外观
论文arXiv 2609.164051 个月前
不完美跟踪目标下的碰撞感知类人机器人全身控制
原文链接：Collision-Aware Humanoid Whole-Body Control under Imperfect Tracking Targets（arXiv 2609.16405 · 发布日期：2026-09-14）
Collision-Aware Humanoid Whole-Body Control under Imperfect Tracking Targets
针对不完美目标可能导致碰撞的问题，提出RECAL以场景几何增强盲WBC，并在仿真和真实Digit V3上验证。
核心要点
- 动机是多数全身控制器不感知场景几何，感知、规划或遥操作误差会使目标运动几何不安全。
- RECAL作为Robot--Environment Cross-Attention Layer封装盲WBC，在目标跟踪与避碰之间进行权衡。
- 方法将机器人、持有物体和环境表示为点云，通过跨注意力生成几何感知控制特征。
- 仿真中相较其他几何感知WBC架构提升避碰并保持跟踪性能，且在真实Digit V3上演示。
验证范围与局限：验证包括仿真中的冻结手臂和自适应手臂行走、搬物、站立操作场景，以及一台真实Digit V3类人机器人演示。
英文摘要 (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.
作者：Mohitvishnu S. Gadde, Ashish Malik, Pranay Dugar, Aayam Kumar Shrestha, Fuxin Li, Alan Fern
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