Pith. sign in

REVIEW 3 major objections 7 minor 8 cited by

A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion

T0 review · 3 major / 7 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read A single whole-body policy can switch a real humanoid among walking, standing, jumping, and hopping while tracking tunable gait parameters and accepting external arm control.

desk verdict Solid, well-executed RL controller paper: the extended command space and intervention training are real contributions, but the abstract overclaims 'any locomotive behavior' when hopping is a separate, intervention-free policy and the hardware validation only covers two posture commands. read the letter →

arxiv 2502.03206 v3 pith:JRRL77KK submitted 2025-02-05 cs.RO cs.AI

Yufei Xue , Wentao Dong , Minghuan Liu , Weinan Zhang , Jiangmiao Pang This is my paper · ORCID
classification cs.ROcs.AI
keywords whole-bodycontrolhumanoidlocomotionreinforcementlearningcommandspacesim-to-realtransferloco-manipulationgaitinterventiontraining
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

HUGWBC is a single reinforcement-learned whole-body controller that aims to make humanoid locomotion as adjustable as human gait. It defines a command space with task commands (linear and angular velocity) and behavior commands (gait frequency, foot swing height, body height, body pitch, waist yaw), plus phase variables that encode walking, standing, and jumping; one policy covers those three gaits, and a second policy covers hopping. The paper claims that a real Unitree H1 tracks all of these commands with low error, in simulation and on hardware, and that the same policy remains stable when an external controller takes over the upper body in real time. If true, HUGWBC offers a reusable low-level controller for loco-manipulation and high-level planning rather than a single-purpose gait module.

What carries the argument

The load-bearing object is the factored command space $C = K \times B$, subdivided into task commands (target velocities) and behavior commands (gait frequency, foot swing height, body height, body pitch, waist yaw). Gait identity enters through two periodic phase variables $\phi_i$, a duty cycle $\phi_{\text{stance}}$, and an expected contact probability function $C(\phi)$ built from normal cumulative distribution functions, which smooths the stance-swing switch and is converted into clock functions $\sin(2\pi\bar{\phi}_i)$ for the policy input. These drive periodic contact-swing rewards and a piecewise quintic foot-trajectory target that produces the actual foot clearing in swing. The second mechanism is intervention training: a binary indicator $I(t)$ tells the policy when an external upper-body controller has taken over, and the training curriculum replaces upper-body actions with smoothly interpolated noise while masking upper-body regularization so the lower body keeps tracking locomotion commands. A mirror function and symmetry loss encourage symmetric motion, and the whole system is trained with an asymmetric actor-critic setup.

What would settle it

Run the deployed HUGWBC policy on a physical Unitree H1 with a motion-capture system, commanding a slow walk at $v_x = 0.3$ m/s with a fixed foot swing height, and record foot clearance and body pitch over several strides. If the real robot's foot-swing or phase tracking error exceeds the simulated values by a wide margin (for example, foot clearance off by more than a few centimeters, or pitch error more than double the reported roughly 0.04 rad), the central sim-to-real claim would fail.

Watch

Extended reading notes

Core claim

The central claim is that versatile whole-body locomotion for a humanoid does not need separate controllers per gait or a motion-tracking reference. HUGWBC instead learns one policy (two, counting hopping) from a factored command space $C = K \times B$: target velocity for the task, and behavior commands for the feet, posture, and gait phase. The paper argues that this single policy, trained with a symmetric mirror loss and a curriculum that injects external upper-body interventions, transfers to the physical Unitree H1 and reproduces natural walking, standing, jumping, and hopping while tracking eight command channels with errors on the order of centimeters and tenths of radians. It further claims that the intervention training makes the whole-body controller robust to arbitrary upper-body takeover, enabling teleoperated loco-manipulation under any of the locomotive behaviors.

Load-bearing premise

The policy's promise rests on sim-to-real transfer: that gaits and tracking accuracy learned in a physics simulator with randomized dynamics carry over to the physical Unitree H1, so the real-robot results match the simulation numbers.

Editorial extensions

If this is right

  • One policy can switch among standing, walking, and jumping on demand, so gait transitions do not require per-gait controllers or reference motion clips.
  • Users can modulate frequency, foot swing height, body height, pitch, and waist yaw online; low speeds track best, and walking preserves the widest range of orthogonal command combinations.
  • External upper-body takeover (teleoperation) can be layered on top of any supported locomotion without retraining, enabling loco-manipulation that remains accurate while the robot moves.
  • Intervention training also hardens the policy against pushes and added loads, not just arm interventions.
  • Tracking-error and orthogonality heatmaps give a practical map of which command combinations remain feasible per gait, and a high-level planner can use them to avoid infeasible requests.

Reading between the lines

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

  • The paper notes that running can be derived from walking commands by combining high velocity with a small duty cycle; a natural extension is to test whether the same command space actually produces a running gait on hardware and whether the flight phases match the commanded duty cycle.
  • Because intervention training was compared only against a motion-capture-data baseline, one could test whether the noise curriculum generalizes to other external disturbances, such as unknown payloads or human pushes, by measuring the robustness margins directly.
  • The symmetry loss is applied to asymmetric hopping through left-right mirroring, which suggests the same trick could be used to train other asymmetric behaviors, such as stair ascent or side-stepping, inside the same unified policy.
  • The command orthogonality analysis implies that command feasibility is itself a learned property; a planner could query the policy's estimated tracking-error surface rather than assuming all command combinations are reachable.
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

3 major / 7 minor

Summary. The paper proposes HUGWBC, a reinforcement-learning-based whole-body controller for the Unitree H1 humanoid. The controller uses a general command space that includes task commands (linear/angular velocity) and behavior commands (gait frequency, foot swing height, body height, body pitch, waist yaw, and gait phase parameters), and it is trained with an asymmetric actor-critic setup, a symmetry loss, and an intervention-training curriculum that replaces upper-body actions with noisy external signals. The authors report simulation tracking errors for eight commands under four gaits, command-combination heatmaps, ablations of the intervention strategy, and real-robot demonstrations of walking, jumping, standing, and hopping, with additional qualitative real-world intervention results.

Significance. If the central claims hold, HUGWBC would be a useful step toward a single learned whole-body controller that supports multiple gaits, customizable gait parameters, and external upper-body intervention for loco-manipulation. The simulation study is extensive: Table III gives per-command tracking errors across gaits, Fig. 8 provides systematic command-combination heatmaps, Tables IV, V, and X ablate intervention training, and Fig. 6 measures disturbance tolerance. The real-robot demos in Fig. 1 are also valuable evidence of transfer. However, the paper's strongest claims overreach the evidence in two specific ways: the 'any locomotive behavior' intervention claim is contradicted by the hopping policy's training protocol, and the real-world quantitative evaluation covers only two posture commands. These gaps need to be fixed before the claims can be accepted as stated.

major comments (3)
  1. [Abstract, Fig. 1, Sec. IV-E, Appendix C-B] The abstract and Fig. 1 caption state that external upper-body intervention enables loco-manipulation 'under any locomotive behavior,' but this is contradicted by the paper's own training protocol. Section IV-E describes intervention training for the whole-body policy, while Appendix C-B explicitly states that hopping is implemented with an independent policy and that 'we did not involve intervention training for the hopping gait.' Tables IV, V, and X show that robustness to upper-body intervention is a trained property rather than an emergent one (for example, without intervention training, uniform-noise intervention degrades Evx from 0.048 to 0.866 m/s under walking and foot displacement from 0.034 to 17.54 m under standing). Since hopping is one of the four gaits featured in the abstract and Fig. 1, the 'any locomotive behavior' claim is not supported for hopping. The claim should be restricted to standing, walking, and jumping, or the hopping policy must receive intervention training and be evaluated under intervention.
  2. [Sec. V-D, Table VI] The real-world validation is substantially narrower than the paper's headline claims. Table VI reports tracking error only for body pitch and waist yaw from motor readings; there is no hardware measurement of linear or angular velocity tracking, gait frequency, foot swing height, or the effect of upper-body intervention on locomotion. Consequently, the claim that HUGWBC 'enables real-world humanoid robots to produce various natural gaits' with high tracking accuracy for all commands is not quantitatively established on hardware; the sim-to-real transfer of the command-tracking and intervention properties is supported mainly by qualitative demonstrations for most commands. The authors should either add motion-capture-based measurements for velocity, frequency, and foot height on the real robot, or explicitly narrow the real-world claims to the quantities actually measured.
  3. [Appendix B-B, Table VII] The hopping gait does not support the full command space. Appendix B-B restricts the hopping commands to {vx, vy, omega, h}, and Table VII omits frequency, foot swing height, body pitch, and waist yaw. Combined with the separate hopping policy described in Sec. IV-A, this undercuts the 'unified and general' framing for hopping. The abstract and contributions currently list frequency and foot swing height as customizable parameters without noting this exception, and the paper should state clearly which parameters apply to which gaits.
minor comments (7)
  1. [Sec. IV-C, Eq. (8)] The text says 'We set the value as σcf = 50, σcf = 5'; the second symbol should presumably be σcv = 5.
  2. [Sec. IV-C, Eq. (5)] The contact probability function uses σ, but the value of σ is not specified there; the meaning of σ should be defined when it is first introduced.
  3. [Fig. 2] The framework name in the figure caption is written 'HUGHBC' rather than 'HUGWBC'.
  4. [Appendix B-C] The text says 'pe,z is the z-coordinate of the start position' twice; the second occurrence should be 'end position'.
  5. [Sec. V-C] The text uses 'AAMAS dataset' where the intended name is 'AMASS' (as in the references and Table IV); please unify the spelling.
  6. [Appendix C-B, Table XI] The comparison with HOVER and ExBody is explicitly described as forcing those policies 'to perform tasks beyond their intended design.' This caveat weakens the claim of superiority; the table should be framed as an exploratory comparison rather than a head-to-head benchmark.
  7. [Fig. 8 caption] The caption refers to 'waist roll' in the parameter list, but the command is 'waist yaw'.

Circularity Check

0 steps flagged · score 1.0 of 10

No meaningful circularity: HUGWBC is an empirical RL system whose claims are validated by direct tracking evaluations, not by construction or self-citation.

full rationale

The paper does not claim to derive its controller from first principles; it trains a policy with reward functions and command tracking, then evaluates tracking accuracy and intervention robustness in simulation and on hardware. The reward terms, foot-trajectory polynomials, and command spaces are engineering designs, not parameters fitted to the reported results, so no fitted input is renamed as a prediction. The symmetric loss and intervention curriculum are training techniques whose effects are ablated against baselines, not circularly asserted. The few self-citations ([27], [52], both involving author Yufei Xue) are used for standard techniques such as terrain curricula and are not load-bearing; the central claims do not reduce to these citations. The paper explicitly states that hopping uses an independent policy and was excluded from intervention training (Appendix C-B: "we did not involve intervention training for the hopping gait"), which conflicts with the abstract's "under any locomotive behavior" phrasing, but that is a scope-overclaim issue rather than circularity. Overall, the derivation chain is empirically self-contained and the circularity burden is low.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on hand-tuned reward weights, curriculum hyperparameters, and command ranges, plus standard domain assumptions about simulator fidelity and encoder accuracy. No new physical entities are introduced. The foot trajectory and phase-based contact rewards are engineering constructs, not fitted predictions.

free parameters (6)
  • Reward weights (15 terms in Table I) = e.g., -40 (body height), -10 (body pitch), -2 (waist yaw), -30 (foot swing)
    Hand-tuned to balance task, behavior, and regularization objectives; the reported tracking behavior depends on these weights.
  • Beta (symmetry loss weight) = 0.5
    Chosen in Section IV-D to balance the RL objective and the symmetry loss.
  • p (intervention mode switch probability) = 0.005
    Chosen in Section IV-E so control mode switches infrequently during training.
  • tinterval (intervention noise hold time) = 90 steps
    Set in Section IV-E to avoid jitter from noise sampling.
  • sigma_cf, sigma_cv (contact reward coefficients) = sigma_cf=50, sigma_cv=5
    Fine-tuned in Eq. (8) based on prior work [33].
  • Command ranges (Tab. II) = vx in [-0.6, 2.0], f in [1.5, 3.5] Hz, l in [0.1, 0.35] m, h in [-0.3, 0] m, p in [0, 0.4] rad, w in [-1, 1] rad
    Design choices that define the claimed versatility; different ranges would change the tracking-error results.
assumptions (4)
  • domain assumption IsaacGym simulation with its default contact models faithfully represents Unitree H1 dynamics enough for sim-to-real transfer.
    Used throughout Section V; if the sim-to-real gap is large, the real-world gaits would not match reported tracking.
  • domain assumption The asymmetric actor-critic encoder can recover linear velocity, foot clearance, and body height from a five-step proprioceptive history.
    Relied on in Section IV-B and Appendix B-E; the deployed policy has no direct measurement of these privileged states.
  • domain assumption PPO with the given rewards converges to a policy that generalizes across the full command ranges and terrain curriculum.
    Implicit in the training setup of Section IV-F.
  • ad hoc to paper The quintic polynomial foot trajectory is an appropriate target for natural and stable locomotion.
    Adopted in Eq. (10) and Appendix B-C as a design choice, not derived from first principles.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion." pith.science (2026). https://pith.science/paper/JRRL77KK

@misc{pith2026250203206,
  author       = {Pith},
  title        = {Pith review of: A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JRRL77KK}},
  note         = {Machine review of arXiv:2502.03206}
}
read the original abstract

Locomotion is a fundamental skill for humanoid robots. However, most existing works make locomotion a single, tedious, unextendable, and unconstrained movement. This limits the kinematic capabilities of humanoid robots. In contrast, humans possess versatile athletic abilities-running, jumping, hopping, and finely adjusting gait parameters such as frequency and foot height. In this paper, we investigate solutions to bring such versatility into humanoid locomotion and thereby propose HugWBC: a unified and general humanoid whole-body controller for versatile locomotion. By designing a general command space in the aspect of tasks and behaviors, along with advanced techniques like symmetrical loss and intervention training for learning a whole-body humanoid controlling policy in simulation, HugWBC enables real-world humanoid robots to produce various natural gaits, including walking, jumping, standing, and hopping, with customizable parameters such as frequency, foot swing height, further combined with different body height, waist rotation, and body pitch. Beyond locomotion, HugWBC also supports real-time interventions from external upper-body controllers like teleoperation, enabling loco-manipulation with precision under any locomotive behavior. Extensive experiments validate the high tracking accuracy and robustness of HugWBC with/without upper-body intervention for all commands, and we further provide an in-depth analysis of how the various commands affect humanoid movement and offer insights into the relationships between these commands. To our knowledge, HugWBC is the first humanoid whole-body controller that supports such versatile locomotion behaviors with high robustness and flexibility.

Figures

Figures reproduced from arXiv: 2502.03206 by the authors.

Figure 1
Figure 1. Humanoid capabilities supported by HUGWBC. First row: HUGWBC allows four standard gaits - walking, jumping, standing, and hopping - with multiple customizable parameters to adjust the foot and pose behaviors, using one policy for 3 of the 4 gaits. Second row: HUGWBC supports real-time interventions from external upper-body controllers, enabling loco-manipulation while maintaining precise control under any locomotive… view at source ↗
Figure 2
Figure 2. Framework of HUGHBC. Illustration with the Unitree H1 robot. a): Visualization of parts of commands. The side view (left) highlights the linear velocity, foot swing height, and body pitch commands. The top-right view shows the angular velocity and waist yaw commands, and the bottom-right view shows the body height command. b): Policy inputs/outputs. The policy is provided with commands, proprioceptive observations, … view at source ↗
Figure 3
Figure 3. The expected contact probability function C(ϕt,i) in the loose and normal formulation. The larger C(ϕt,i), the higher the expectation of contact with the ground. The CDF of the normal distribution is introduced into the normal contact probability function to relax the constraint of the foot contact at the switching boundary, resulting in a smooth transition between the swing and the stance phase. set the phase offse… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Phase variables and clock functions under different gaits. Left: The purple ring represents the phase variable ϕ1 for the left foot, while the green ring represents the phase variable ϕ2 for the right foot. ψ is the phase offset from ϕ1 to ϕ2. The dividing phase betwee…
Figure 5
Figure 5. Figure 5: Intervention noise curriculum. We illustrate sampled noise by visualizing the hand positions relative to the visualized robot hand joints. Top 1-3: Noise samples of three curriculum stages with noise levels ranging from small to large. These noises are only relative to…
Figure 6
Figure 6. Figure 6: External disturbance tolerance. Left: A constant and continuous force is applied to the robot. Right: A one-second force is exerted on the robot. The experiment is conducted under a standing gait with default commands. If the robot’s survival ratio exceeds 98%, it is d…
Figure 7
Figure 7. Figure 7: DOF demonstration of Unitree H1. TABLE VII: Ranges and default values of commands for gait hopping. Group Term Default Range Movement linear velocity vx 0 [−0.6, 0.6] linear velocity vy 0 [−0.6, 0.6] angular velocity ω 0 [−0.6, 0.6] Posture body height h 0 [−0.3, 0] wh…
Figure 8
Figure 8. Figure 8: Tracking-error heat maps of command combination under different gaits. Each column represents one of the following command parameters: linear velocity x, linear velocity y, angular velocity yaw, gait frequency, foot swing height, body height, body pitch, and waist roll…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 8 Pith papers

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

  1. Scaling Behavior Foundation Model for Humanoid Robots

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A scaling recipe for humanoid behavior foundation models—global-frame motion tracking, on-policy data quantity plus reference diversity, and a transformer with hyperspherical latents—cuts global tracking error by roug...

  2. Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation

    cs.RO 2025-10 conditional novelty 6.0 of 10

    A 10,300-demonstration, 260-task multimodal humanoid manipulation dataset with baseline policy evaluations and a cloud evaluation platform.

  3. A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting

    cs.RO 2025-09 conditional novelty 6.0 of 10

    A neural retargeting pipeline maps human motion to humanoid robot motion at 5000+ frames per second using a shared latent space and physics-based fine-tuning, filtering noise and producing physically feasible trajectories.

  4. Learning Motion Skills with Adaptive Assistive Curriculum Force in Humanoid Robots

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A2CF uses an adaptive assistive-force agent to guide humanoid robots through training, yielding faster convergence and robust policies that work without the external force.

  5. GMT: General Motion Tracking for Humanoid Whole-Body Control

    cs.RO 2025-06 conditional novelty 6.0 of 10

    GMT trains a single unified humanoid policy using adaptive sampling and mixture-of-experts, achieving lower tracking errors than a re-implemented ExBody2 across diverse whole-body motions.

  6. KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A robot control method that adaptively tightens motion-tracking reward tolerances achieves lower tracking errors on dynamic skills and transfers zero-shot to a real humanoid.

  7. MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A two-stage reinforcement learning pipeline with a mixture of latent residual experts gives a Unitree G1 humanoid multiple commanded human-like gaits over complex terrains.

  8. A Survey: Learning Embodied Intelligence from Physical Simulators and World Models

    cs.RO 2025-07 conditional novelty 4.0 of 10

    Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.

Reference graph

Works this paper leans on

56 extracted references · 35 canonical work pages · cited by 8 Pith papers

  1. [1]

    Multicontact locomotion of legged robots

    Justin Carpentier and Nicolas Mansard. Multicontact locomotion of legged robots. IEEE Transactions on Robotics, 34(6):1441–1460, 2018

    work page 2018

  2. [2]

    Learning smooth humanoid locomotion through lipschitz-constrained poli- cies

    Zixuan Chen, Xialin He, Yen-Jen Wang, Qiayuan Liao, Yanjie Ze, Zhongyu Li, S Shankar Sastry, Jiajun Wu, Koushil Sreenath, Saurabh Gupta, et al. Learning smooth humanoid locomotion through lipschitz-constrained poli- cies. arXiv preprint arXiv:2410.11825 , 2024

    arXiv

  3. [3]

    Expressive whole- body control for humanoid robots

    Xuxin Cheng, Yandong Ji, Junming Chen, Ruihan Yang, Ge Yang, and Xiaolong Wang. Expressive whole- body control for humanoid robots. arXiv preprint arXiv:2402.16796, 2024

    arXiv 2024

  4. [4]

    Open-television: Teleoperation with immersive active visual feedback

    Xuxin Cheng, Jialong Li, Shiqi Yang, Ge Yang, and Xiaolong Wang. Open-television: Teleoperation with immersive active visual feedback. arXiv preprint arXiv:2407.01512, 2024

    arXiv 2024

  5. [5]

    Extreme parkour with legged robots

    Xuxin Cheng, Kexin Shi, Ananye Agarwal, and Deepak Pathak. Extreme parkour with legged robots. In 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages 11443–11450. IEEE, 2024

    2024

  6. [6]

    Learning quadrupedal locomotion on deformable terrain

    Suyoung Choi, Gwanghyeon Ji, Jeongsoo Park, Hyeongjun Kim, Juhyeok Mun, Jeong Hyun Lee, and Jemin Hwangbo. Learning quadrupedal locomotion on deformable terrain. Science Robotics , 8(74):eade2256, 2023

    work page 2023

  7. [7]

    Combining trajectory optimization, supervised machine learning, and model structure for mitigating the curse of dimensionality in the control of bipedal robots

    Xingye Da and Jessy Grizzle. Combining trajectory optimization, supervised machine learning, and model structure for mitigating the curse of dimensionality in the control of bipedal robots. The International Journal of Robotics Research, 38(9):1063–1097, 2019

    work page 2019

  8. [8]

    Bipedal walking on constrained footholds: Momentum regulation via vertical com control

    Min Dai, Xiaobin Xiong, and Aaron Ames. Bipedal walking on constrained footholds: Momentum regulation via vertical com control. In 2022 International Conference on Robotics and Automation (ICRA) , pages 10435–10441, 2022

    work page 2022

Show all 56 references
  1. [9]

    Dario Bellicoso, Christian Gehring, Jemin Hwangbo, Péter Fankhauser, and Marco Hutter

    C. Dario Bellicoso, Christian Gehring, Jemin Hwangbo, Péter Fankhauser, and Marco Hutter. Perception-less terrain adaptation through whole body control and hierar- chical optimization. In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) , pages 558–564, 2016

    2016

  2. [10]

    Wensing, Benjamin Katz, Gerardo Bledt, and Sangbae Kim

    Jared Di Carlo, Patrick M. Wensing, Benjamin Katz, Gerardo Bledt, and Sangbae Kim. Dynamic locomotion in the mit cheetah 3 through convex model-predictive control. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 1–9, 2018. doi: 10.110...

    2018

  3. [11]

    C-croc: Continuous and convex resolution of centroidal dynamic trajectories for legged robots in multicontact scenarios

    Pierre Fernbach, Steve Tonneau, Olivier Stasse, Justin Car- pentier, and Michel Taïx. C-croc: Continuous and convex resolution of centroidal dynamic trajectories for legged robots in multicontact scenarios. IEEE Transactions on Robotics, 36(3):676–691, 2020

    2020

  4. [12]

    Humanplus: Humanoid shadowing and imitation from humans

    Zipeng Fu, Qingqing Zhao, Qi Wu, Gordon Wetzstein, and Chelsea Finn. Humanplus: Humanoid shadowing and imitation from humans. arXiv preprint arXiv:2406.10454 , 2024

    2024 arXiv

  5. [13]

    Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation

    Zipeng Fu, Tony Z Zhao, and Chelsea Finn. Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation. arXiv preprint arXiv:2401.02117, 2024

    2024 arXiv

  6. [14]

    Control of dynamic gaits for a quadrupedal robot

    Christian Gehring, Stelian Coros, Marco Hutter, Michael Bloesch, Markus A Hoepflinger, and Roland Siegwart. Control of dynamic gaits for a quadrupedal robot. In 2013 IEEE international conference on Robotics and automation, pages 3287–3292. IEEE, 2013

    2013

  7. [15]

    Perceptive locomo- tion through nonlinear model-predictive control

    Ruben Grandia, Fabian Jenelten, Shaohui Yang, Far- bod Farshidian, and Marco Hutter. Perceptive locomo- tion through nonlinear model-predictive control. IEEE Transactions on Robotics , 39(5):3402–3421, 2023. doi: 10.1109/TRO.2023.3275384

    2023

  8. [16]

    Griffin, Georg Wiedebach, Stephen McCrory, Sylvain Bertrand, Inho Lee, and Jerry Pratt

    Robert J. Griffin, Georg Wiedebach, Stephen McCrory, Sylvain Bertrand, Inho Lee, and Jerry Pratt. Footstep planning for autonomous walking over rough terrain. In 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) , pages 9–16, 2019

    2019

  9. [17]

    Advancing humanoid locomotion: Mastering challenging terrains with denoising world model learning

    Xinyang Gu, Yen-Jen Wang, Xiang Zhu, Chengming Shi, Yanjiang Guo, Yichen Liu, and Jianyu Chen. Advancing humanoid locomotion: Mastering challenging terrains with denoising world model learning. arXiv preprint arXiv:2408.14472, 2024

    2024 arXiv

  10. [18]

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

    Tairan He, Zhengyi Luo, Xialin He, Wenli Xiao, Chong Zhang, Weinan Zhang, Kris Kitani, Changliu Liu, and Guanya Shi. Omnih2o: Universal and dexterous human-to- humanoid whole-body teleoperation and learning. arXiv preprint arXiv:2406.08858, 2024

    2024 arXiv

  11. [19]

    Learning human- to-humanoid real-time whole-body teleoperation

    Tairan He, Zhengyi Luo, Wenli Xiao, Chong Zhang, Kris Kitani, Changliu Liu, and Guanya Shi. Learning human- to-humanoid real-time whole-body teleoperation. arXiv preprint arXiv:2403.04436, 2024

    2024 arXiv

  12. [20]

    Hover: Versatile neural whole- body controller for humanoid robots

    Tairan He, Wenli Xiao, Toru Lin, Zhengyi Luo, Zhenjia Xu, Zhenyu Jiang, Jan Kautz, Changliu Liu, Guanya Shi, Xiaolong Wang, et al. Hover: Versatile neural whole- body controller for humanoid robots. arXiv preprint arXiv:2410.21229, 2024

    arXiv

  13. [21]

    Cousineau, Christian M

    Ayonga Hereid, Eric A. Cousineau, Christian M. Hubicki, and Aaron D. Ames. 3d dynamic walking with underac- tuated humanoid robots: A direct collocation framework for optimizing hybrid zero dynamics. In 2016 IEEE International Conference on Robotics and Automation (ICRA), page...

    2016

  14. [22]

    Dtc: Deep tracking control

    Fabian Jenelten, Junzhe He, Farbod Farshidian, and Marco Hutter. Dtc: Deep tracking control. Science Robotics , 9 (86):eadh5401, 2024

    2024

  15. [23]

    Exbody2: Advanced expressive humanoid whole-body control

    Mazeyu Ji, Xuanbin Peng, Fangchen Liu, Jialong Li, Ge Yang, Xuxin Cheng, and Xiaolong Wang. Exbody2: Advanced expressive humanoid whole-body control. arXiv preprint arXiv:2412.13196, 2024

    2024 arXiv

  16. [24]

    Biped walking stabilization based on linear inverted pendulum tracking

    Shuuji Kajita, Mitsuharu Morisawa, Kanako Miura, Shin’ichiro Nakaoka, Kensuke Harada, Kenji Kaneko, Fumio Kanehiro, and Kazuhito Yokoi. Biped walking stabilization based on linear inverted pendulum tracking. In 2010 IEEE/RSJ International Conference on Intelligent Robots and S...

    2010

  17. [25]

    Koenemann, A

    J. Koenemann, A. Del Prete, Y . Tassa, E. Todorov, O. Stasse, M. Bennewitz, and N. Mansard. Whole-body model-predictive control applied to the hrp-2 humanoid. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 3346–3351, 2015

    2015

  18. [26]

    Visual whole-body control for legged loco-manipulation

    Minghuan Liu, Zixuan Chen, Xuxin Cheng, Yandong Ji, Ri-Zhao Qiu, Ruihan Yang, and Xiaolong Wang. Visual whole-body control for legged loco-manipulation. arXiv preprint arXiv:2403.16967, 2024

    2024 arXiv

  19. [27]

    Skill latent space based multigait learning for a legged robot

    Xin Liu, Jinze Wu, Yufei Xue, Chenkun Qi, Guiyang Xin, and Feng Gao. Skill latent space based multigait learning for a legged robot. IEEE Transactions on Industrial Electronics, 2024

    2024

  20. [28]

    Learning hu- manoid locomotion with perceptive internal model

    Junfeng Long, Junli Ren, Moji Shi, Zirui Wang, Tao Huang, Ping Luo, and Jiangmiao Pang. Learning hu- manoid locomotion with perceptive internal model. arXiv preprint arXiv:2411.14386, 2024

    2024 arXiv

  21. [29]

    Mobile-television: Predictive motion priors for humanoid whole-body control

    Chenhao Lu, Xuxin Cheng, Jialong Li, Shiqi Yang, Mazeyu Ji, Chengjing Yuan, Ge Yang, Sha Yi, and Xiaolong Wang. Mobile-television: Predictive motion priors for humanoid whole-body control. arXiv preprint arXiv:2412.07773, 2024

    2024 arXiv

  22. [30]

    Amass: Archive of motion capture as surface shapes

    Naureen Mahmood, Nima Ghorbani, Nikolaus F Troje, Gerard Pons-Moll, and Michael J Black. Amass: Archive of motion capture as surface shapes. In Proceedings of the IEEE/CVF international conference on computer vision , pages 5442–5451, 2019

    2019

  23. [31]

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

    Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et al. Isaac gym: High performance gpu-based physics simulation for robot learning. arXiv preprint arXiv:2108.10470 , 2021

    2021 arXiv

  24. [32]

    Rapid locomotion via reinforcement learning

    Gabriel Margolis, Ge Yang, Kartik Paigwar, Tao Chen, and Pulkit Agrawal. Rapid locomotion via reinforcement learning. In Robotics: Science and Systems , 2022

    2022

  25. [33]

    Walk these ways: Tuning robot control for generalization with multiplicity of behavior

    Gabriel B Margolis and Pulkit Agrawal. Walk these ways: Tuning robot control for generalization with multiplicity of behavior. Conference on Robot Learning , 2022

    2022

  26. [34]

    Crocoddyl: An efficient and versatile framework for multi-contact optimal control

    Carlos Mastalli, Rohan Budhiraja, Wolfgang Merkt, Guilhem Saurel, Bilal Hammoud, Maximilien Naveau, Justin Carpentier, Ludovic Righetti, Sethu Vijayakumar, and Nicolas Mansard. Crocoddyl: An efficient and versatile framework for multi-contact optimal control. In 2020 IEEE Inte...

    2020

  27. [35]

    Biconmp: A nonlinear model predictive control framework for whole body motion planning

    Avadesh Meduri, Paarth Shah, Julian Viereck, Majid Khadiv, Ioannis Havoutis, and Ludovic Righetti. Biconmp: A nonlinear model predictive control framework for whole body motion planning. IEEE Transactions on Robotics , 39(2):905–922, 2023

    2023

  28. [36]

    Dreamwaq: Learning robust quadrupedal lo- comotion with implicit terrain imagination via deep reinforcement learning

    I Made Aswin Nahrendra, Byeongho Yu, and Hyun Myung. Dreamwaq: Learning robust quadrupedal lo- comotion with implicit terrain imagination via deep reinforcement learning. In 2023 IEEE International Conference on Robotics and Automation (ICRA) , pages 5078–5084. IEEE, 2023

    2023

  29. [37]

    Centroidal dynamics of a humanoid robot

    David E Orin, Ambarish Goswami, and Sung-Hee Lee. Centroidal dynamics of a humanoid robot. Autonomous robots, 35:161–176, 2013

    2013

  30. [38]

    Asymmetric actor critic for image-based robot learning

    Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel. Asymmetric actor critic for image-based robot learning. In Robotics: Science and Systems , 2018

    2018

  31. [39]

    A convex model of humanoid momentum dynamics for multi-contact motion generation

    Brahayam Ponton, Alexander Herzog, Stefan Schaal, and Ludovic Righetti. A convex model of humanoid momentum dynamics for multi-contact motion generation. In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) , pages 842–849, 2016

    2016

  32. [40]

    Efficient multicontact pattern generation with sequential convex approximations of the centroidal dynamics

    Brahayam Ponton, Majid Khadiv, Avadesh Meduri, and Ludovic Righetti. Efficient multicontact pattern generation with sequential convex approximations of the centroidal dynamics. IEEE Transactions on Robotics , 37(5):1661– 1679, 2021

    2021

  33. [41]

    Real-world hu- manoid locomotion with reinforcement learning

    Ilija Radosavovic, Tete Xiao, Bike Zhang, Trevor Darrell, Jitendra Malik, and Koushil Sreenath. Real-world hu- manoid locomotion with reinforcement learning. Science Robotics, 9(89):eadi9579, 2024

    2024

  34. [42]

    Learning to walk in minutes using massively parallel deep reinforcement learning

    Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter. Learning to walk in minutes using massively parallel deep reinforcement learning. In 5th Annual Conference on Robot Learning , 2021

    2021

  35. [43]

    Proximal policy optimization algorithms

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 , 2017

    2017 arXiv

  36. [44]

    Modeling and optimal control of human-like running

    Gerrit Schultz and Katja Mombaur. Modeling and optimal control of human-like running. IEEE/ASME Transactions on mechatronics, 15(5):783–792, 2009

    2009

  37. [45]

    Sim-to-real learning of all common bipedal gaits via periodic reward composition

    Jonah Siekmann, Yesh Godse, Alan Fern, and Jonathan Hurst. Sim-to-real learning of all common bipedal gaits via periodic reward composition. In 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021

    2021

  38. [46]

    A unified mpc framework for whole-body dynamic locomotion and manipulation

    Jean-Pierre Sleiman, Farbod Farshidian, Maria Vittoria Minniti, and Marco Hutter. A unified mpc framework for whole-body dynamic locomotion and manipulation. IEEE Robotics and Automation Letters , 6(3):4688–4695, 2021

    2021

  39. [47]

    Daeun Song, Pierre Fernbach, Thomas Flayols, An- drea Del Prete, Nicolas Mansard, Steve Tonneau, and Young J. Kim. Solving footstep planning as a feasibility problem using l1-norm minimization. IEEE Robotics and Automation Letters, 6(3):5961–5968, 2021

    2021

  40. [48]

    A compliant hybrid zero dynamics controller for stable, efficient and fast bipedal walking on mabel

    Koushil Sreenath, Hae-Won Park, Ioannis Poulakakis, and Jessy W Grizzle. A compliant hybrid zero dynamics controller for stable, efficient and fast bipedal walking on mabel. The International Journal of Robotics Research , 30(9):1170–1193, 2011

    2011

  41. [49]

    Multi-fidelity receding horizon planning for multi-contact locomotion

    Jiayi Wang, Sanghyun Kim, Sethu Vijayakumar, and Steve Tonneau. Multi-fidelity receding horizon planning for multi-contact locomotion. In 2020 IEEE-RAS 20th Inter- national Conference on Humanoid Robots (Humanoids) , pages 53–60, 2021

    2020

  42. [50]

    Improved computation of the humanoid centroidal dynamics and application for whole-body control

    Patrick M Wensing and David E Orin. Improved computation of the humanoid centroidal dynamics and application for whole-body control. International Journal of Humanoid Robotics , 13(01):1550039, 2016

    2016

  43. [51]

    Winkler, C

    Alexander W. Winkler, C. Dario Bellicoso, Marco Hutter, and Jonas Buchli. Gait and trajectory optimization for legged systems through phase-based end-effector parameterization. IEEE Robotics and Automation Letters , 3(3):1560–1567, 2018

    2018

  44. [52]

    Learning robust and agile legged locomotion using adversarial motion priors

    Jinze Wu, Guiyang Xin, Chenkun Qi, and Yufei Xue. Learning robust and agile legged locomotion using adversarial motion priors. IEEE Robotics and Automation Letters, 8(8):4975–4982, 2023. doi: 10.1109/LRA.2023. 3290509

    2023 doi

  45. [53]

    X Xinjilefu, Siyuan Feng, and Christopher G. Atkeson. Dynamic state estimation using quadratic programming. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems , pages 989–994, 2014

    2014

  46. [54]

    Karen Liu

    Wenhao Yu, Greg Turk, and C. Karen Liu. Learning symmetric and low-energy locomotion. ACM Transactions on Graphics (TOG) , 37(4), jul 2018

    2018

  47. [55]

    Wococo: Learning whole-body humanoid control with sequential contacts

    Chong Zhang, Wenli Xiao, Tairan He, and Guanya Shi. Wococo: Learning whole-body humanoid control with sequential contacts. arXiv preprint arXiv:2406.06005 , 2024

    2024 arXiv

  48. [56]

    Humanoid parkour learning

    Ziwen Zhuang, Shenzhe Yao, and Hang Zhao. Humanoid parkour learning. arXiv preprint arXiv:2406.10759 , 2024. APPENDIX A EXTENDED BACKGROUND A. Proximal Policy Optimization Proximal policy optimization (PPO) [ 43] is one of the popular algorithms that solve reinforcement learni...

    2024 arXiv

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.