of H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies." pith.science (2026). https://pith.science/paper/M6J4LB2M

@misc{pith2026250517627,
  author       = {Pith},
  title        = {Pith review of: H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M6J4LB2M}},
  note         = {Machine review of arXiv:2505.17627}
}
read the original abstract

We present a hierarchical policy-learning framework that enables a legged humanoid to cooperatively carry extended loads with a human partner using only haptic cues for intent inference. At the upper tier, a lightweight behavior-cloning network consumes six-axis force/torque streams from dual wrist-mounted sensors and outputs whole-body planar velocity commands that capture the leader's applied forces. At the lower tier, a deep-reinforcement-learning policy, trained under randomized payloads (0-3 kg) and friction conditions in Isaac Gym and validated in MuJoCo and on a real Unitree G1, maps these high-level twists to stable, under-load joint trajectories. By decoupling intent interpretation (force -> velocity) from legged locomotion (velocity -> joints), our method combines intuitive responsiveness to human inputs with robust, load-adaptive walking. We collect training data without motion-capture or markers, only synchronized RGB video and F/T readings, employing SAM2 and WHAM to extract 3D human pose and velocity. In real-world trials, our humanoid achieves cooperative carry-and-move performance (completion time, trajectory deviation, velocity synchrony, and follower-force) on par with a blindfolded human-follower baseline. This work is the first to demonstrate learned haptic guidance fused with full-body legged control for fluid human-humanoid co-manipulation. Code and videos are available on the H2-COMPACT website.

Figures

Figures reproduced from arXiv: 2505.17627 by the authors.

Figure 1
Figure 1. Real-world human–humanoid co-manipulation. The human leads the humanoid robot—unaware of the route or [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. H²-COMPACT’s pipeline: raw force/torque and RGB inputs are cleaned by SAM2 and WHAM, then passed through [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview of experimental hardware. From left to right: [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The eight motion primitives executed during dyadic data collection [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Position tracking of the humanoid and human follow [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Velocity error tracking between the leader and the [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Sim2Sim transfer of the baseline and load-adaptive [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

46 extracted references · 34 canonical work pages

  1. [1]

    Omnih2o: Universal and dexterous human- to-humanoid whole-body teleoperation and learning,

    T. He, Z. Luo, X. He, W. Xiao, C. Zhang, W. Zhang, K. Kitani, C. Liu, and G. Shi, “Omnih2o: Universal and dexterous human- to-humanoid whole-body teleoperation and learning,” arXiv preprint arXiv:2406.08858, 2024

    arXiv 2024

  2. [2]

    Learning humanoid locomotion with perceptive internal model,

    J. Long, J. Ren, M. Shi, Z. Wang, T. Huang, P. Luo, and J. Pang, “Learning humanoid locomotion with perceptive internal model,” 2024

    work page 2024

  3. [3]

    Embodied chain of action reasoning with multi-modal foundation model for humanoid loco- manipulation,

    Y . Hao, G. C. R. Bethala, N. Pudasaini, H. Huang, S. Yuan, C. Wen, B. Huang, A. Nguyen, and Y . Fang, “Embodied chain of action reasoning with multi-modal foundation model for humanoid loco- manipulation,” arXiv preprint arXiv:2504.09532 , 2025

    arXiv 2025

  4. [4]

    Constraint-aware intent estimation for dynamic human-robot object co-manipulation,

    Y . Shao, T. Li, S. Keyvanian, P. Chadhuari, V . Kumar, and N. Figueroa, “Constraint-aware intent estimation for dynamic human-robot object co-manipulation,” in Robotics: Science and Systems , 2024

    work page 2024

  5. [5]

    Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads,

    E. Mielke, E. Townsend, D. Wingate, J. L. Salmon, and M. D. Killpack, “Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads,” Frontiers in Neurorobotics, vol. 18, 2024

    work page 2024

  6. [6]

    Combining learning-based locomotion policy with model-based manipulation for legged mobile manipulators,

    Y . Ma, F. Farshidian, T. Miki, J. Lee, and M. Hutter, “Combining learning-based locomotion policy with model-based manipulation for legged mobile manipulators,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 2377–2384, 2022

    2022

  7. [7]

    Proximal policy optimization algorithms,

    J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017

    arXiv 2017

  8. [8]

    Isaac gym: High performance gpu-based physics simulation for robot learning,

    V . Makoviychuk, L. Wawrzyniak, Y . Guo, M. Lu, K. Storey, M. Mack- lin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac gym: High performance gpu-based physics simulation for robot learning,” 2021

    2021

Show all 46 references
  1. [9]

    Mujoco: A physics engine for model-based control,

    E. Todorov, T. Erez, and Y . Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems , pp. 5026–5033, IEEE, 2012

    2012

  2. [10]

    Sam 2: Segment anything in images and videos,

    N. Ravi, V . Gabeur, Y .-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R¨adle, C. Rolland, L. Gustafson, et al., “Sam 2: Segment anything in images and videos,” arXiv preprint arXiv:2408.00714 , 2024

    2024 arXiv

  3. [11]

    Wham: Reconstructing world-grounded humans with accurate 3d motion,

    S. Shin, J. Kim, E. Halilaj, and M. J. Black, “Wham: Reconstructing world-grounded humans with accurate 3d motion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, pp. 2070–2080, 2024

    2024

  4. [12]

    Unitree g1 humanoid robot

    U. Robotics, “Unitree g1 humanoid robot.” https://www. unitree.com/g1, 2024. Accessed: 2025-05-16

    2024

  5. [13]

    Variable impedance control of a robot for cooperation with a human,

    R. Ikeura and H. Inooka, “Variable impedance control of a robot for cooperation with a human,” in Proceedings of 1995 IEEE International Conference on Robotics and Automation , vol. 3, pp. 3097–3102 vol.3, 1995

    1995

  6. [14]

    General model of human-robot cooperation using a novel velocity based variable impedance control,

    V . Duchaine and C. M. Gosselin, “General model of human-robot cooperation using a novel velocity based variable impedance control,” in Second Joint EuroHaptics Conference and Symposium on Haptic In- terfaces for Virtual Environment and Teleoperator Systems (WHC’07) , pp. 446–...

    2007

  7. [15]

    Using human motion estimation for human-robot cooperative manipulation,

    A. Thobbi, Y . Gu, and W. Sheng, “Using human motion estimation for human-robot cooperative manipulation,” in 2011 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems , pp. 2873–2878, 2011

    2011

  8. [16]

    Proactive behavior of a humanoid robot in a haptic transportation task with a human partner,

    A. Bussy, P. Gergondet, A. Kheddar, F. Keith, and A. Crosnier, “Proactive behavior of a humanoid robot in a haptic transportation task with a human partner,” in 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communi- cation, pp. 962–967, 2012

    2012

  9. [17]

    A human–robot co- manipulation approach based on human sensorimotor information,

    L. Peternel, N. Tsagarakis, and A. Ajoudani, “A human–robot co- manipulation approach based on human sensorimotor information,” IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 25, no. 7, pp. 811–822, 2017

    2017

  10. [18]

    Learning physical collaborative robot behaviors from human demonstrations,

    L. Rozo, S. Calinon, D. Caldwell, P. Jimenez, and C. Torras, “Learning physical collaborative robot behaviors from human demonstrations,” IEEE Transactions on Robotics , vol. 32, pp. 1–15, 04 2016

    2016

  11. [19]

    Robots taking initiative in collaborative object manipulation: Lessons from physical human-human interaction,

    Z. Rysbek, K.-H. Oh, A. Shervedani, T. Klemencic, M. Zefran, and B. Di Eugenio, “Robots taking initiative in collaborative object manipulation: Lessons from physical human-human interaction,” 04 2023

    2023

  12. [20]

    Learning agile and dynamic motor skills for legged robots,

    J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V . Tsounis, V . Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, Jan. 2019

    2019

  13. [21]

    Learning quadrupedal locomotion over challenging terrain,

    J. Lee, J. Hwangbo, L. Wellhausen, V . Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics, vol. 5, Oct. 2020

    2020

  14. [22]

    Sim-to-real: Learning agile locomotion for quadruped robots,

    J. Tan, T. Zhang, E. Coumans, A. Iscen, Y . Bai, D. Hafner, S. Bo- hez, and V . Vanhoucke, “Sim-to-real: Learning agile locomotion for quadruped robots,” 2018

    2018

  15. [23]

    Humanoid-gym: Reinforcement learning for humanoid robot with zero-shot sim2real transfer,

    X. Gu, Y .-J. Wang, and J. Chen, “Humanoid-gym: Reinforcement learning for humanoid robot with zero-shot sim2real transfer,” arXiv preprint arXiv:2404.05695, 2024

    2024 arXiv

  16. [24]

    Robust humanoid walking on compliant and uneven terrain with deep reinforcement learning,

    R. P. Singh, M. Morisawa, M. Benallegue, Z. Xie, and F. Kanehiro, “Robust humanoid walking on compliant and uneven terrain with deep reinforcement learning,” in 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids) , p. 497–504, IEEE, Nov. 2024

    2024

  17. [25]

    Beamdojo: Learning agile humanoid locomotion on sparse footholds,

    H. Wang, Z. Wang, J. Ren, Q. Ben, T. Huang, W. Zhang, and J. Pang, “Beamdojo: Learning agile humanoid locomotion on sparse footholds,” 2025

    2025

  18. [26]

    Dynamic walk of a biped,

    H. Miura and I. Shimoyama, “Dynamic walk of a biped,” The International Journal of Robotics Research , vol. 3, no. 2, pp. 60–74, 1984

    1984

  19. [27]

    Optimization-based control for dynamic legged robots,

    P. M. Wensing, M. Posa, Y . Hu, A. Escande, N. Mansard, and A. D. Prete, “Optimization-based control for dynamic legged robots,” IEEE Transactions on Robotics , vol. 40, pp. 43–63, 2024

    2024

  20. [28]

    Proximal policy optimization algorithms,

    J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” 2017

    2017

  21. [29]

    Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

    T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,” in Proceedings of the 35th International Conference on Ma- chine Learning (J. Dy and A. Krause, eds.), vol. 80 of Proceedings o...

    2018

  22. [30]

    Dribblebot: Dynamic legged manipulation in the wild,

    Y . Ji, G. B. Margolis, and P. Agrawal, “Dribblebot: Dynamic legged manipulation in the wild,” 2023

    2023

  23. [31]

    Learning agile soccer skills for a bipedal robot with deep reinforcement learning,

    T. Haarnoja, B. Moran, G. Lever, S. H. Huang, D. Tirumala, J. Hump- lik, M. Wulfmeier, S. Tunyasuvunakool, N. Y . Siegel, R. Hafner, M. Bloesch, K. Hartikainen, A. Byravan, L. Hasenclever, Y . Tassa, F. Sadeghi, N. Batchelor, F. Casarini, S. Saliceti, C. Game, N. Sreen- dra, K...

    2024

  24. [32]

    Learning to open and traverse doors with a legged manipulator,

    M. Zhang, Y . Ma, T. Miki, and M. Hutter, “Learning to open and traverse doors with a legged manipulator,” 2024

    2024

  25. [33]

    Learning force control for legged manipulation,

    T. Portela, G. B. Margolis, Y . Ji, and P. Agrawal, “Learning force control for legged manipulation,” 2024

    2024

  26. [34]

    Diffusion policy: Visuomotor policy learn- ing via action diffusion,

    C. Chi, Z. Xu, S. Feng, E. Cousineau, Y . Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learn- ing via action diffusion,” International Journal of Robotics Research , p. 02783649241273668, 2023

    2023

  27. [35]

    Denoising diffusion implicit models,

    J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in International Conference on Learning Representations , 2021

    2021

  28. [36]

    The redundant discrete wavelet transform and additive noise,

    J. E. Fowler, “The redundant discrete wavelet transform and additive noise,” IEEE Signal Processing Letters , vol. 12, no. 9, pp. 629–632, 2005

    2005

  29. [37]

    Improved denoising diffusion prob- abilistic models,

    A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion prob- abilistic models,” in International conference on machine learning , pp. 8162–8171, PMLR, 2021

    2021

  30. [38]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems , vol. 30, 2017

    2017

  31. [39]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems , vol. 33, pp. 6840–6851, 2020

    2020

  32. [40]

    Learning to walk in minutes using massively parallel deep reinforcement learning,

    N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Proceedings of the 5th Conference on Robot Learning , vol. 164 of Proceedings of Machine Learning Research, pp. 91–100, PMLR, 2022

    2022

  33. [41]

    Domain randomization for transferring deep neural networks from simulation to the real world,

    J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” 2017

    2017

  34. [42]

    Mini45 titanium six-axis force/torque sensor,

    ATI Industrial Automation, Inc., “Mini45 titanium six-axis force/torque sensor,” 2024. Accessed: 2025-04-28

    2024

  35. [43]

    Ros: an open-source robot operating system,

    M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Ng, “Ros: an open-source robot operating system,” 01 2009

    2009

  36. [44]

    G1 developer documentation,

    Unitree Robotics, Inc., “G1 developer documentation,” 2025. Ac- cessed: 2025-04-28

    2025

  37. [45]

    Human-robot collab- orative carrying of objects with unknown deformation characteristics,

    D. Sirintuna, A. Giammarino, and A. Ajoudani, “Human-robot collab- orative carrying of objects with unknown deformation characteristics,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10681–10687, 2022

    2022

  38. [46]

    Generating distinctive marker configurations for robot detection in motion capture systems,

    M. Hamandi, A. M. Ali, N. Evangeliou, A. Tzes, and F. Khorrami, “Generating distinctive marker configurations for robot detection in motion capture systems,” in 2025 11th International Conference on Automation, Robotics, and Applications (ICARA) , pp. 247–251, 2025. APPENDIX T...

    2025

Pith tools

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