Enabling Fast Instruction-Based Modification of Learned Robot Skills


  • Tyler Frasca Tufts University
  • Bradley Oosterveld Thinking Robots
  • Meia Chita-Tegmark Tufts University
  • Matthias Scheutz Tufts University Thinking Robots




Behavior Learning & Control, Action, Change, and Causality


Much research effort in HRI has focused on how to enable robots to learn new skills from observations, demonstrations, and instructions. Less work, however, has focused on how skills can be corrected if they were learned incorrectly, adapted to changing circumstances, or generalized/specialized to different contexts. In this paper, a skill modification framework is introduced that allows users to modify a robot’s stored skills quickly through instructions to (1) reduce inefficiencies, (2) fix errors, and (3) enable generalizations, all in a way for modified skills to be immediately available for task performance. A thorough evaluation of the implemented framework shows the operation of the algorithms integrated in a cognitive robotic architecture on different fully autonomous robots in various HRI case studies. An additional online HRI user study verifies that subjects prefer to quickly modify robot knowledge in the way we proposed in the framework.




How to Cite

Frasca, T., Oosterveld, B., Chita-Tegmark, M., & Scheutz, M. (2021). Enabling Fast Instruction-Based Modification of Learned Robot Skills. Proceedings of the AAAI Conference on Artificial Intelligence, 35(7), 6075-6083. https://doi.org/10.1609/aaai.v35i7.16757



AAAI Technical Track on Intelligent Robots