Avoidance of an unexpected obstacle without reinforcement learning: Why not using advanced control-theoretic tools?
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909769305948160 |
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| author | Join, Cédric Fliess, Michel |
| author_facet | Join, Cédric Fliess, Michel |
| contents | This communication on collision avoidance with unexpected obstacles is motivated by some critical appraisals on reinforcement learning (RL) which "requires ridiculously large numbers of trials to learn any new task" (Yann LeCun). We use the classic Dubins' car in order to replace RL with flatness-based control, combined with the HEOL feedback setting, and the latest model-free predictive control approach. The two approaches lead to convincing computer experiments where the results with the model-based one are only slightly better. They exhibit a satisfactory robustness with respect to randomly generated mismatches/disturbances, which become excellent in the model-free case. Those properties would have been perhaps difficult to obtain with today's popular machine learning techniques in AI. Finally, we should emphasize that our two methods require a low computational burden. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03721 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Avoidance of an unexpected obstacle without reinforcement learning: Why not using advanced control-theoretic tools? Join, Cédric Fliess, Michel Systems and Control Robotics Optimization and Control This communication on collision avoidance with unexpected obstacles is motivated by some critical appraisals on reinforcement learning (RL) which "requires ridiculously large numbers of trials to learn any new task" (Yann LeCun). We use the classic Dubins' car in order to replace RL with flatness-based control, combined with the HEOL feedback setting, and the latest model-free predictive control approach. The two approaches lead to convincing computer experiments where the results with the model-based one are only slightly better. They exhibit a satisfactory robustness with respect to randomly generated mismatches/disturbances, which become excellent in the model-free case. Those properties would have been perhaps difficult to obtain with today's popular machine learning techniques in AI. Finally, we should emphasize that our two methods require a low computational burden. |
| title | Avoidance of an unexpected obstacle without reinforcement learning: Why not using advanced control-theoretic tools? |
| topic | Systems and Control Robotics Optimization and Control |
| url | https://arxiv.org/abs/2509.03721 |