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cs.RO2026
Safe and Robust Neural Policy Learning with Statistical Verification for Sim-to-Real Deployment in Robotics
Riccardo Curcio, Hongpeng Cao, Marco Caccamo
Synthesizing safe and robust neural controllers in simulation for reliable sim-to-real deployment remains a critical challenge in robotics. Existing learning-based methods typicall…
cs.RO2025
Runtime Learning of Quadruped Robots in Wild Environments
Yihao Cai, Yanbing Mao, Lui Sha +2
This paper presents a runtime learning framework for quadruped robots, enabling them to learn and adapt safely in dynamic wild environments. The framework integrates sensing, navig…
cs.RO2024
Physics-model-guided Worst-case Sampling for Safe Reinforcement Learning
Hongpeng Cao, Yanbing Mao, Lui Sha +1
Real-world accidents in learning-enabled CPS frequently occur in challenging corner cases. During the training of deep reinforcement learning (DRL) policy, the standard setup for t…