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This paper presents the design of a wearable robotic forearm for close-range human-robot collaboration. The robot's function is to serve as a lightweight supernumerary third arm for shared workspace activities. We present a functional prototype resulting from an iterative design process including several user studies. An analysis of the robot's kinematics shows an increase in reachable workspace by 246 % compared to the natural human reach. The robot's degrees of freedom and range of motion support a variety of usage scenarios with the robot as a collaborative tool, including self-handovers, fetching objects while the human's hands are occupied, assisting human-human collaboration, and stabilizing an object. We analyze the bio-mechanical loads for these scenarios and find that the design is able to operate within human ergonomic wear limits. We then report on a pilot human-robot interaction study that indicates robot autonomy is more task-time efficient and preferred by users when compared to direct voice-control. These results suggest that the design presented here is a promising configuration for a lightweight wearable robotic augmentation device, and can serve as a basis for further research into human-wearable collaboration.
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A Learning-based Adjustable Autonomy Framework for Human-Robot Collaboration
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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- Award ID(s):
- 1832110
- PAR ID:
- 10326308
- Date Published:
- Journal Name:
- IEEE Transactions on Industrial Informatics
- ISSN:
- 1551-3203
- Page Range / eLocation ID:
- 1 to 1
- 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/10.1109/TII.2022.3145567
