Manipulation tasks often require robots to recognize interactions between objects. For example, a robot may need to determine if it has grasped an object properly or if one object is resting on another in a stable manner. These interactions usually depend on the contacts between the objects, with different distributions of contacts affording different interactions. In this paper, we address the problem of learning to recognize interactions between objects based oncontactdistributions.Wepresentakernel-basedapproach forrepresentingtheestimatedcontactdistributions.Thekernel can be used for various interactions, and it allows the robot to employ a variety of kernel methods from machine learning.Theapproachwasevaluatedonblindgrasping,lifting, and stacking tasks. Using 30 training samples and the proposed kernel, the robot already achieved classification accuracies of 71.9, 85.93, and 97.5% for the blind grasping, lifting and stacking tasks respectively. The kernel was alsousedtoclusterinteractionsusingspectralclustering.The clustering method successfully differentiated between differenttypesofinteractions,includingplacing,inserting,and pushing.Thecontactpointswereextractedusingtactilesensorsor3Dpointcloudmodelsoftheobjects.Therobotcould
constructsmalltowersofassortedblocksusingtheclassifier for the stacking task.

EB00000003552K

Available

• A kernel-based approach to learning contact distributions for robot manipulation tasks