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RoboArm-NMP: a Learning Environment for Neural Motion Planning

arXiv 2405.16335 ↗First posted 25 May 2024cs.RO cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 25 May 2024.

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25 of 41 references.

References · 25

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13 more
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