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Section: New Results

The SAGG-RIAC algorithm: competence based active learning of motor skills

Participants : Adrien Baranès, Pierre-Yves Oudeyer.

We have introduced the Self-Adaptive Goal Generation - Robust Intelligent Adaptive Curiosity (SAGG-RIAC) algorithm as an intrinsically motivated goal exploration mechanism which allows a redundant high-dimensional robot to efficiently and actively learn to control its own body. 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 have achieved experiments in both simulated and real robots, in various sensorimotor space (learning the inverse kinematics of a redundant arm with unknown geometry, learning quadruped walking with CPG's), showing that 1) exploration in the goal space can be a lot faster than exploration in the actuator space for learning the control of a redundant robot; 2) selecting goals based on the maximal improvement heuristics is statistically significantly more efficient than selecting goals randomly. These results were partly published in [21] .


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