40 citations · 43 across the 6 of their papers we have counts for
7 papers
Foundational World Models Accurately Detect Bimanual Manipulator Failures
Isaac R. Ward, Michelle Ho, Houjun Liu +7
Deploying visuomotor robots at scale is challenging due to the potential for anomalous failures to degrade performance, cause damage, or endanger human life. Bimanual manipulators…
Learning Robot Safety from Sparse Human Feedback using Conformal Prediction
Aaron O. Feldman, Joseph A. Vincent, Maximilian Adang +2
Ensuring robot safety can be challenging; user-defined constraints can miss edge cases, policies can become unsafe even when trained from safe data, and safety can be subjective. T…
How Generalizable Is My Behavior Cloning Policy? A Statistical Approach to Trustworthy Performance Evaluation
Joseph A. Vincent, Haruki Nishimura, Masha Itkina +3
With the rise of stochastic generative models in robot policy learning, end-to-end visuomotor policies are increasingly successful at solving complex tasks by learning from human d…
Guarantees on Robot System Performance Using Stochastic Simulation Rollouts
Joseph A. Vincent, Aaron O. Feldman, Mac Schwager
We provide finite-sample performance guarantees for control policies executed on stochastic robotic systems. Given an open- or closed-loop policy and a finite set of trajectory rol…
Reachable Polyhedral Marching (RPM): An Exact Analysis Tool for Deep-Learned Control Systems
Joseph A. Vincent, Mac Schwager
Neural networks are increasingly used in robotics as policies, state transition models, state estimation models, or all of the above. With these components being learned from data,…
DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning
Javier Yu, Joseph A. Vincent, Mac Schwager
We present a distributed algorithm that enables a group of robots to collaboratively optimize the parameters of a deep neural network model while communicating over a mesh network.…
Reachable Polyhedral Marching (RPM): A Safety Verification Algorithm for Robotic Systems with Deep Neural Network Components
Joseph A. Vincent, Mac Schwager
We present a method for computing exact reachable sets for deep neural networks with rectified linear unit (ReLU) activation. Our method is well-suited for use in rigorous safety a…