Active Affordance Learning in Continuous State and Action Spaces

Active Affordance Learning in Continuous State and Action Spaces

Wang, C.
Hindriks, K.V.
Babuska, R.

Electrical Engineering, Mathematics and Computer Science

Intelligent Systems

2014-11-18

Learning object affordances and manipulation skills is essential for developing cognitive service robots. We propose an active affordance learning approach in continuous state and action spaces without manual discretization of states or exploratory motor primitives. During exploration in the action space, the robot learns a forward model to predict action effects. It simultaneously updates the active exploration policy through reinforcement learning, whereby the prediction error serves as the intrinsic reward. By using the learned forward model, motor skills are obtained in a bottom-up manner to achieve goal states of an object. We demonstrate that a humanoid robot NAO is able to learn how to manipulate garbage cans with different lids by using different motor skills.

http://resolver.tudelft.nl/uuid:59069258-f7aa-4ab2-8805-4303a97c62dc

Active Learning in Robotics Workshop at IEEE-RAS International Conference on Humanoid Robots, Madrid, Spain, 18 November 2014

Institutional Repository

conference paper

(c) 2014 The Author(s)