11 citations · 21 across the 14 of their papers we have counts for
14 papers
VLPSA: Vision-Language-Poisson-Safe Actions for Full-Body Safety of Learned Policies
Meg Wilkinson, Emily Fourney, Joel W. Burdick +1
Vision-language-action (VLA) models enable increasingly general-purpose robotic manipulation, but such learned policies do not provide safety guarantees for collision avoidance---e…
Robust Adaptive Backup Control Barrier Functions
Ersin Daş, David E. J. van Wijk, Tamas G. Molnar +2
We propose a notion of robust adaptive backup control barrier functions for nonlinear control affine systems with parametric uncertainty in both the drift dynamics and actuation ma…
Full-Body Dynamic Safety for Robot Manipulators: 3D Poisson Safety Functions for CBF-Based Safety Filters
Meg Wilkinson, Gilbert Bahati, Ryan M. Bena +3
Collision avoidance for robotic manipulators requires enforcing full-body safety constraints in high-dimensional configuration spaces. Control Barrier Function (CBF) based safety f…
From Vertices to Convex Hulls: Certifying Set-Wise Compatibility for CBF Constraints
Shima Sadat Mousavi, Xiao Tan, Aaron D. Ames
This paper develops certificates that propagate compatibility of multiple control barrier function (CBF) constraints from sampled vertices to their convex hull. Under mild concavit…
KALIKO: Kalman-Implicit Koopman Operator Learning For Prediction of Nonlinear Dynamical Systems
Albert H. Li, Ivan Dario Jimenez Rodriguez, Joel W. Burdick +2
Long-horizon dynamical prediction is fundamental in robotics and control, underpinning canonical methods like model predictive control. Yet, many systems and disturbance phenomena…
Safety-Critical Control with Bounded Inputs: A Closed-Form Solution for Backup Control Barrier Functions
David E. J. van Wijk, Ersin Das, Tamas G. Molnar +2
Verifying the safety of controllers is critical for many applications, but is especially challenging for systems with bounded inputs. Backup control barrier functions (bCBFs) offer…
Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis
Kejun Li, Jeeseop Kim, Maxime Brunet +3
Robust bipedal locomotion in exoskeletons requires the ability to dynamically react to changes in the environment in real time. This paper introduces the hybrid data-driven predict…
Minimizing Conservatism in Safety-Critical Control for Input-Delayed Systems via Adaptive Delay Estimation
Yitaek Kim, Ersin Das, Jeeseop Kim +3
Input delays affect systems such as teleoperation and wirelessly autonomous connected vehicles, and may lead to safety violations. One promising way to ensure safety in the presenc…
Zero-Order Control Barrier Functions for Sampled-Data Systems with State and Input Dependent Safety Constraints
Xiao Tan, Ersin Das, Aaron D. Ames +1
We propose a novel zero-order control barrier function (ZOCBF) for sampled-data systems to ensure system safety. Our formulation generalizes conventional control barrier functions…
Preferential Multi-Objective Bayesian Optimization
Raul Astudillo, Kejun Li, Maegan Tucker +3
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker's latent preferences over available design choices. While preferences often involve multiple…
Constructive Safety-Critical Control: Synthesizing Control Barrier Functions for Partially Feedback Linearizable Systems
Max H. Cohen, Ryan K. Cosner, Aaron D. Ames
Certifying the safety of nonlinear systems, through the lens of set invariance and control barrier functions (CBFs), offers a powerful method for controller synthesis, provided a C…
Risk-Aware Robotics: Tail Risk Measures in Planning, Control, and Verification
Prithvi Akella, Anushri Dixit, Mohamadreza Ahmadi +5
The need for a systematic approach to risk assessment has increased in recent years due to the ubiquity of autonomous systems that alter our day-to-day experiences and their need f…
Data-Driven Predictive Control for Robust Exoskeleton Locomotion
Kejun Li, Jeeseop Kim, Xiaobin Xiong +3
Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive…
Rollover Prevention for Mobile Robots with Control Barrier Functions: Differentiator-Based Adaptation and Projection-to-State Safety
Ersin Das, Aaron D. Ames, Joel W. Burdick
This paper develops rollover prevention guarantees for mobile robots using control barrier function (CBF) theory, and demonstrates the method experimentally. We consider a safety m…