Beyond Task and Motion Planning: Hierarchical Robot Planning with General-Purpose Skills
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917206933110784 |
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| author | Hedegaard, Benned Wei, Yichen Jaafar, Ahmed Tellex, Stefanie Konidaris, George Shah, Naman |
| author_facet | Hedegaard, Benned Wei, Yichen Jaafar, Ahmed Tellex, Stefanie Konidaris, George Shah, Naman |
| contents | Task and motion planning is a well-established approach for solving long-horizon robot planning problems. However, traditional methods assume that each task-level robot action, or skill, can be reduced to kinematic motion planning. We address the challenge of combining motion planning with closed-loop motor controllers that go beyond mere kinematic considerations. We propose a novel framework that integrates these policies into motion planning using Composable Interaction Primitives (CIPs), enabling the use of diverse, non-composable pre-learned skills in hierarchical robot planning. We validate our Task and Skill Planning (TASP) approach through real-world experiments on a bimanual manipulator and a mobile manipulator, demonstrating that CIPs allow diverse robots to combine motion planning with general-purpose skills to solve complex, long-horizon tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_17901 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Task and Motion Planning: Hierarchical Robot Planning with General-Purpose Skills Hedegaard, Benned Wei, Yichen Jaafar, Ahmed Tellex, Stefanie Konidaris, George Shah, Naman Robotics Artificial Intelligence Task and motion planning is a well-established approach for solving long-horizon robot planning problems. However, traditional methods assume that each task-level robot action, or skill, can be reduced to kinematic motion planning. We address the challenge of combining motion planning with closed-loop motor controllers that go beyond mere kinematic considerations. We propose a novel framework that integrates these policies into motion planning using Composable Interaction Primitives (CIPs), enabling the use of diverse, non-composable pre-learned skills in hierarchical robot planning. We validate our Task and Skill Planning (TASP) approach through real-world experiments on a bimanual manipulator and a mobile manipulator, demonstrating that CIPs allow diverse robots to combine motion planning with general-purpose skills to solve complex, long-horizon tasks. |
| title | Beyond Task and Motion Planning: Hierarchical Robot Planning with General-Purpose Skills |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.17901 |