Publikation
Offline and active gradient-based learning strategies in a pushing scenario
Sergio Roa; Geert-Jan Kruijff
In: Proceedings of the 3rd International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems. International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems (ERLARS-2010), 19th European Conference on Artificial Intelligence ECAI, August 16, Lisbon, Portugal, 2010.
Zusammenfassung
When operating in the real world, a robot needs to accurately
predict the consequences of its own actions. This is important to
guide its own behavior, and in adapting it based on feedback from
the environment. The paper focuses on a specific problem in this
context, namely predicting affordances of simple geometrical objects
called polyflaps. A machine learning approach is presented for
acquiring models of object movement, resulting from a robot
performing pushing actions on a polyflap. Long Short-Term Memory
machines (LSTMs) are used to deal with the inherent spatiotemporal
nature of this problem. An LSTM is a gradient-based model of a
Recurrent Neural Network, and can successively predict a sequence of
feature vectors. The paper discusses offline experiments to test
the ability of LSTMs to solve the prediction problem considered
here. Cross-validation methods are applied as a measure of
convergence performance. An active learning method based on
Intelligent Adaptive Curiosity is also applied for improving the
learning performance of learners trained offline, generating a
combination of learners specialized in different sensorimotor spaces
after the knowledge transfer.