Authors
- Keya Ghonasgi*
- Reuth Mirsky*
- Adrian M Haith*
- Peter Stone
- Ashish D Deshpande*
* External authors
Venue
- IROS
Date
- 2022
Share
Quantifying Changes in Kinematic Behavior of a Human-Exoskeleton Interactive System
Keya Ghonasgi*
Reuth Mirsky*
Adrian M Haith*
Ashish D Deshpande*
* External authors
IROS
2022
Abstract
While human-robot interaction studies are becoming more common, quantification of the effects of repeated interaction with an exoskeleton remains unexplored. We draw upon existing literature in human skill assessment and present extrinsic and intrinsic performance metrics that quantify how the human-exoskeleton system’s behavior changes over time. Specifically, in this paper, we present a new performance metric that provides insight into the system’s kinematics associated with ‘successful’ movements resulting in a richer characterization of changes in the system’s behavior. A human subject study is carried out wherein participants learn to play a challenging and dynamic reaching game over multiple attempts, while donning an upper-body exoskeleton. The results demonstrate that repeated practice results in learning over time as identified through the improvement of extrinsic performance. Changes in the newly developed kinematics-based measure further illuminate how the participant’s intrinsic behavior is altered over the training period. Thus, we are able to quantify the changes in the human-exoskeleton system’s behavior observed in relation with learning.
Related Publications
Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learning algorithm controls all agents in the scenario, while in ad hoc teamwork, the learning algor…
The ability to approach the same problem from different angles is a cornerstone of human intelligence that leads to robust solutions and effective adaptation to problem variations. In contrast, current RL methodologies tend to lead to policies that settle on a single solutio…
Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics. Recently, an end-to-end deep reinforcement learning agent met this challenge in a high-fidelity racing simulator, Gran Tu…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.
