# Human and Machine Learning for Customized Control of Assistive Robots

> **NIH NIH R01** · REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB · 2023 · $1

## Abstract

PROJECT SUMMARY
This application will result in a technological platform that re-empowers persons with severe paralysis, by allowing them
to independently control a wide spectrum of robotic actions. Severe paralysis is devastating, and chronic—and reliance
on caregivers is persistent. Assistive machines such as wheelchairs and robotic arms offer a groundbreaking path to
independence: where control over their environment and interactions is returned to the person.
 However, to operate complex machines like robotic arms and hands typically poses a difficult learning challenge and
requires complex control signals—and the commercial control interfaces accessible to persons with severe paralysis
(e.g. sip-and-puff, switch-based head arrays) are not adequate. As a result, assistive robotic arms remain largely
inaccessible to those with severe paralysis—arguably the population who would benefit from them most.
 The purpose of the proposed study is to provide people with tetraplegia with the means to control robotic arms with
their available body mobility, while concurrently promoting the exercise of available body motions and the maintenance
of physical health. Control interfaces that generate a unique map from a user's body motions to control signals for
a machine offer a customized interaction, however these interfaces have only been used to issue low-dimensional
(2-D) control signals whereas more complex machines require higher-dimensional (e.g. 6-D) signals. We propose
an approach that leverages robotics autonomy and machine learning in order to aid the end-user in learning how to
issue effective higher-dimensional control signals through body motions. Specifically, initially the human issues a lower-
dimensional control signal and robotics autonomy is used to bridge the gap by taking over whatever is not covered by
the human's control signal. Help from the robotics autonomy is then progressively scaled back, automatically, to cover
fewer and fewer control dimensions as the user becomes more skilled.
 The first piece to our approach deals with how to extract control signals from the human, using the body-machine
interface. The development and optimization of decoding procedures for controlling a robotic arm using residual body
motions will be addressed under Specific Aim 1. The second piece to our approach deals with how to interpret control
signals from a human within a paradigm that shares control between the human and robotics autonomy. To identify
which shared-control formulations most effectively utilize the human's control signals will be the topic of Specific Aim 2.
The final piece to our approach deals with how to adapt the shared-control paradigm so that more control is transferred
to the human over time. This adaptation is necessary for the human's learning process, since the goal in the end is for
the human to be able to fully control the robotic arm him/herself, and will be assessed under Specific Aim 3.
 At the completion of this project, tetra...

## Key facts

- **NIH application ID:** 10468598
- **Project number:** 5R01EB024058-04
- **Recipient organization:** REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB
- **Principal Investigator:** Brenna Argall
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1
- **Award type:** 5
- **Project period:** 2018-09-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10468598

## Citation

> US National Institutes of Health, RePORTER application 10468598, Human and Machine Learning for Customized Control of Assistive Robots (5R01EB024058-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10468598. Licensed CC0.

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