27 citations · 56 across the 20 of their papers we have counts for
20 papers
Characterizing Job Power Elasticity for Power-Flexible AI Training
Philip Colangelo, Charles Dawson, Shayan Sengupta +2
Large language model (LLM) training is among the fastest-growing sources of electricity demand in modern data centers, and power availability is a primary bottleneck to continued A…
A Roadmap for Climate-Relevant Robotics Research
Alan Papalia, Charles Dawson, Laurentiu L. Anton +25
Climate change is one of the defining challenges of the 21st century, and many in the robotics community are looking for ways to contribute. This paper presents a roadmap for clima…
Rare event modeling with self-regularized normalizing flows: what can we learn from a single failure?
Charles Dawson, Van Tran, Max Z. Li +1
Increased deployment of autonomous systems in fields like transportation and robotics have seen a corresponding increase in safety-critical failures. These failures can be difficul…
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
Allen M. Wang, Alessandro Pau, Cristina Rea +12
The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advance…
RADIUM: Predicting and Repairing End-to-End Robot Failures using Gradient-Accelerated Sampling
Charles Dawson, Anjali Parashar, Chuchu Fan
Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of…
Active Disruption Avoidance and Trajectory Design for Tokamak Ramp-downs with Neural Differential Equations and Reinforcement Learning
Allen M. Wang, Oswin So, Charles Dawson +3
The tokamak offers a promising path to fusion energy, but plasma disruptions pose a major economic risk, motivating considerable advances in disruption avoidance. This work develop…
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…
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help
Charles Dawson, Chuchu Fan
To ensure safe, reliable operation of the electrical grid, we must be able to predict and mitigate likely failures. This need motivates the classic security-constrained AC optimal…
A Bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling
Charles Dawson, Chuchu Fan
Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of…
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…
Chance-Constrained Trajectory Optimization for High-DOF Robots in Uncertain Environments
Charles Dawson, Ashkan Jasour, Andreas Hofmann +1
Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly im…
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…
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…
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…
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…
Provably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstacles
Charles Dawson, Ashkan Jasour, Andreas Hofmann +1
Real-world environments are inherently uncertain, and to operate safely in these environments robots must be able to plan around this uncertainty. In the context of motion planning…
Fast Certification of Collision Probability Bounds with Uncertain Convex Obstacles
Charles Dawson, Andreas Hofmann, Brian Williams
To operate reactively in uncertain environments, robots need to be able to quickly estimate the risk that they will collide with their environment. This ability is important for bo…