Analogical Generalization of Actions from Single Exemplars in a Robotic Architecture

Jason R. Wilson and Evan Krause and Matthias Scheutz and Morgan Rivers

Humans are often able to generalize knowledge learned from a single exemplar. We present a novel integration of mental simulation and analogical generalization algorithms into a cognitive robotic architecture that enables a similarly rudimentary generalization capability in robots. Specifically, we show how a robot can generate variations of a given scenario and then use the results of those new scenarios run in a physics simulator to generate generalized action scripts using analogical mappings.

@inproceedings{wilson2016aamas,
  title={Analogical Generalization of Actions from Single Exemplars in a Robotic Architecture},
  author={Jason R. Wilson and Evan Krause and Matthias Scheutz and Morgan Rivers},
  year={2016},
  booktitle={Proceedings of AAMAS 2016},
  url={https://hrilab.tufts.edu/publications/wilson2016aamas.pdf}
}