mlsanityData to 7 Oct 2026

Interaction-Aware Whole-Body Control for Compliant Object Transport

arXiv 2603.03751 ↗First posted 4 Mar 2026cs.RO cs.AIcited by 0

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Abstract (arXiv)

Cooperative object transport in unstructured environments remains challenging for assistive humanoids because strong, time-varying interaction forces can make tracking-centric whole-body control unreliable, especially in close-contact support tasks. This paper proposes a bio-inspired, interaction-oriented whole-body control (IO-WBC) that functions as an artificial cerebellum - an adaptive motor agent that translates upstream (skill-level) commands into stable, physically consistent whole-body behavior under contact. This work structurally separates upper-body interaction execution from lower-body support control, enabling the robot to maintain balance while shaping force exchange in a tightly coupled robot-object system. A trajectory-optimized reference generator (RG) provides a kinematic prior, while a reinforcement learning (RL) policy governs body responses under heavy-load interactions and disturbances. The policy is trained in simulation with randomized payload mass/inertia and external perturbations, and deployed via asymmetric teacher-student distillation so that the student relies only on proprioceptive histories at runtime. Extensive experiments demonstrate that IO-WBC maintains stable whole-body behavior and physical interaction even when precise velocity tracking becomes infeasible, enabling compliant object transport across a wide range of scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 4 Mar 2026.

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References · 10

  1. 2019Learning agile and dynamic motor skills for legged robotscited by 402
  2. 2019Control Barrier Functions: Theory and Applicationscited by 332
  3. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 278
  4. 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 239
  5. 2020Learning Stable Deep Dynamics Modelscited by 110
  6. 2021Representation Matters: Offline Pretraining for Sequential Decision Makingcited by 90
  7. 2025AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Controlcited by 47
  8. 2020Learning Stability Certificates from Datacited by 45
  9. 2022RoLoMa: Robust Loco-Manipulation for Quadruped Robots with Armscited by 13
  10. 2021Lyapunov Barrier Policy Optimizationcited by 12

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