27 citations · 56 across the 20 of their papers we have counts for
6 papers · 1 filter
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…