5 citations · 6 across the 13 of their papers we have counts for
13 papers
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,…
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…
Bellman Value Decomposition for Task Logic in Safe Optimal Control
William Sharpless, Oswin So, Dylan Hirsch +2
Real-world tasks involve nuanced combinations of goal and safety specifications. In high dimensions, the challenge is exacerbated: formal automata become cumbersome, and the combin…
Maximizing Reach-Avoid Probabilities for Linear Stochastic Systems via Control Architectures
Niklas Schmid, Jaeyoun Choi, Oswin So +1
The maximization of reach-avoid probabilities for stochastic systems is a central topic in the control literature. Yet, the available methods are either restricted to low-dimension…
Parameter-Robust MPPI for Safe Online Learning of Unknown Parameters
Matti Vahs, Jaeyoun Choi, Niklas Schmid +2
Robots deployed in dynamic environments must remain safe even when key physical parameters are uncertain or change over time. We propose Parameter-Robust Model Predictive Path Inte…
Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL
Songyuan Zhang, Oswin So, Mitchell Black +2
Tasks for multi-robot systems often require the robots to collaborate and complete a team goal while maintaining safety. This problem is usually formalized as a constrained Markov…
Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control
Songyuan Zhang, Oswin So, Mitchell Black +1
Control policies that can achieve high task performance and satisfy safety constraints are desirable for any system, including multi-agent systems (MAS). One promising technique fo…
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…
Solving Minimum-Cost Reach Avoid using Reinforcement Learning
Oswin So, Cheng Ge, Chuchu Fan
Current reinforcement-learning methods are unable to directly learn policies that solve the minimum cost reach-avoid problem to minimize cumulative costs subject to the constraints…
Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime
Luzia Knoedler, Oswin So, Ji Yin +5
Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of di…