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This paper introduces a challenging object grasping task and proposes aself-supervised learning approach. The goal of the task is to grasp an objectwhich is not feasible with a single parallel gripper, but only with harnessingenvironment fixtures (e.g., walls, furniture, heavy objects). ThisSlide-to-Wall grasping task assumes no prior knowledge except the partialobservation of a target object. Hence the robot should learn an effectivepolicy given a scene observation that may include the target object,environmental fixtures, and any other disturbing objects. We formulate theproblem as visual affordances learning for which Target-Oriented Deep Q-Network(TO-DQN) is proposed to efficiently learn visual affordance maps (i.e., Q-maps)to guide robot actions. Since the training necessitates robot's exploration andcollision with the fixtures, TO-DQN is first trained safely with a simulatedrobot manipulator and then applied to a real robot. We empirically show thatTO-DQN can learn to solve the task in different environment settings insimulation and outperforms a standard and a variant of Deep Q-Network (DQN) interms of training efficiency and robustness. The testing performance in bothsimulation and real-robot experiments shows that the policy trained by TO-DQNachieves comparable performance to humans.

Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures