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Bootstrapping interactions with objects from raw sensorimotor data: a Novelty Search based approach

Carlos Maestre 1, * Antoine Cully 1, 2 Christophe Gonzales 3 Stephane Doncieux 1, 2
* Corresponding author
2 AMAC
ISIR - Institut des Systèmes Intelligents et de Robotique
3 DECISION
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Determining in advance all objects that a robot will interact with in an open environment is very challenging, if not impossible. It makes difficult the development of models that will allow to perceive and recognize objects, to interact with them and to predict how these objects will react to interactions with other objects or with the robot. Developmental robotics proposes to make robots learn by themselves such models through a dedicated exploration step. It raises a chicken-and-egg problem: the robot needs to learn about objects to discover how to interact with them and, to this end, it needs to interact with them. In this work, we propose Novelty-driven Evolutionary Babbling (NovEB), an approach enabling to bootstrap this process and to acquire knowledge about objects in the surrounding environment without requiring to include a priori knowledge about the environment, including objects, or about the means to interact with them. Our approach consists in using an evolutionary algorithm driven by a novelty criterion defined in the raw sensorimotor flow: behaviours, described by a trajectory of the robot end effector, are generated with the goal to maximize the novelty of raw perceptions. The approach is tested on a simulated PR2 robot and is compared to a random motor babbling.
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Submitted on : Wednesday, June 17, 2015 - 6:05:51 PM
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Carlos Maestre, Antoine Cully, Christophe Gonzales, Stephane Doncieux. Bootstrapping interactions with objects from raw sensorimotor data: a Novelty Search based approach. IEEE International Conference on Development and Learning and on Epigenetic Robotics, Aug 2015, Providence, United States. pp.7-12, ⟨10.1109/DEVLRN.2015.7346098⟩. ⟨hal-01164789⟩

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