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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 Shared Autonomy Approach for Wheelchair Navigation based on Learned User Preferences.
Research on robotic wheelchairs covers a broad range from complete autonomy to shared autonomy to manual navigation by a joystick or other means. Shared autonomy is valuable because it allows the user and the robot to complement each other, to correct each other's mistakes and to avoid collisions. In this paper, we present an approach that can learn to replicate path selection according to the wheelchair user's individual, often subjective, criteria in order to reduce the number of times the user has to intervene during automatic navigation. This is achieved by learning to rank paths using a support vector machine trained on selections made by the user in a simulator. If the classifier's confidence in the top ranked path is high, it is executed without requesting confirmation from the user. Otherwise, the choice is deferred to the user. Simulations and laboratory experiments using two path generation strategies demonstrate the effectiveness of our approach.
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- Award ID(s):
- 1637761
- PAR ID:
- 10041311
- Date Published:
- Journal Name:
- Fifth International Workshop on Assistive Computer Vision and Robotics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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