Learning relational affordance models for two-arm robots


Affordances are used in robotics to model action opportunities of a robotic manipulator on an object in the environment. Previous work has shown how statistical relational learning can be used in a discrete setting to extend affordances to model relations and interactions between multiple objects being manipulated by a robotic arm and deal with environment uncertainty. In this paper, we first extend this concept of relational affordances to a continuous setting and then to a two-arm robot. A relational affordance model can first be learnt for one arm through a behavioural babbling stage, and then with the use of statistical relational learning, after constructing a symmetrical model for the other arm, two-arm manipulation actions can be modelled, where the arms can act sequentially or simultaneously. The model is evaluated in a two-arm action recognition task in a shelf object manipulation setting.

DOI: 10.1109/IROS.2014.6942964

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@article{Moldovan2014LearningRA, title={Learning relational affordance models for two-arm robots}, author={Bogdan Moldovan and Luc De Raedt}, journal={2014 IEEE/RSJ International Conference on Intelligent Robots and Systems}, year={2014}, pages={2916-2922} }