1 citations · 1 across the 6 of their papers we have counts for
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LASER: Latent Space Adjoint Matching for Support-Constrained Entropy-Regularized Offline RL
Songyuan Zhang, Oswin So, Eric Yang Yu +3
While offline reinforcement learning (RL) enables policy optimization from static datasets without costly online interaction, it remains bottlenecked by the risk of executing out-o…
LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials
Oswin So, Eric Yu, Chuchu Fan
Control barrier functions (CBF) are a popular safety filter to ensure safety for nonlinear dynamical systems. However, when the system is subject to uncertainties and disturbances,…
SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing
Anjali Parashar, Yingke Li, Eric Yang Yu +4
As autonomous systems such as drones, become increasingly deployed in high-stakes, human-centric domains, it is critical to evaluate the ethical alignment since failure to do so im…
Solving Parameter-Robust Avoid Problems with Unknown Feasibility using Reinforcement Learning
Oswin So, Eric Yang Yu, Songyuan Zhang +3
Recent advances in deep reinforcement learning (RL) have achieved strong results on high-dimensional control tasks, but applying RL to reachability problems raises a fundamental mi…
ReFORM: Reflected Flows for On-support Offline RL via Noise Manipulation
Songyuan Zhang, Oswin So, H. M. Sabbir Ahmad +4
Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed dataset generated by behavior policies without additional environment interactions. One common cha…