Unsupervised learning of predictive parts for cross-object grasp transfer

We present a principled solution to the problem of transferring grasps across objects. Our approach identifies, through autonomous exploration, the size and shape of object parts that consistently predict the applicability of a grasp across multiple objects. The robot can then use these parts to plan grasps onto novel objects. By contrast to most recent methods, we aim to solve the part-learning problem without the help of a human teacher. The robot collects training data autonomously by exploring different grasps on its own. The core principle of our approach is an intensive encoding of low-level sensorimotor uncertainty with probabilistic models, which allows the robot to generalize the noisy autonomously-generated grasps. Object shape, which is our main cue for predicting grasps, is encoded with surface densities, that model the spatial distribution of points that belong to an object's surface. Grasp parameters are modeled with grasp densities, that correspond to the spatial distribution of object-relative gripper poses that lead to a grasp. The size and shape of grasp-predicting parts are identified by sampling the cross-object correlation of local shape and grasp parameters. We approximate sampling and integrals via Monte Carlo methods to make our computer implementation tractable. We demonstrate the applicability of our method in simulation. A proof of concept on a real robot is also provided.