RoboArm-NMP: a Learning Environment for Neural Motion Planning
<|reference_start|>RoboArm-NMP: a Learning Environment for Neural Motion Planning: 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.<|reference_end|>
@article{jurgenson2024roboarm-nmp:,
    title={RoboArm-NMP: a Learning Environment for Neural Motion Planning},
    author={Tom Jurgenson, Matan Sudry, Gal Avineri, Aviv Tamar},
    journal={arXiv preprint arXiv:2405.16335},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16335},
    primaryClass={cs.RO cs.LG}
}