  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.
