Guiding Co-Adaptation in Physically Interacting Human-Robot Teams

Guiding Co-Adaptation in Physically Interacting Human-Robot Teams

Flipse, Lorenzo (TU Delft Mechanical, Maritime and Materials Engineering)

Abbink, D.A. (mentor)
Beckers, N.W.M. (mentor)
Grammatico, S. (mentor)
Bianchi, M. (mentor)

Delft University of Technology

2021-12-16

To make the cooperation within a physical human-robot team as efficient as possible, the team members must be able to co-adapt.
We developed and evaluated a robot that adapts to a human, using an adaptation strategy, in such a way as to guide the co-adaptation to have a positive effect on human task contribution and team performance.
A novel adaptive control algorithm for the robot was designed, estimating and adapting to the human control, using a Nash equilibrium to compute the robot's control inputs. Stability of the controller was theoretically proven, and validated using physical tests.
Two robot adaptation strategies, positive and negative reinforcement, were compared in an experiment in which 18 participants participated. The negative reinforcement adaptation strategy provides assistance on an assist-as-needed basis, whereas the positive reinforcement strategy is designed to intrinsically motivate humans to contribute to the control task.
Results show a significant increase in performance in the negative reinforcement adaptation strategy compared to the positive reinforcement adaptation strategy, whereas both conditions show a significant increase in performance compared to manual control. Results additionally show a significant decrease in both estimated (by the robot) and perceived (by the human) control share in the negative reinforcement adaptation strategy compared to the positive reinforcement adaptation strategy.
In conclusion, to guide the co-adaptation to both increase performance and engage humans to actively contribute to a control task, a robot should be designed to adapt using a positive adaptation strategy.

http://resolver.tudelft.nl/uuid:33dfa6b0-beaf-4491-b10a-b6ec28fcfef0

Student theses

master thesis

© 2021 Lorenzo Flipse