6 papers
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…
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…