41 citations · 48 across the 13 of their papers we have counts for
14 papers
Unified Overview of Safety Certificate Functions: Techniques from Hamilton--Jacobi Reachability and Control Barrier Functions
Hao Wang, Max Cohen, Zeyuan Feng +9
Safety is a central concern in modern autonomous systems. Safety certificates---functions whose existence guarantees that a system meets its safety specification---have long been u…
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…
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…
Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems
H. M. Sabbir Ahmad, Ehsan Sabouni, Alexander Wasilkoff +6
We address the problem of safe policy learning in multi-agent safety-critical autonomous systems. In such systems, it is necessary for each agent to meet the safety requirements at…
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…
Foundation Models to the Rescue: Deadlock Resolution in Connected Multi-Robot Systems
Kunal Garg, Songyuan Zhang, Jacob Arkin +1
Connected multi-agent robotic systems (MRS) are prone to deadlocks in an obstacle environment where the robots can get stuck away from their desired locations under a smooth low-le…
GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control
Songyuan Zhang, Oswin So, Kunal Garg +1
Distributed, scalable, and safe control of large-scale multi-agent systems is a challenging problem. In this paper, we design a distributed framework for safe multi-agent control i…
Learning Safe Control for Multi-Robot Systems: Methods, Verification, and Open Challenges
Kunal Garg, Songyuan Zhang, Oswin So +2
In this survey, we review the recent advances in control design methods for robotic multi-agent systems (MAS), focussing on learning-based methods with safety considerations. We st…
Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control
Songyuan Zhang, Kunal Garg, Chuchu Fan
We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agen…
State-Compute Replication: Parallelizing High-Speed Stateful Packet Processing
Qiongwen Xu, Sebastiano Miano, Xiangyu Gao +6
With the slowdown of Moore's law, CPU-oriented packet processing in software will be significantly outpaced by emerging line speeds of network interface cards (NICs). Single-core p…
Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration
Songyuan Zhang, Chuchu Fan
Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that can hardly be accurately modeled with…
Compositional Neural Certificates for Networked Dynamical Systems
Songyuan Zhang, Yumeng Xiu, Guannan Qu +1
Developing stable controllers for large-scale networked dynamical systems is crucial but has long been challenging due to two key obstacles: certifiability and scalability. In this…