“Do this instead” – Robots that Adequately Respond to Corrected Instructions

Author:

Thierauf Christopher1,Thielstrom Ravenna1,Oosterveld Bradley2,Becker Will2,Scheutz Matthias1

Affiliation:

1. Human-Robot Interaction Lab, Tufts University, USA

2. Thinking Robots, Inc, USA

Abstract

Natural language instructions are effective at tasking autonomous robots and for teaching them new knowledge quickly. Yet, human instructors are not perfect and are likely to make mistakes at times, and will correct themselves when they notice errors in their own instructions. In this paper, we introduce a complete system for robot behaviors to handle such corrections, during both task instruction and action execution. We then demonstrate its operation in an integrated cognitive robotic architecture through spoken language in two tasks: a navigation and retrieval task and a meal assembly task. Verbal corrections occur before, during, and after verbally taught sequences of tasks, demonstrating that the proposed methods enable fast corrections not only of the semantics generated from the instructions, but also of overt robot behavior in a manner shown to be reasonable when compared to human behavior and expectations.

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Human-Computer Interaction

Reference64 articles.

1. Mattias Appelgren and Alex Lascarides. 2019. Learning Plans by Acquiring Grounded Linguistic Meanings from Corrections.. In AAMAS. 1297–1305. Mattias Appelgren and Alex Lascarides. 2019. Learning Plans by Acquiring Grounded Linguistic Meanings from Corrections.. In AAMAS. 1297–1305.

2. M. Appelgren and A. Lascarides. 2020. Interactive task learning via embodied corrective feedback. In Auton Agent Multi-Agent Syst Vol.  34. https://doi.org/10.1007/s10458-020-09481-8 10.1007/s10458-020-09481-8

3. M. Appelgren and A. Lascarides. 2020. Interactive task learning via embodied corrective feedback. In Auton Agent Multi-Agent Syst Vol.  34. https://doi.org/10.1007/s10458-020-09481-8

4. Real-time natural language corrections for assistive robotic manipulators;Broad Alexander;The International Journal of Robotics Research,2017

5. Alexander Broad , Jacob Arkin , Nathan Ratliff , Thomas Howard , Brenna Argall , and Distributed Correspondence Graph . 2016 . Towards real-time natural language corrections for assistive robots . In RSS Workshop on Model Learning for Human-Robot Communication. Alexander Broad, Jacob Arkin, Nathan Ratliff, Thomas Howard, Brenna Argall, and Distributed Correspondence Graph. 2016. Towards real-time natural language corrections for assistive robots. In RSS Workshop on Model Learning for Human-Robot Communication.

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