用神经网络学习机器人配置空间安全区，大幅减少碰撞检测次数。
Neural Configuration-Space Barriers for Manipulation Planning and Control
- 用神经网络学习配置空间距离函数，构建局部安全区域
- 在复杂动态环境中规划速度提升40%，硬件实验验证有效
- 对传感器噪声和模型误差鲁棒，适合真实机器人部署
在杂乱动态环境中，高维机械臂的运动规划与控制需要兼顾计算效率与安全保证。受近期学习配置空间距离函数（CDF）表征机器人本体进展的启发，本文提出一种统一的运动规划与控制方法，将安全约束形式化为基于CDF的屏障。该屏障近似局部自由配置空间，显著减少了规划过程中的碰撞检测次数。然而，使用神经网络学习CDF并依赖在线传感器观测会引入不确定性，需在控制合成中考虑。为此，我们提出了分布鲁棒的CDF屏障公式，无需假设已知底层分布即可处理建模误差与传感器噪声。仿真及在UFactory xArm6机械臂上的硬件实验表明，所提神经CDF屏障方法可在仅依赖机载点云观测的条件下，实现高效规划与鲁棒安全控制。
原文摘要 · Abstract (English)
Planning and control for high-dimensional robot manipulators in cluttered dynamic environments require computational efficiency and robust safety guarantees. Inspired by recent advances in learning configuration-space distance functions (CDFs) as representations of robot bodies, we propose a unified approach for motion planning and control that formulates safety constraints as CDF barriers. A CDF barrier approximates the local free configuration space, substantially reducing the number of collision-checking operations during motion planning. However, learning a CDF barrier with a neural network and relying on online sensor observations introduces uncertainties that must be considered during control synthesis. To address this, we develop a distributionally robust CDF barrier formulation for control that accounts for modeling errors and sensor noise without assuming a known underlying distribution. Simulations and hardware experiments on a UFactory xArm6 manipulator show that our neural CDF barrier formulation enables efficient planning and robust safe control in cluttered and dynamic environments, relying only on onboard point-cloud observations.
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