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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | https://arxiv.org/abs/2604.13323 |
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| _version_ | 1866913032911716352 |
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| author | Iyer, Shrutheesh R Chang, I-Chia Liu, Andrew Z. Gu, Yan Kingston, Zachary |
| author_facet | Iyer, Shrutheesh R Chang, I-Chia Liu, Andrew Z. Gu, Yan Kingston, Zachary |
| contents | Many robot planning tasks require satisfaction of one or more constraints throughout the entire trajectory. For geometric constraints, manifold-constrained motion planning algorithms are capable of planning collision-free path between start and goal configurations on the constraint submanifolds specified by task. Current state-of-the-art methods can take tens of seconds to solve these tasks for complex systems such as humanoid robots, making real-world use impractical, especially in dynamic settings. Inspired by recent advances in hardware accelerated motion planning, we present a CPU SIMD-accelerated manifold-constrained motion planner that revisits projection-based constraint satisfaction through the lens of parallelization. By transforming relevant components into parallelizable structures, we use SIMD parallelism to plan constraint satisfying solutions. Our approach achieves up to 100-1000x speed-ups over the state-of-the-art, making real-time constrained motion planning feasible for the first time. We demonstrate our planner on a real humanoid robot and show real-time whole-body quasi-static plan generation. Our work is available at https://commalab.org/papers/mcvamp/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13323 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control Iyer, Shrutheesh R Chang, I-Chia Liu, Andrew Z. Gu, Yan Kingston, Zachary Robotics Many robot planning tasks require satisfaction of one or more constraints throughout the entire trajectory. For geometric constraints, manifold-constrained motion planning algorithms are capable of planning collision-free path between start and goal configurations on the constraint submanifolds specified by task. Current state-of-the-art methods can take tens of seconds to solve these tasks for complex systems such as humanoid robots, making real-world use impractical, especially in dynamic settings. Inspired by recent advances in hardware accelerated motion planning, we present a CPU SIMD-accelerated manifold-constrained motion planner that revisits projection-based constraint satisfaction through the lens of parallelization. By transforming relevant components into parallelizable structures, we use SIMD parallelism to plan constraint satisfying solutions. Our approach achieves up to 100-1000x speed-ups over the state-of-the-art, making real-time constrained motion planning feasible for the first time. We demonstrate our planner on a real humanoid robot and show real-time whole-body quasi-static plan generation. Our work is available at https://commalab.org/papers/mcvamp/. |
| title | Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.13323 |