Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909737571844096 |
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| author | Murooka, Masaki Kumagai, Iori Morisawa, Mitsuharu Kanehiro, Fumio |
| author_facet | Murooka, Masaki Kumagai, Iori Morisawa, Mitsuharu Kanehiro, Fumio |
| contents | To reduce the computational cost of humanoid motion generation, we introduce a new approach to representing robot kinematic reachability: the differentiable reachability map. This map is a scalar-valued function defined in the task space that takes positive values only in regions reachable by the robot's end-effector. A key feature of this representation is that it is continuous and differentiable with respect to task-space coordinates, enabling its direct use as constraints in continuous optimization for humanoid motion planning. We describe a method to learn such differentiable reachability maps from a set of end-effector poses generated using a robot's kinematic model, using either a neural network or a support vector machine as the learning model. By incorporating the learned reachability map as a constraint, we formulate humanoid motion generation as a continuous optimization problem. We demonstrate that the proposed approach efficiently solves various motion planning problems, including footstep planning, multi-contact motion planning, and loco-manipulation planning for humanoid robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation Murooka, Masaki Kumagai, Iori Morisawa, Mitsuharu Kanehiro, Fumio Robotics To reduce the computational cost of humanoid motion generation, we introduce a new approach to representing robot kinematic reachability: the differentiable reachability map. This map is a scalar-valued function defined in the task space that takes positive values only in regions reachable by the robot's end-effector. A key feature of this representation is that it is continuous and differentiable with respect to task-space coordinates, enabling its direct use as constraints in continuous optimization for humanoid motion planning. We describe a method to learn such differentiable reachability maps from a set of end-effector poses generated using a robot's kinematic model, using either a neural network or a support vector machine as the learning model. By incorporating the learned reachability map as a constraint, we formulate humanoid motion generation as a continuous optimization problem. We demonstrate that the proposed approach efficiently solves various motion planning problems, including footstep planning, multi-contact motion planning, and loco-manipulation planning for humanoid robots. |
| title | Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.11275 |