1 citations · 1 across the 12 of their papers we have counts for
6 papers · 1 filter
Explicit Control Barrier Function-based Safety Filters and their Resource-Aware Computation
Pol Mestres, Shima Sadat Mousavi, Pio Ong +4
This paper studies the efficient implementation of safety filters that are designed using control barrier functions (CBFs), which minimally modify a nominal controller to render it…
Architecture Is All You Need: Diversity-Enabled Sweet Spots for Robust Humanoid Locomotion
Blake Werner, Lizhi Yang, Aaron D. Ames
Robust humanoid locomotion in unstructured environments requires architectures that balance fast low-level stabilization with slower perceptual decision-making. We show that a simp…
CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions
Lizhi Yang, Blake Werner, Massimiliano de Sa +1
Reinforcement learning (RL), while powerful and expressive, can often prioritize performance at the expense of safety. Yet safety violations can lead to catastrophic outcomes in re…
Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Ryan M. Bena, Gilbert Bahati, Blake Werner +3
Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular,…
Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models
Lizhi Yang, Blake Werner, Adrian B. Ghansah +1
Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unified framework for walking control…
SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics
Lizhi Yang, Blake Werner, Ryan K. Cosner +3
Robot learning has produced remarkably effective ``black-box'' controllers for complex tasks such as dynamic locomotion on humanoids. Yet ensuring dynamic safety, i.e., constraint…