48 citations · 192 across the 38 of their papers we have counts for
7 papers · 1 filter
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…
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
Yongchao Chen, Jacob Arkin, Charles Dawson +3
For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language. Recent advances in large language mod…
Solving Stabilize-Avoid Optimal Control via Epigraph Form and Deep Reinforcement Learning
Oswin So, Chuchu Fan
Tasks for autonomous robotic systems commonly require stabilization to a desired region while maintaining safety specifications. However, solving this multi-objective problem is ch…
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…
Hybrid Systems Neural Control with Region-of-Attraction Planner
Yue Meng, Chuchu Fan
Hybrid systems are prevalent in robotics. However, ensuring the stability of hybrid systems is challenging due to sophisticated continuous and discrete dynamics. A system with all…
ConBaT: Control Barrier Transformer for Safe Policy Learning
Yue Meng, Sai Vemprala, Rogerio Bonatti +2
Large-scale self-supervised models have recently revolutionized our ability to perform a variety of tasks within the vision and language domains. However, using such models for aut…
Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Approach Using Control Barrier Functions
Ji Yin, Charles Dawson, Chuchu Fan +1
Model Predictive Path Integral (MPPI) control is a type of sampling-based model predictive control that simulates thousands of trajectories and uses these trajectories to synthesiz…