Entropy (Oct 2023)

Incremental Learning of Goal-Directed Actions in a Dynamic Environment by a Robot Using Active Inference

  • Takazumi Matsumoto,
  • Wataru Ohata,
  • Jun Tani

DOI
https://doi.org/10.3390/e25111506
Journal volume & issue
Vol. 25, no. 11
p. 1506

Abstract

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This study investigated how a physical robot can adapt goal-directed actions in dynamically changing environments, in real-time, using an active inference-based approach with incremental learning from human tutoring examples. Using our active inference-based model, while good generalization can be achieved with appropriate parameters, when faced with sudden, large changes in the environment, a human may have to intervene to correct actions of the robot in order to reach the goal, as a caregiver might guide the hands of a child performing an unfamiliar task. In order for the robot to learn from the human tutor, we propose a new scheme to accomplish incremental learning from these proprioceptive–exteroceptive experiences combined with mental rehearsal of past experiences. Our experimental results demonstrate that using only a few tutoring examples, the robot using our model was able to significantly improve its performance on new tasks without catastrophic forgetting of previously learned tasks.

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