HiWET: Hierarchical World-Frame End-Effector Tracking for Long-Horizon Humanoid Loco-Manipulation

• URL: http://arxiv.org/abs/2602.06341v1
• Date: Fri, 06 Feb 2026 03:11:56 GMT
• Title: HiWET: Hierarchical World-Frame End-Effector Tracking for Long-Horizon Humanoid Loco-Manipulation
• Abstract summary: We reformulate the problem as world-frame end-effector tracking for humanoid loco-manipulation.<n>We propose HiWET, a hierarchical reinforcement learning framework that decouples global reasoning from dynamic execution.<n>We show that HiWET achieves precise and stable end-effector tracking in long-horizon world-frame tasks.
• Score: 43.43128572722804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humanoid loco-manipulation requires executing precise manipulation tasks while maintaining dynamic stability amid base motion and impacts. Existing approaches typically formulate commands in body-centric frames, fail to inherently correct cumulative world-frame drift induced by legged locomotion. We reformulate the problem as world-frame end-effector tracking and propose HiWET, a hierarchical reinforcement learning framework that decouples global reasoning from dynamic execution. The high-level policy generates subgoals that jointly optimize end-effector accuracy and base positioning in the world frame, while the low-level policy executes these commands under stability constraints. We introduce a Kinematic Manifold Prior (KMP) that embeds the manipulation manifold into the action space via residual learning, reducing exploration dimensionality and mitigating kinematically invalid behaviors. Extensive simulation and ablation studies demonstrate that HiWET achieves precise and stable end-effector tracking in long-horizon world-frame tasks. We validate zero-shot sim-to-real transfer of the low-level policy on a physical humanoid, demonstrating stable locomotion under diverse manipulation commands. These results indicate that explicit world-frame reasoning combined with hierarchical control provides an effective and scalable solution for long-horizon humanoid loco-manipulation.

Related papers

• MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation [17.392786435444062]
  We present MotionWAM, a real-time WAM that drives autonomous humanoid loco-manipulation from a single egocentric camera.<n>A three-stage learning framework progressively adapts the video world model to egocentric visual dynamics.
  arXiv  Detail & Related papers  (2026-06-08T08:50:14Z)
• Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking [78.30338456206984]
  We introduce a novel framework for training general-purpose 3D foothold-tracking policies.<n>By dynamically providing footstep support through a goal sampler, this method enables the learned policy to be agnostic to specific terrains.<n>Our new target representation effectively mitigates challenges arising in the real world, such as noisy and inaccurate pose estimation and foot contact estimation.
  arXiv  Detail & Related papers  (2026-06-06T16:44:56Z)
• AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models [60.04879435087352]
  Vision-language-action (VLA) policies generate actions within a single unified space.<n>We propose AnchorRefine, a hierarchical framework that factorizes VLA action modeling into trajectory anchor and residual refinement.<n> Experiments on LIBERO, CALVIN, and real-robot tasks demonstrate that AnchorRefine consistently improves both regression-based and diffusion-based VLA backbones.
  arXiv  Detail & Related papers  (2026-04-20T04:25:24Z)
• REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning [23.094280494500214]
  We present REFINE-DP (REinforcement learning FINE-tuning of Diffusion Policy), a hierarchical framework that jointly optimize a DP high-level planner and an RL-based low-level loco-manipulation controller.<n> REFINE-DP achieves an over $90%$ success rate in simulation, even in out-of-distribution cases not seen in the pre-trained data.
  arXiv  Detail & Related papers  (2026-03-14T02:21:19Z)
• 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)
• CLOT: Closed-Loop Global Motion Tracking for Whole-Body Humanoid Teleoperation [54.7399209456857]
  We present CLOT, a real-time whole-body humanoid teleoperation system that achieves closed-loop global motion tracking.<n>CLOT synchronizes operator and robot poses in a closed loop, enabling drift-free human-to-humanoid mimicry over long timehorizons.<n>We propose a data-driven randomization strategy that decouples observation trajectories from reward evaluation, enabling smooth and stable global corrections.
  arXiv  Detail & Related papers  (2026-02-13T12:03:13Z)
• 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)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)

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.