We present RoboArm-NMP, a learning and evaluation environment that allows simple and thorough evaluations of Neural Motion Planning (NMP) algorithms, focused on robotic manipulators. Our Python-based environment provides baseline implementations for learning control policies (either supervised or reinforcement learning based), a simulator based on PyBullet, data of solved instances using a classical motion planning solver, various representation learning methods for encoding the obstacles, and a clean interface between the learning and planning frameworks. Using RoboArm-NMP, we compare several prominent NMP design points, and demonstrate that the best methods mostly succeed in generalizing to unseen goals in a scene with fixed obstacles, but have difficulty in generalizing to unseen obstacle configurations, suggesting focus points for future research.

翻译：我们提出了RoboArm-NMP，一个用于神经运动规划（NMP）算法学习与评估的环境，其重点在于机器人机械臂，能够进行简单而全面的评估。我们基于Python的环境提供了以下功能：学习控制策略（基于监督学习或强化学习）的基线实现、基于PyBullet的模拟器、使用经典运动规划求解器获得的已解决实例数据、多种用于编码障碍物的表示学习方法，以及学习与规划框架之间的清晰接口。利用RoboArm-NMP，我们比较了若干重要的NMP设计点，结果表明，最佳方法在固定障碍物场景中向未见目标泛化时大多能成功，但在向未见障碍物配置泛化时存在困难，这为未来研究指明了重点方向。