7 citations · 17 across the 21 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
A Learning-Based Framework for Safe Human-Robot Collaboration with Multiple Backup Control Barrier Functions
Neil C. Janwani, Ersin Daş, Thomas Touma +3
Ensuring robot safety in complex environments is a difficult task due to actuation limits, such as torque bounds. This paper presents a safety-critical control framework that lever…
An Active Learning Based Robot Kinematic Calibration Framework Using Gaussian Processes
Ersin Daş, Joel W. Burdick
Future NASA lander missions to icy moons will require completely automated, accurate, and data efficient calibration methods for the robot manipulator arms that sample icy terrains…