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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.07375 |
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| _version_ | 1866911258227245056 |
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| author | Han, Shaohang Verhagen, Joris Tumova, Jana |
| author_facet | Han, Shaohang Verhagen, Joris Tumova, Jana |
| contents | We study motion planning under Signal Temporal Logic (STL), a useful formalism for specifying spatial-temporal requirements. We pose STL synthesis as a trajectory optimization problem leveraging the STL robustness semantics. To obtain a differentiable problem without approximation error, we introduce an exact reformulation of the max and min operators. The resulting method is exact, smooth, and sound. We validate it in numerical simulations, demonstrating its practical performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07375 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exact Smooth Reformulations for Trajectory Optimization Under Signal Temporal Logic Specifications Han, Shaohang Verhagen, Joris Tumova, Jana Robotics We study motion planning under Signal Temporal Logic (STL), a useful formalism for specifying spatial-temporal requirements. We pose STL synthesis as a trajectory optimization problem leveraging the STL robustness semantics. To obtain a differentiable problem without approximation error, we introduce an exact reformulation of the max and min operators. The resulting method is exact, smooth, and sound. We validate it in numerical simulations, demonstrating its practical performance. |
| title | Exact Smooth Reformulations for Trajectory Optimization Under Signal Temporal Logic Specifications |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.07375 |