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Intrinsically Motivated Goal Exploration for Active Motor Learning in Robots: a Case Study

Adrien Baranes 1, * Pierre-Yves Oudeyer 1
* Corresponding author
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : We introduce the Self-Adaptive Goal Generation - Robust Intelligent Adaptive Curiosity (SAGG-RIAC) algorithm as an intrinsically motivated goal exploration mechanism which allows a redundant robot to efficiently and actively learn its inverse kinematics. The main idea is to push the robot to perform babbling in the goal/operational space, as opposed to motor babbling in the actuator space, by self-generating goals actively and adaptively in regions of the goal space which provide a maximal competence improvement for reaching those goals. Then, a lower level active motor learning algorithm, inspired by the SSA algorithm, is used to allow the robot to locally explore how to reach a given self-generated goal. We present simulated experiments in a 32 dimensional continuous sensorimotor space showing that 1) exploration in the goal space can be a lot faster than exploration in the actuator space for learning the inverse kinematics of a redundant robot; 2) selecting goals based on the maximal improvement heuristics is statistically significantly more efficient than selecting goals randomly.
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Adrien Baranes, Pierre-Yves Oudeyer. Intrinsically Motivated Goal Exploration for Active Motor Learning in Robots: a Case Study. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), 2010, Taipei, Taiwan. ⟨inria-00541769⟩

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