In this paper, an adjustable autonomy framework is proposed for Human-Robot Collaboration (HRC) in which a robot uses a reinforcement learning mechanism guided by a human operator's rewards in an initially unknown workspace. Within the proposed framework, the robot can adjust its autonomy level in an HRC setting that is represented by a Markov Decision Process. A novel Q-learning mechanism with an integrated greedy approach is implemented for robot learning to capture the correct actions and the robot's mistakes for adjusting its autonomy level. The proposed HRC framework can adapt to changes in the workspace, and can adjust the autonomy level, provided consistent human operator's reward. The developed algorithm is applied to a realistic HRC setting, involving a Baxter humanoid robot. The experimental results confirm the capability of the developed framework to successfully adjust the robot's autonomy level in response to changes in the human operator's commands or the workspace.
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A Trust-Assist Framework for Human–Robot Co-Carry Tasks
Robots are increasingly being employed for diverse applications where they must work and coexist with humans. The trust in human–robot collaboration (HRC) is a critical aspect of any shared-task performance for both the human and the robot. The study of a human-trusting robot has been investigated by numerous researchers. However, a robot-trusting human, which is also a significant issue in HRC, is seldom explored in the field of robotics. Motivated by this gap, we propose a novel trust-assist framework for human–robot co-carry tasks in this study. This framework allows the robot to determine a trust level for its human co-carry partner. The calculations of this trust level are based on human motions, past interactions between the human–robot pair, and the human’s current performance in the co-carry task. The trust level between the human and the robot is evaluated dynamically throughout the collaborative task, and this allows the trust to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Additionally, the proposed framework can enable the robot to generate and perform assisting movements to follow human-carrying motions and paces when the human is considered trustworthy in the co-carry task. The results of our experiments suggest that the robot effectively assists the human in real-world collaborative tasks through the proposed trust-assist framework.
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- PAR ID:
- 10423874
- Date Published:
- Journal Name:
- Robotics
- Volume:
- 12
- Issue:
- 2
- ISSN:
- 2218-6581
- Page Range / eLocation ID:
- 30
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/robotics12020030
