A Formalism for Customizing and Training Intelligent Assistive Devices

Project: Research project

Project Details

Description

Highly capable assistive robotic arms are well-poised to dramatically increase the independence of those with
severe motor impairments, by reducing their dependence on caregivers to perform select activities of daily
living. However, the more sophisticated a robotic arm, the more complicated its control. Intuitive operation
remains a challenge that only increases with task complexity, and is exasperated by limited or
low-dimensional control interfaces. Our solution is to introduce machine automation and intelligence. We
propose a formalism for customizable shared control that enables users to customize the way they share
control with intelligent assistive devices based on the user's abilities and preferences. In our formalism, the
system arbitrates between user input and the autonomous policy prediction, based on the confidence it has in
the policy’s prediction and in the user’s ability to perform the task. Moreover, the system is invisible and able
to augment minimal teleoperation interfaces (e.g. Sip-N-Puff). This point is critical for users whose own
control signals are limited, and are only able to operate minimal interfaces as a result. While dexterous
manipulation can be difficult for a user to achieve using minimal control interfaces, full robot autonomy is often
lacking in robustness or unsatisfactory for users who wish to retain some control authority. Assistive
teleoperation offers a customized and robust alternative. We propose methods for customizing system
components based on user data that can generalize to new situations. In particular, we aim to address the
questions of (i) how can the system adapt its arbitration function to a new user or task, (ii) how can a user
achieve the optimal arbitration function for a given task, (iii) how can the robot learn good policies from user
demonstration and interaction, and (iv) whether there are user-centric and/or task-centric measures of
confidence. We test the proposed methods for customizing system components in user studies with high
Spinal Cord Injury patients as well as uninjured subjects. The result will be a system that can learn from its
user and improve over time. The larger goal is assistive device—here specifically, robotic arm—operation that
is accessible to, and intuitive for, persons with extremely limited or no upper limb motor control.
StatusFinished
Effective start/end date9/1/142/28/19

Funding

  • National Institute of Biomedical Imaging and Bioengineering (5R01EB019335-03)

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