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Title: Robotic Caregivers — Simulation and Capacitive Servoing for Physical Human-Robot Interaction
Date: Tuesday, July 6th
Time: 3 PM - 5 PM (EDT)
Location: Zoom meeting — https://cmu.zoom.us/j/95578462028?pwd=bVA2NVZGVXByTkowOGVJUEZVM3JCUT09
Meeting ID: 955 7846 2028
Passcode: 785742
Zackory Erickson
Robotics PhD Candidate
School of Electrical and Computer Engineering
Georgia Institute of Technology
Committee
Prof. Charles C. Kemp (Advisor) — Dept. of Biomedical Engineering, Georgia Institute of Technology
Prof. C. Karen Liu — Computer Science Dept., Stanford University
Prof. Sonia Chernova — School of Interactive Computing, Georgia Institute of Technology
Prof. Greg Turk — School of Interactive Computing, Georgia Institute of Technology
Prof. Pieter Abbeel — Dept. of Electrical Engineering and Computer Sciences, UC Berkeley
Abstract
Physical human-robot interaction and robotic assistance presents an opportunity to benefit the lives of many people, including the millions of older adults and people with physical disabilities, who have difficulty performing activities of daily living (ADLs) on their own. Yet, physical robotic assistance presents several challenges, including risks associated with physical human-robot interaction, difficulty sensing the human body, and a lack of tools for benchmarking and training physically assistive robots. To address each of these core challenges in robotic caregiving, this dissertation presents techniques spanning the intersection of machine learning, physics simulation, sensing, and physical human-robot interaction. First, I will introduce a method inspired by human perspective-taking which allow assistive robots to predict how their future actions will apply forces to a person's body. I will then present capacitive servoing, a new sensing technique for robots to sense human limb motion and track trajectories along the body during physical assistance. Finally, I will show how we can develop intelligent robotic caregivers via simulation and virtual reality, and I will introduce Assistive Gym, the first physics simulation framework for benchmarking and training physically assistive robots.
