6 papers
Repairing Neural Networks for Safety in Robotic Systems using Predictive Models
Keyvan Majd, Geoffrey Clark, Georgios Fainekos +1
This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural netwo…
Enhancing State Estimation in Robots: A Data-Driven Approach with Differentiable Ensemble Kalman Filters
Xiao Liu, Geoffrey Clark, Joseph Campbell +2
This paper introduces a novel state estimation framework for robots using differentiable ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble Kalma…
Certifiably-correct Control Policies for Safe Learning and Adaptation in Assistive Robotics
Keyvan Majd, Geoffrey Clark, Tanmay Khandait +4
Guaranteeing safety in human-centric applications is critical in robot learning as the learned policies may demonstrate unsafe behaviors in formerly unseen scenarios. We present a…
Safe Robot Learning in Assistive Devices through Neural Network Repair
Keyvan Majd, Geoffrey Clark, Tanmay Khandait +4
Assistive robotic devices are a particularly promising field of application for neural networks (NN) due to the need for personalization and hard-to-model human-machine interaction…
Learning Predictive Models for Ergonomic Control of Prosthetic Devices
Geoffrey Clark, Joseph Campbell, Heni Ben Amor
We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanic…
Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking
Geoffrey Clark, Joseph Campbell, Seyed Mostafa Rezayat Sorkhabadi +2
We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing fo…