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Shared autonomy enables robots to infer user intent and assist in accomplishing it. But when the user wants to do a new task that the robot does not know about, shared autonomy will hinder their performance by attempting to assist them with something that is not their intent. Our key idea is that the robot can detect when its repertoire of intents is insufficient to explain the user’s input, and give them back control. This then enables the robot to observe unhindered task execution, learn the new intent behind it, and add it to this repertoire. We demonstrate with both a case study and a user study that our proposed method maintains good performance when the human’s intent is in the robot’s repertoire, outperforms prior shared autonomy approaches when it isn’t, and successfully learns new skills, enabling efficient lifelong learning for confidence-based shared autonomy.
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A Barrier Pair Method for Safe Human-Robot Shared Autonomy
Shared autonomy provides a framework where a human and an automated system, such as a robot, jointly control the system’s behavior, enabling an effective solution for various applications, including human-robot interaction and remote operation of a semi-autonomous system. However, a challenging problem in shared autonomy is safety because the human input may be unknown and unpredictable, which affects the robot’s safety constraints. If the human input is a force applied through physical contact with the robot, it also alters the robot’s behavior to maintain safety. We address the safety issue of shared autonomy in real-time applications by proposing a two-layer control framework. In the first layer, we use the history of human input measurements to infer what the human wants the robot to do and define the robot’s safety constraints according to that inference. In the second layer, we formulate a rapidly-exploring random tree of barrier pairs, with each barrier pair composed of a barrier function and a controller. Using the controllers in these barrier pairs, the robot is able to maintain its safe operation under the intervention from the human input. This proposed control framework allows the robot to assist the human while preventing them from encountering safety issues. We demonstrate the proposed control framework on a simulation of a two-linkage manipulator robot.
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- Award ID(s):
- 1836900
- PAR ID:
- 10284946
- Date Published:
- Journal Name:
- IEEE Conference on Decision and Control
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
