RoboArm-NMP: a Learning Environment for Neural Motion Planning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| author | Jurgenson, Tom Sudry, Matan Avineri, Gal Tamar, Aviv |
| author_facet | Jurgenson, Tom Sudry, Matan Avineri, Gal Tamar, Aviv |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_16335 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RoboArm-NMP: a Learning Environment for Neural Motion Planning Jurgenson, Tom Sudry, Matan Avineri, Gal Tamar, Aviv Robotics Machine Learning 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. |
| title | RoboArm-NMP: a Learning Environment for Neural Motion Planning |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2405.16335 |