activity
20212023
most citedSim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees

39 citations · 79 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY2023

Fast, Smooth, and Safe: Implicit Control Barrier Functions through Reach-Avoid Differential Dynamic Programming

Athindran Ramesh Kumar, Kai-Chieh Hsu, Peter J. Ramadge +1

Safety is a central requirement for autonomous system operation across domains. Hamilton-Jacobi (HJ) reachability analysis can be used to construct "least-restrictive" safety filte…

eess.SY2023★ 1 cited

Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics

Haimin Hu, Kensuke Nakamura, Kai-Chieh Hsu +2

We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided. Dynamic non-cooperative games have been…

cs.LG2022★ 1 cited

ISAACS: Iterative Soft Adversarial Actor-Critic for Safety

Kai-Chieh Hsu, Duy Phuong Nguyen, Jaime Fernández Fisac

The deployment of robots in uncontrolled environments requires them to operate robustly under previously unseen scenarios, like irregular terrain and wind conditions. Unfortunately…

cs.RO2022★ 39 cited

Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees

Kai-Chieh Hsu, Allen Z. Ren, Duy Phuong Nguyen +2

Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using rei…

cs.LG2021★ 38 cited

Safety and Liveness Guarantees through Reach-Avoid Reinforcement Learning

Kai-Chieh Hsu, Vicenç Rubies-Royo, Claire J. Tomlin +1

Reach-avoid optimal control problems, in which the system must reach certain goal conditions while staying clear of unacceptable failure modes, are central to safety and liveness a…