Interaction-Aware Whole-Body Control for Compliant Object Transport

2026-03-04

AI Summary

This work addresses the challenge of stabilizing humanoid robots during collaborative object transportation in unstructured environments, where highly time-varying interaction forces often destabilize conventional trajectory-tracking whole-body controllers, compromising both balance and compliant interaction. To overcome this, the authors propose a biologically inspired Interaction-Oriented Whole-Body Control (IO-WBC) framework that decouples upper-body interaction from lower-body support, mimicking cerebellar coordination. The framework integrates trajectory optimization for reference motion generation with a reinforcement learning policy for adaptive interaction behavior. Crucially, it employs an asymmetric teacher–student distillation scheme, enabling the policy to generalize to real-world scenarios using only proprioceptive history. Experiments demonstrate that the system maintains stable full-body posture and compliant force interaction under demanding conditions—such as high payload and imprecise velocity tracking—successfully accomplishing diverse transportation tasks.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• cooperative object transport
• whole-body control
• interaction forces
• compliant manipulation
• humanoid robots

Innovation

• interaction-aware control
• whole-body control
• reinforcement learning
• teacher-student distillation
• compliant manipulation