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Predicting object interactions from contact distributions

Oliver Kroemer; Jan Peters
In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2014), September 14-18, Chicago, IL, USA, Pages 3361-3367, IEEE, 2014.


Contacts between objects play an important role in manipulation tasks. Depending on the locations of contacts, different manipulations or interactions can be performed with the object. By observing the contacts between two objects, a robot can learn to detect potential interactions between them. Rather than defining a set of features for modeling the contact distributions, we propose a kernel-based approach. The contact points are first modeled using a Gaussian distribution. The similarity between these distributions is computed using a kernel function. The contact distributions are then classified using kernel logistic regression. The proposed approach was used to predict stable grasps of an elongated object, as well as to construct towers out of assorted toy blocks.

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