Simulating Assistive Robotics Tasks

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Gangaram, Vamsee K.
Kemp, Charles C.
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In this thesis, I summarize two published research papers [1][2] to which I contributed as an undergraduate researcher. My contributions to this research primarily consisted of implementing realistic human joint limitations and better cloth visualization in Assistive Gym [1], as well as testing out various capacitive sensor designs for the multidimensional capacitive sensing work [2]. Physics-based simulation offers an opportunity for robots to learn to better provide safe and efficient assistance to people. By training robotic controllers in accurate simulations, we can drastically improve data collection and training times as compared to data collection with real robots and real people. Simulation also provides robots with a safe environment to learn, practice, and make mistakes, without having to put real people at risk. In a previous work Erickson et al. introduced Assistive Gym, a simulation framework based on the PyBullet physics engine to simulate various assistive tasks with robot and human interaction [1]. The six assistive tasks modeled are drinking, eating, itch scratching, dressing, bed bathing, and arm manipulation. We also model various human limitations as well as active human cooperation which results in better learned assistance policies. We include four common assistive robots as options for training in the six environments and show how they can be benchmarked for each assistive task. Another work from Erickson et al. on using multidimensional capacitive sensing for dressing and bathing tasks [2] is summarized, and we describe how this sensor can be modelled in simulation to incorporate into Assistive Gym in the future. Overall, Assistive Gym is shown to be an encouraging framework for training assistive robots in simulation and is open source for the research community to build upon.
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