Hierarchical Affordance Discovery using Intrinsic Motivation

Alexandre Manoury 1, 2 Sao Mai Nguyen 2, 1 Cédric Buche 3
2 Lab-STICC_IMTA_CID_IHSEV
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_ENIB_CID_IHSEV
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : To be capable of lifelong learning in a real-life environment, robots have to tackle multiple challenges. Being able to relate physical properties they may observe in their environment to possible interactions they may have is one of them. This skill, named affordance learning, is strongly related to embodiment and is mastered through each person's development: each individual learns affordances differently through their own interactions with their surroundings. Current methods for affordance learning usually use either fixed actions to learn these affordances or focus on static setups involving a robotic arm to be operated. In this article, we propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties. We then present one experiment and analyse our system before comparing it with other approaches from reinforcement learning and affordance learning.
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02283820
Contributor : Alexandre Manoury <>
Submitted on : Wednesday, September 11, 2019 - 11:38:03 AM
Last modification on : Thursday, October 17, 2019 - 12:36:58 PM

File

HAI_2019___Camera_Ready.pdf
Files produced by the author(s)

Identifiers

Citation

Alexandre Manoury, Sao Mai Nguyen, Cédric Buche. Hierarchical Affordance Discovery using Intrinsic Motivation. 7th International Conference on Human-Agent Interaction (HAI '19), Oct 2019, Kyoto, Japan. ⟨10.1145/3349537.3351898⟩. ⟨hal-02283820⟩

Share

Metrics

Record views

65

Files downloads

154