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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.15412 |
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| _version_ | 1866912594447564800 |
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| author | Lee, Easop Moore, Samuel A. Chen, Boyuan |
| author_facet | Lee, Easop Moore, Samuel A. Chen, Boyuan |
| contents | We present Sym2Real, a fully data-driven framework that provides a principled way to train low-level adaptive controllers in a highly data-efficient manner. Using only about 10 trajectories, we achieve robust control of both a quadrotor and a racecar in the real world, without expert knowledge or simulation tuning. Our approach achieves this data efficiency by bringing symbolic regression to real-world robotics while addressing key challenges that prevent its direct application, including noise sensitivity and model degradation that lead to unsafe control. Our key observation is that the underlying physics is often shared for a system regardless of internal or external changes. Hence, we strategically combine low-fidelity simulation data with targeted real-world residual learning. Through experimental validation on quadrotor and racecar platforms, we demonstrate consistent data-efficient adaptation across six out-of-distribution sim2sim scenarios and successful sim2real transfer across five real-world conditions. More information and videos can be found at at http://generalroboticslab.com/Sym2Real |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15412 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sym2Real: Symbolic Dynamics with Residual Learning for Data-Efficient Adaptive Control Lee, Easop Moore, Samuel A. Chen, Boyuan Robotics Systems and Control We present Sym2Real, a fully data-driven framework that provides a principled way to train low-level adaptive controllers in a highly data-efficient manner. Using only about 10 trajectories, we achieve robust control of both a quadrotor and a racecar in the real world, without expert knowledge or simulation tuning. Our approach achieves this data efficiency by bringing symbolic regression to real-world robotics while addressing key challenges that prevent its direct application, including noise sensitivity and model degradation that lead to unsafe control. Our key observation is that the underlying physics is often shared for a system regardless of internal or external changes. Hence, we strategically combine low-fidelity simulation data with targeted real-world residual learning. Through experimental validation on quadrotor and racecar platforms, we demonstrate consistent data-efficient adaptation across six out-of-distribution sim2sim scenarios and successful sim2real transfer across five real-world conditions. More information and videos can be found at at http://generalroboticslab.com/Sym2Real |
| title | Sym2Real: Symbolic Dynamics with Residual Learning for Data-Efficient Adaptive Control |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2509.15412 |