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cs.RO2026
Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier +1
Autonomous systems have achieved superhuman performance in isolation or simulation, yet they remain brittle in shared, dynamic real-world spaces. This failure stems from the domina…
cs.RO2026
What Matters for Simulation to Online Reinforcement Learning on Real Robots
Yarden As, Dhruva Tirumala, René Zurbrügg +4
We investigate what specific design choices enable successful online reinforcement learning (RL) on physical robots. Across 100 real-world training runs on three distinct robotic p…
cs.RO2025
Exploiting Policy Idling for Dexterous Manipulation
Annie S. Chen, Philemon Brakel, Antonia Bronars +7
Learning-based methods for dexterous manipulation have made notable progress in recent years. However, learned policies often still lack reliability and exhibit limited robustness…