Differentiable Motion Manifold Primitives for Reactive Motion Generation under Kinodynamic Constraints
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908463040299008 |
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| author | Lee, Yonghyeon |
| author_facet | Lee, Yonghyeon |
| contents | Real-time motion generation -- which is essential for achieving reactive and adaptive behavior -- under kinodynamic constraints for high-dimensional systems is a crucial yet challenging problem. We address this with a two-step approach: offline learning of a lower-dimensional trajectory manifold of task-relevant, constraint-satisfying trajectories, followed by rapid online search within this manifold. Extending the discrete-time Motion Manifold Primitives (MMP) framework, we propose Differentiable Motion Manifold Primitives (DMMP), a novel neural network architecture that encodes and generates continuous-time, differentiable trajectories, trained using data collected offline through trajectory optimizations, with a strategy that ensures constraint satisfaction -- absent in existing methods. Experiments on dynamic throwing with a 7-DoF robot arm demonstrate that DMMP outperforms prior methods in planning speed, task success, and constraint satisfaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12193 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Differentiable Motion Manifold Primitives for Reactive Motion Generation under Kinodynamic Constraints Lee, Yonghyeon Robotics Artificial Intelligence Real-time motion generation -- which is essential for achieving reactive and adaptive behavior -- under kinodynamic constraints for high-dimensional systems is a crucial yet challenging problem. We address this with a two-step approach: offline learning of a lower-dimensional trajectory manifold of task-relevant, constraint-satisfying trajectories, followed by rapid online search within this manifold. Extending the discrete-time Motion Manifold Primitives (MMP) framework, we propose Differentiable Motion Manifold Primitives (DMMP), a novel neural network architecture that encodes and generates continuous-time, differentiable trajectories, trained using data collected offline through trajectory optimizations, with a strategy that ensures constraint satisfaction -- absent in existing methods. Experiments on dynamic throwing with a 7-DoF robot arm demonstrate that DMMP outperforms prior methods in planning speed, task success, and constraint satisfaction. |
| title | Differentiable Motion Manifold Primitives for Reactive Motion Generation under Kinodynamic Constraints |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2410.12193 |