Interactive Learning Gives the Tempo to an Intrinsically Motivated Robot Learner

Sao Mai Nguyen 1 Pierre-Yves Oudeyer 1
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : This paper studies an interactive learning system that couples internally guided learning and social interaction for robot learning of motor skills. We present Socially Guided Intrinsic Motivation with Interactive learning at the Meta level (SGIM-IM), an algorithm for learning forward and inverse models in high-dimensional, continuous and non-preset environments. The robot actively self-determines: at a meta level a strategy, whether to choose active autonomous learning or social learning strategies; and at the task level a goal task in autonomous exploration. We illustrate through 2 experimental set-ups that SGIM-IM efficiently combines the advantages of social learning and intrinsic motivation to be able to produce a wide range of effects in the environment, and develop precise control policies in large spaces, while minimising its reliance on the teacher, and offering a flexible interaction framework with humans
Complete list of metadatas

Cited literature [25 references]  Display  Hide  Download

https://hal.inria.fr/hal-00762753
Contributor : Sao Mai Nguyen <>
Submitted on : Friday, December 7, 2012 - 5:32:48 PM
Last modification on : Wednesday, July 3, 2019 - 10:48:04 AM
Long-term archiving on: Saturday, December 17, 2016 - 10:36:14 PM

File

Humanoids2012Nguyen.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00762753, version 1

Collections

Citation

Sao Mai Nguyen, Pierre-Yves Oudeyer. Interactive Learning Gives the Tempo to an Intrinsically Motivated Robot Learner. IEEE-RAS International Conference on Humanoid Robots, Nov 2012, Osaka, Japan. ⟨hal-00762753⟩

Share

Metrics

Record views

424

Files downloads

260