Robots have the potential to assist people in daily tasks, such as cooking a meal. Communicating with the robots verbally and in an unstructured way is important, as spoken language is the main form of communication for humans. This paper proposes a novel framework that automatically generates robot actions from unstructured speech. The proposed framework was evaluated by collecting data from 15 participants preparing their meals while seating on a chair in a randomly disrupted environment. The system can identify and respond to a task sequence while the user may be engaged in unrelated conversations, even if the user’s speech might be unstructured and grammatically incorrect. The accuracy of the proposed system is 98.6%, which is a very promising finding.
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Towards Robot Learning from Spoken Language
The paper proposes a robot learning framework that empowers a robot to automatically generate a sequence of actions from unstructured spoken language. The robot learning framework was able to distinguish between instructions and unrelated conversations. Data were collected from 25 participants, who were asked to instruct the robot to perform a collaborative cooking task while being interrupted and distracted. The system was able to identify the sequence of instructed actions for a cooking task with the accuracy of 92.85 ± 3.87%.
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- Award ID(s):
- 2226165
- NSF-PAR ID:
- 10433568
- Date Published:
- Journal Name:
- Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’23 Companion)
- Page Range / eLocation ID:
- 112 to 116
- 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
- Conference Paper:
- https://doi.org/10.1145/3568294.3580053
