1 citations · 1 across the 8 of their papers we have counts for
8 papers
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…
Safe Online Learning via Smooth Safety-Structured Policy Composition
Hongpeng Cao, Liqun Zhao, Yuliang Gu +3
Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics. Existing approaches typically rely on either stri…
Observations Meet Actions: Learning Control-Sufficient Representations for Robust Policy Generalization
Yuliang Gu, Hongpeng Cao, Marco Caccamo +1
Capturing latent variations ("contexts") is key to deploying reinforcement-learning (RL) agents beyond their training regime. We recast context-based RL as a dual inference-control…
Bregman Centroid Guided Cross-Entropy Method
Yuliang Gu, Hongpeng Cao, Marco Caccamo +1
The Cross-Entropy Method (CEM) is a widely adopted trajectory optimizer in model-based reinforcement learning (MBRL), but its unimodal sampling strategy often leads to premature co…
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…
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…
Simplex-enabled Safe Continual Learning Machine
Hongpeng Cao, Yanbing Mao, Yihao Cai +2
This paper proposes the SeC-Learning Machine: Simplex-enabled safe continual learning for safety-critical autonomous systems. The SeC-learning machine is built on Simplex logic (th…
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning
Mirco Theile, Hongpeng Cao, Marco Caccamo +1
In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance. However, ensuring that the deep RL policy and…