48 citations · 192 across the 38 of their papers we have counts for
21 papers · 1 filter
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…
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…
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…
Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric
Dawei Sun, Mohammad Javad Khojasteh, Shubhanshu Shekhar +1
In this paper, we consider the problem of using a robot to explore an environment with an unknown, state-dependent disturbance function while avoiding some forbidden areas. The goa…
Optimal Mixed Discrete-Continuous Planning for Linear Hybrid Systems
Jingkai Chen, Brian Williams, Chuchu Fan
Planning in hybrid systems with both discrete and continuous control variables is important for dealing with real-world applications such as extra-planetary exploration and multi-v…
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances
Jingkai Chen, Jiaoyang Li, Chuchu Fan +1
We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and o…
Reactive motion planning with probabilistic safety guarantees
Yuxiao Chen, Ugo Rosolia, Chuchu Fan +2
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots. This paper considers th…
Learning Certified Control using Contraction Metric
Dawei Sun, Susmit Jha, Chuchu Fan
In this paper, we solve the problem of finding a certified control policy that drives a robot from any given initial state and under any bounded disturbance to the desired referenc…