为仿人机器人设计穿戴式感知系统，提升复杂环境避障与运动规划能力
ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning
- 采用可穿戴深度传感器构建分布式视觉感知，增强空间认知
- 仿真训练变压器式模仿学习策略，实测碰撞减少63.7%，成功率提升78.7%
- 真实机器人部署验证，计算延迟降低26倍，适合高动态场景应用
仿人机器人在感知能力上存在明显短板，难以在密集环境中进行有效运动规划。为此，我们提出ARMOR——一种新型的自适应视角感知系统，融合软硬件设计，专为仿人机器人配备类穿戴式深度传感器。该分布式感知架构显著提升了机器人的空间感知能力，支持更敏捷的运动规划。我们在模拟环境中利用AMASS数据集中的约86小时人类真实动作，训练了一个基于Transformer的模仿学习（IL）策略，实现动态避障。实验表明，相较于多个密集安装的头戴及外置深度相机方案，我们的ARMOR感知系统使碰撞减少63.7%，成功率提升78.7%。与基于采样的运动规划专家cuRobo相比，该IL策略碰撞减少31.6%，成功率提高16.9%，计算延迟降低26倍。最后，我们将ARMOR感知系统部署于傅里叶智能公司研发的真实GR1仿人机器人上，后续将在arXiv版本更新源代码、硬件说明及3D CAD文件。
原文摘要 · Abstract (English)
Humanoid robots have significant gaps in their sensing and perception, making it hard to perform motion planning in dense environments. To address this, we introduce ARMOR, a novel egocentric perception system that integrates both hardware and software, specifically incorporating wearable-like depth sensors for humanoid robots. Our distributed perception approach enhances the robot's spatial awareness, and facilitates more agile motion planning. We also train a transformer-based imitation learning (IL) policy in simulation to perform dynamic collision avoidance, by leveraging around 86 hours worth of human realistic motions from the AMASS dataset. We show that our ARMOR perception is superior against a setup with multiple dense head-mounted, and externally mounted depth cameras, with a 63.7% reduction in collisions, and 78.7% improvement on success rate. We also compare our IL policy against a sampling-based motion planning expert cuRobo, showing 31.6% less collisions, 16.9% higher success rate, and 26x reduction in computational latency. Lastly, we deploy our ARMOR perception on our real-world GR1 humanoid from Fourier Intelligence. We are going to update the link to the source code, HW description, and 3D CAD files in the arXiv version of this text.
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