41 citations · 62 across the 32 of their papers we have counts for
9 papers · 1 filter
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,…
Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer
Jaeyoun Choi, Oswin So, Songyuan Zhang +2
Multi-drone payload transportation has emerged as a promising research paradigm with potential applications in construction, logistics, and disaster response. However, the complex…
Value Functions for Temporal Logic: Optimal Policies and Safety Filters
Oswin So, William Sharpless, Sylvia Herbert +1
While Bellman equations for basic reach, avoid, and reach-avoid problems are well studied, the relationship between value optimality and policy optimality becomes subtle in the und…
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…
Discrete Adjoint Matching
Oswin So, Brian Karrer, Chuchu Fan +2
Computation methods for solving entropy-regularized reward optimization -- a class of problems widely used for fine-tuning generative models -- have advanced rapidly. Among those,…