1 citations · 1 across the 6 of their papers we have counts for
7 papers
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…
Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions
Ji Yin, Oswin So, Eric Yang Yu +2
A common problem when using model predictive control (MPC) in practice is the satisfaction of safety specifications beyond the prediction horizon. While theoretical works have show…
Image-Caption Encoding for Improving Zero-Shot Generalization
Eric Yang Yu, Christopher Liao, Sathvik Ravi +2
Recent advances in vision-language models have combined contrastive approaches with generative methods to achieve state-of-the-art (SOTA) on downstream inference tasks like zero-sh…