ADAPT: Analytical Disturbance-Aware Policy Training for Humanoid Locomotion

• URL: http://arxiv.org/abs/2606.16542v1
• Date: Mon, 15 Jun 2026 10:47:18 GMT
• Title: ADAPT: Analytical Disturbance-Aware Policy Training for Humanoid Locomotion
• Abstract summary: Analytical Disturbance-Aware Policy Training (ADAPT) is a framework that equips humanoid policies with a physically grounded disturbance observer.<n>ADAPT estimates residual force/torque online with the accessible robot dynamics, without requiring force/torque sensors.
• Score: 21.269965799228576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humanoids deployed in human-centered environments must handle force-interactive tasks, where external contacts introduce unexpected disturbances that disrupt locomotion accuracy and stability. Existing learning-based approaches rely on broad domain randomization, task-specific force objectives, or learning-based force estimators from motion history, each of which compromises accuracy, task transferability, or out-of-distribution (OOD) robustness. We present Analytical Disturbance-Aware Policy Training (ADAPT), a framework that equips humanoid policies with a physically grounded disturbance observer. The core of ADAPT is an analytical whole-body disturbance observer that estimates residual force/torque online with the accessible robot dynamics, without requiring force/torque sensors. Fed directly into the policy, the estimated disturbances give the humanoid an explicit, physics-derived sense of external force/torque that can generalize across diverse unseen scenes. Experiments on a Unitree G1 humanoid show that ADAPT achieves accurate disturbance prediction and stronger robustness than a proprioception-only baseline under torso perturbations, standing pushes, and asymmetric hand payloads, with improved velocity tracking even on OOD disturbances. Moreover, ADAPT enables penalizing inferred disturbances at lower-body joints to encourage lighter locomotion.

Related papers

• ThorArena: Benchmarking Humanoid Physical Interaction with Human Motion-Force Demonstrations [24.273078797508884]
  ThorArena is a benchmark for evaluating force-aware humanoid interaction based on human demonstrations with synchronized motion and force measurements.<n>We propose force-aware evaluation metrics that jointly assess whole-body tracking accuracy, robustness under different force levels, control effort, and episode survival.
  arXiv  Detail & Related papers  (2026-07-07T09:26:51Z)
• Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot [56.377161933570335]
  Humans naturally exploit a "weightless" state during non-self-stabilizing (NSS) motions.<n>Inspired by this biological mechanism, we propose a weightlessness-state auto-labeling strategy for dataset annotation.<n>Our work bridges the gap between precise trajectory tracking and adaptive environmental interaction, offering a biologically-inspired solution for contact-rich humanoid control.
  arXiv  Detail & Related papers  (2026-04-23T07:10:05Z)
• Neural Assistive Impulses: Synthesizing Exaggerated Motions for Physics-based Characters [13.307372537282333]
  Impulse Neural Control is a framework that reformulates external assistance in impulse space rather than force space to ensure numerical stability.<n>We demonstrate that our method enables robust tracking of highly agile, dynamically infeasible maneuvers.
  arXiv  Detail & Related papers  (2026-04-07T03:47:14Z)
• Interaction-Aware Whole-Body Control for Compliant Object Transport [33.233203393813376]
  This paper proposes an interaction-oriented whole-body control (IO-WBC) that functions as an artificial cerebellum.<n>IO-WBC translates upstream (skill-level) commands into stable, physically consistent whole-body behavior under contact.
  arXiv  Detail & Related papers  (2026-03-04T05:50:40Z)
• ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation [55.467742403416175]
  We introduce a physics-driven neural algorithm that translates large-scale motion capture to humanoid embodiments.<n>We learn a unified multimodal controller that supports both dense references and sparse task specifications.<n>Results show that ULTRA generalizes to autonomous, goal-conditioned whole-body loco-manipulation from egocentric perception.
  arXiv  Detail & Related papers  (2026-03-03T18:59:29Z)
• HAIC: Humanoid Agile Object Interaction Control via Dynamics-Aware World Model [56.4392302336014]
  We present HAIC, a framework for robust interaction across diverse object dynamics without external state estimation.<n>Our key contribution is a dynamics predictor that estimates high-order object states (velocity, acceleration) solely from proprioceptive history.<n> Experiments on a humanoid robot show HAIC achieves high success rates in agile tasks.
  arXiv  Detail & Related papers  (2026-02-12T09:34:35Z)
• GentleHumanoid: Learning Upper-body Compliance for Contact-rich Human and Object Interaction [14.278503723930998]
  GentleHumanoid is a framework that integrates impedance control into a whole-body motion tracking policy to achieve upper-body compliance.<n>We evaluate our approach in both simulation and on the Unitree G1 humanoid across tasks requiring different levels of compliance.
  arXiv  Detail & Related papers  (2025-11-06T18:59:33Z)
• ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning [59.64325421657381]
  Humanoid whole-body loco-manipulation promises transformative capabilities for daily service and warehouse tasks.<n>We introduce ResMimic, a two-stage residual learning framework for precise and expressive humanoid control from human motion data.<n>Results show substantial gains in task success, training efficiency, and robustness over strong baselines.
  arXiv  Detail & Related papers  (2025-10-06T17:47:02Z)
• AdvGrasp: Adversarial Attacks on Robotic Grasping from a Physical Perspective [4.428272932902862]
  This paper introduces AdvGrasp, a framework for adversarial attacks on robotic grasping from a physical perspective.<n>By deforming the object's shape to increase gravitational torque and reduce stability margin in the wrench space, our method generates adversarial objects that compromise grasp performance.
  arXiv  Detail & Related papers  (2025-07-14T01:48:42Z)
• Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
  We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
  arXiv  Detail & Related papers  (2025-02-24T14:53:45Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.