Learning Agile Perceptive Traversal of Sparse 3D Structures for Humanoids

2026-08-30

AI Summary

研究通过强化学习和基于注意力机制的编码器，解决了类人机器人在稀疏3D结构中感知并执行敏捷动作的问题，实现了猴子杆穿越任务。

Abstract

Traversing sparse 3D structures requires humanoid robots to perceive thin, overhanging geometry while executing agile, accurate whole-body motions. We study this problem through monkey-bar traversal, where the robot must jump to the structure, traverse it through sparse bar interactions, and land safely. For this task, we present a reinforcement-learning-based perceptive control system that operates directly on observations from a head-mounted solid-state lidar. To extract task-relevant geometry from the sparse returns, the policy consumes the raw lidar scan through an attention-based encoder with recurrent memory. This policy is obtained by a phase-scheduled teacher- student pipeline that combines privileged experts for jumping up, brachiating, and jumping down. For transfer to hardware, we model lidar noise, battery-voltage sag, and actuator thermal limits, and equip the humanoid with passive hook end-effectors for robust bar interaction. On hardware, the resulting policy completes the full jump-up->brachiation->jump-down sequence in 14 of 15 trials across three bar configurations and reaches brachiation speeds up to 0.5 m/s. Beyond brachiation, the same perception backbone supports a separately trained policy that ducks beneath thin overhead obstacles with 2 cm cross-sections.

Problem

• sparse 3D structures
• humanoid robots
• perceptive control
• lidar
• agile traversal

Innovation

• reinforcement learning
• attention-based encoder
• recurrent memory
• sparse 3D structures
• lidar noise