48 citations · 192 across the 38 of their papers we have counts for
38 papers
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…
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Ruipeng Zhang, Chenning Yu, Jingkai Chen +2
Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of plan…
Density Planner: Minimizing Collision Risk in Motion Planning with Dynamic Obstacles using Density-based Reachability
Laura Lützow, Yue Meng, Andres Chavez Armijos +1
Uncertainty is prevalent in robotics. Due to measurement noise and complex dynamics, we cannot estimate the exact system and environment state. Since conservative motion planners a…
Barrier functions enable safety-conscious force-feedback control
Charles Dawson, Austin Garrett, Falk Pollok +2
In order to be effective partners for humans, robots must become increasingly comfortable with making contact with their environment. Unfortunately, it is hard for robots to distin…
Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning
Yue Meng, Zeng Qiu, Md Tawhid Bin Waez +1
Density of the reachable states can help understand the risk of safety-critical systems, especially in situations when worst-case reachability is too conservative. Recent work prov…
Enforcing safety for vision-based controllers via Control Barrier Functions and Neural Radiance Fields
Mukun Tong, Charles Dawson, Chuchu Fan
To navigate complex environments, robots must increasingly use high-dimensional visual feedback (e.g. images) for control. However, relying on high-dimensional image data to make c…
Certifiable Robot Design Optimization using Differentiable Programming
Charles Dawson, Chuchu Fan
There is a growing need for computational tools to automatically design and verify autonomous systems, especially complex robotic systems involving perception, planning, control, a…
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Jingkai Chen, Jiaoyang Li, Yijiang Huang +7
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.…
Robust Counterexample-guided Optimization for Planning from Differentiable Temporal Logic
Charles Dawson, Chuchu Fan
Signal temporal logic (STL) provides a powerful, flexible framework for specifying complex autonomy tasks; however, existing methods for planning based on STL specifications have d…
Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods
Charles Dawson, Sicun Gao, Chuchu Fan
Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics, but this performance comes at the cost of reduced t…
Multi-agent Motion Planning from Signal Temporal Logic Specifications
Dawei Sun, Jingkai Chen, Sayan Mitra +1
We tackle the challenging problem of multi-agent cooperative motion planning for complex tasks described using signal temporal logic (STL), where robots can have nonlinear and nonh…
SABLAS: Learning Safe Control for Black-box Dynamical Systems
Zengyi Qin, Dawei Sun, Chuchu Fan
Control certificates based on barrier functions have been a powerful tool to generate probably safe control policies for dynamical systems. However, existing methods based on barri…
Learning Safe, Generalizable Perception-based Hybrid Control with Certificates
Charles Dawson, Bethany Lowenkamp, Dylan Goff +1
Many robotic tasks require high-dimensional sensors such as cameras and Lidar to navigate complex environments, but developing certifiably safe feedback controllers around these se…
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
Charles Dawson, Zengyi Qin, Sicun Gao +1
Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models. We devel…
A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques Slotine +1
This paper presents a theoretical overview of a Neural Contraction Metric (NCM): a neural network model of an optimal contraction metric and corresponding differential Lyapunov fun…
Learning Density Distribution of Reachable States for Autonomous Systems
Yue Meng, Dawei Sun, Zeng Qiu +2
State density distribution, in contrast to worst-case reachability, can be leveraged for safety-related problems to better quantify the likelihood of the risk for potentially hazar…
Reactive and Safe Road User Simulations using Neural Barrier Certificates
Yue Meng, Zengyi Qin, Chuchu Fan
Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications. In this work, we proposed a reactive agent model which can en…
Density Constrained Reinforcement Learning
Zengyi Qin, Yuxiao Chen, Chuchu Fan
We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by…