9 citations · 31 across the 25 of their papers we have counts for
8 papers · 1 filter
Multimodal Safe Control for Human-Robot Interaction
Ravi Pandya, Tianhao Wei, Changliu Liu
Generating safe behaviors for autonomous systems is important as they continue to be deployed in the real world, especially around people. In this work, we focus on developing a no…
Absolute Policy Optimization
Weiye Zhao, Feihan Li, Yifan Sun +3
In recent years, trust region on-policy reinforcement learning has achieved impressive results in addressing complex control tasks and gaming scenarios. However, contemporary state…
Robust Safe Control with Multi-Modal Uncertainty
Tianhao Wei, Liqian Ma, Ravi Pandya +1
Safety in dynamic systems with prevalent uncertainties is crucial. Current robust safe controllers, designed primarily for uni-modal uncertainties, may be either overly conservativ…
Learn With Imagination: Safe Set Guided State-wise Constrained Policy Optimization
Yifan Sun, Feihan Li, Weiye Zhao +3
Deep reinforcement learning (RL) excels in various control tasks, yet the absence of safety guarantees hampers its real-world applicability. In particular, explorations during lear…
State-wise Constrained Policy Optimization
Weiye Zhao, Rui Chen, Yifan Sun +2
Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with saf…
GUARD: A Safe Reinforcement Learning Benchmark
Weiye Zhao, Yifan Sun, Feihan Li +4
Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-critical real-world applications, such as autonomous driving, human-robot interactio…
Zero-shot Transferable and Persistently Feasible Safe Control for High Dimensional Systems by Consistent Abstraction
Tianhao Wei, Shucheng Kang, Ruixuan Liu +1
Safety is critical in robotic tasks. Energy function based methods have been introduced to address the problem. To ensure safety in the presence of control limits, we need to desig…
State-wise Safe Reinforcement Learning: A Survey
Weiye Zhao, Tairan He, Rui Chen +2
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges. A major con…