外观
论文arXiv 2609.236661 个月前
UniPoint：面向挑战性地形人形机器人运动的统一点级传感器融合
原文链接：UniPoint: Unified Point-Level Sensor Fusion for Humanoid Locomotion Across Challenging Terrains（arXiv 2609.23666 · 发布日期：2026-09-20）
UniPoint: Unified Point-Level Sensor Fusion for Humanoid Locomotion Across Challenging Terrains
UniPoint通过多源点级融合LiDAR与深度相机，训练单一全身运动策略，并在DR02真实多地形上验证。
核心要点
- 开放世界部署需要兼顾广覆盖、局部精度和传感器失效冗余，现有方法难同时满足。
- 方法将360° LiDAR与两个深度相机早期融合为基座坐标系点集，再经体素化与注意力编码。
- 单次训练结合地形感知奖励、感知退化注入和域随机化，得到八类地形通用策略。
- 策略部署于RK3588无需微调，真机验证涵盖高台、间隙、薄障碍和稀疏窄落脚点。
验证范围与局限：验证范围为DR02真机在九个真实场景、七类地形各20次试验，并报告户外零样本泛化。
英文摘要 (Abstract)
Open-world deployment requires humanoid robots to cross highly heterogeneous terrain safely, with perception that simultaneously provides wide coverage, local accuracy, and redundancy against sensor failure. Existing approaches struggle to satisfy all three: one forward depth camera or nearby height sampling covers too little; odometry-corrected elevation maps drift under aggressive motion and miss thin vertical structures; image-level encoding costs grow with camera count. We present UniPoint, a humanoid whole-body locomotion framework built on multi-source point-level sensor fusion. Measurements from a 360° light detection and ranging (LiDAR) sensor and two depth cameras are early-fused into one base-frame point set. Voxelization resamples it to a fixed number of tokens encoded by linear self-attention and proprioception-queried cross-attention, decoupling forward cost from sensor count. The point set retains standing thin barriers; a single-modality failure removes only part of the tokens, so the policy degrades gracefully. A single training run with terrain-aware rewards, perception-degradation injection, and domain randomization produces one policy for all eight terrain types, deployed on an onboard RK3588 without fine-tuning. On a DR02 humanoid, 20 trials at each of nine real-world settings over seven terrain types validate the policy on 70-cm-high platforms, 100-cm gaps, thin barriers, and sparse or narrow footholds; it also generalizes zero-shot outdoors.
作者：Sicen Li, Zhen Chu, Chao Li, Qiuguo Zhu, Wu, Jun
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