Collision-Aware Humanoid Whole-Body Control under Imperfect Tracking Targets

2026-09-14

AI Summary

本文提出RECAL方法，通过结合环境几何信息和机器人控制，解决人形机器人在执行动作时因目标跟踪不准确导致的碰撞问题。

Abstract

Humanoid robots often execute motion commands through whole-body controllers (WBCs) that track targets while maintaining balance and stability. However, most WBCs are blind to scene geometry, which can lead to collisions from imperfect target motions that are geometrically unsafe due to perception, planning, or teleoperation errors. We propose RECAL, a Robot--Environment Cross-Attention Layer that wraps a blind WBC to trade off target tracking against collision avoidance using external scene geometry. RECAL supports collision-aware tracking of floating-base and end-effector commands, including collision avoidance for held objects. It represents the robot, held objects, and environment as point clouds, using cross-attention between robot/object points and the environment to produce geometry-aware control features. In simulation, RECAL improves collision avoidance while preserving target-tracking performance across frozen-arm and adaptive-arm locomotion, object-carrying, and standing-manipulation scenarios relative to alternative geometry-aware WBC architectures. We further demonstrate the controller on a real Digit V3 humanoid robot.

Problem

• Collision Avoidance
• Whole-Body Control
• Scene Geometry

Innovation

• Collision-Aware
• Cross-Attention
• Whole-Body Control
• Point Clouds