1 citations · 2 across the 22 of their papers we have counts for
23 papers
Filtering for Liveness: Recurrence via Backup Reachability Control Barrier Functions
David E. J. van Wijk, Pio Ong, Aaron D. Ames +1
Liveness requires a dynamical system to repeatedly accomplish a desired objective over time. In this work, we consider a particular form of liveness known as recurrence, which requ…
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…
Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting
Sergio A. Esteban, Jason H. K. Siu, Derrick Mach +4
Motion retargeting approaches often prioritize kinematic similarity over whole-body dynamics, contact consistency, and actuation limits, yielding references that are difficult for…
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…
D-SafeMPC: Diffusion-Driven Safe Model Predictive Control with Discrete-Time Control Barrier Functions
Erdi Sayar, Ersin Daş, Joel W. Burdick +2
A key limitation on the use of diffusion models in robotic planning is their inability to inherently enforce safety or dynamical constraints, which often results in physically infe…
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…
Output Feedback Backup Control Barrier Functions: Safety Guarantees Under Input Bounds and State Estimation Error
David E. J. van Wijk, Tamas G. Molnar, Samuel Coogan +3
Guaranteeing the safety of controllers is vital for real-world applications, but is markedly difficult when the states are not perfectly known and when the control inputs are bound…
Probabilistic Control Barrier Functions for Systems with State Estimation Uncertainty using Sub-Gaussian Concentration
Kazuya Echigo, David E. J. van Wijk, Pol Mestres +3
Safety-critical control systems, such as spacecraft performing proximity operations, must provide formal safety guarantees despite stochastic uncertainties from state estimation an…
SafeSpace: Aggregating Safe Sets from Backup Control Barrier Functions under Input Constraints
Pio Ong, David E. J. van Wijk, Massimiliano de Sa +2
Control barrier functions (CBFs) provide a principled framework for enforcing safety in control systems -- yet the certified safe operating region in practice is often conservative…
Generalizations of Backup Control Barrier Functions: Expansion and Adaptation for Input-Bounded Safety-Critical Control
David E. J. van Wijk, Dohyun Lee, Ersin Das +3
Guaranteeing the safety of nonlinear systems with bounded inputs remains a key challenge in safe autonomy. Backup control barrier functions (bCBFs) provide a powerful mechanism for…
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…
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…
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…
Safe Payload Transfer with Ship-Mounted Cranes: A Robust Model Predictive Control Approach
Ersin Das, William A. Welch, Patrick Spieler +14
Ensuring safe real-time control of ship-mounted cranes in unstructured transportation environments requires handling multiple safety constraints while maintaining effective payload…
Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions
Ersin Das, Rahal Nanayakkara, Xiao Tan +4
Measurements and state estimates are often imperfect in control practice, posing challenges for safety-critical applications, where safety guarantees rely on accurate state informa…
AI Space Cortex: An Experimental System for Future Era Space Exploration
Thomas Touma, Ersin Daş, Erica Tevere +9
Our Robust, Explainable Autonomy for Scientific Icy Moon Operations (REASIMO) effort contributes to NASA's Concepts for Ocean worlds Life Detection Technology (COLDTech) program, w…
Uncertainty Estimators for Robust Backup Control Barrier Functions
David E. J. van Wijk, Ersin Das, Anil Alan +5
Designing safe controllers is crucial and notoriously challenging for input-constrained safety-critical control systems. Backup control barrier functions offer an approach for the…
Generative Predictive Control: Flow Matching Policies for Dynamic and Difficult-to-Demonstrate Tasks
Vince Kurtz, Joel W. Burdick
Generative control policies have recently unlocked major progress in robotics. These methods produce action sequences via diffusion or flow matching, with training data provided by…
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…
Equality Constrained Diffusion for Direct Trajectory Optimization
Vince Kurtz, Joel W. Burdick
The recent success of diffusion-based generative models in image and natural language processing has ignited interest in diffusion-based trajectory optimization for nonlinear contr…
Supervised Learning for Stochastic Optimal Control
Vince Kurtz, Joel W. Burdick
Supervised machine learning is powerful. In recent years, it has enabled massive breakthroughs in computer vision and natural language processing. But leveraging these advances for…
Bayesian Optimal Experimental Design for Robot Kinematic Calibration
Ersin Das, Thomas Touma, Joel W. Burdick
This paper develops a Bayesian optimal experimental design for robot kinematic calibration on . Our method builds upon a Gaussian process ap…