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Poster
Assistive Teaching of Motor Control Tasks to Humans
Megha Srivastava · Erdem Biyik · Suvir Mirchandani · Noah Goodman · Dorsa Sadigh

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #227
in Poster Session 6 »

Recent works on shared autonomy and assistive-AI technologies, such as assistive robotic teleoperation, seek to model and help human users with limited ability in a fixed task. However, these approaches often fail to account for humans' ability to adapt and eventually learn how to execute a control task themselves. Furthermore, in applications where it may be desirable for a human to intervene, these methods may have inhibited their ability to learn how to succeed with full self-control. In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft. Despite their ubiquitous role in humans' daily activities and occupations, motor tasks are rarely taught in a uniform way due to their high complexity and variance. We propose an AI-assisted teaching algorithm that leverages skill discovery methods from reinforcement learning (RL) literature to (i) break down any motor control task into teachable skills, (ii) construct novel drill sequences, and (iii) individualize curricula to students with different capabilities. Through an extensive mix of synthetic and user studies on two motor control tasks - parking a car with a joystick and writing characters from the Balinese alphabet - we show that assisted teaching with skills improve student performance by around 40% compared to practicing full trajectories without skills, and practicing with individualized drills can result in up to 25% further improvement.

Keywords: Reinforcement Learning · human-AI interaction · shared autonomy · education · teaching
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Megha Srivastava (Stanford University)
Erdem Biyik (Stanford University)

Erdem Biyik is a PhD candidate in Electrical Engineering at Stanford University. He is working on AI for Robotics in Intelligent and Interactive Autonomous Systems Group (ILIAD), and advised by Prof. Dorsa Sadigh. His research interests are: machine learning, artificial intelligence (AI), and their applications for human-robot interaction and multi-agent systems. He also works on AI and optimization for autonomous driving and traffic management. Before coming to Stanford, Erdem was an undergraduate student in the Department of Electrical and Electronics Engineering at Bilkent University, where he worked in Imaging and Computational Neuroscience Laboratory (ICON Lab) in National Magnetic Resonance Research Center under the supervision of Prof. Tolga Çukur with a focus on compressed sensing reconstructions, coil compression, and bSSFP banding suppression in MRI. He also worked on generalized approximate message passing algorithms as an intern in Prof. Rudiger Urbanke's Communication Theory Laboratory (LTHC) in EPFL, under the supervision of Dr. Jean Barbier, for a summer.

Suvir Mirchandani (Stanford University)
Noah Goodman (Stanford University)
Dorsa Sadigh (Stanford)

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