Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910227483328512 |
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| author | Lemmel, Julian Resch, Felix Farsang, Mónika Hasani, Ramin Rus, Daniela Grosu, Radu |
| author_facet | Lemmel, Julian Resch, Felix Farsang, Mónika Hasani, Ramin Rus, Daniela Grosu, Radu |
| contents | We study online fine-tuning of pretrained control policies for autonomous driving using Real-Time Recurrent Reinforcement Learning (RTRRL), a memory-efficient algorithm that updates policy parameters at every time step without backpropagation through time. We extend RTRRL to support LrcSSM, a recently proposed nonlinear diagonal state-space model, and combine offline behavioral cloning with online RTRRL fine-tuning to adapt policies to distribution shifts at deployment. We validate the approach in the CarRacing simulation and on a 1:10-scale RoboRacer platform equipped with an event camera, where a pretrained policy is fine-tuned online during real-world line-following. To our knowledge, this is the first demonstration of online RL fine-tuning with event-camera observations on standard (non-spiking) hardware in closed-loop control. LrcSSM-based policies improve fastest and most consistently across both settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02236 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Control in Autonomous Driving via Real-Time Recurrent RL Lemmel, Julian Resch, Felix Farsang, Mónika Hasani, Ramin Rus, Daniela Grosu, Radu Robotics Machine Learning Neural and Evolutionary Computing Systems and Control We study online fine-tuning of pretrained control policies for autonomous driving using Real-Time Recurrent Reinforcement Learning (RTRRL), a memory-efficient algorithm that updates policy parameters at every time step without backpropagation through time. We extend RTRRL to support LrcSSM, a recently proposed nonlinear diagonal state-space model, and combine offline behavioral cloning with online RTRRL fine-tuning to adapt policies to distribution shifts at deployment. We validate the approach in the CarRacing simulation and on a 1:10-scale RoboRacer platform equipped with an event camera, where a pretrained policy is fine-tuned online during real-world line-following. To our knowledge, this is the first demonstration of online RL fine-tuning with event-camera observations on standard (non-spiking) hardware in closed-loop control. LrcSSM-based policies improve fastest and most consistently across both settings. |
| title | Adaptive Control in Autonomous Driving via Real-Time Recurrent RL |
| topic | Robotics Machine Learning Neural and Evolutionary Computing Systems and Control |
| url | https://arxiv.org/abs/2602.02236 |