ArticleEntropy (Basel, Switzerland)2023
Incremental Learning of Goal-Directed Actions in a Dynamic Environment by a Robot Using Active Inference.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Intermittent Active Inference.Entropy (Basel, Switzerland) · 2026Article
- Five Fristonian Formulae.Entropy (Basel, Switzerland) · 2025Article
- Active Inference with Dynamic Planning and Information Gain in Continuous Space by Inferring Low-Dimensional Latent States.Entropy (Basel, Switzerland) · 2025Article
- The Many Roles of Precision in Action.Entropy (Basel, Switzerland) · 2024Review
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Authors and funding
3 authors.
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Abstract
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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Registered trials
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