A Deep Learning Approach to Grasping the Invisible

Y ang Y ang 1, Hengyue Liang 2 and Changhyun Choi 2 Abstract -- We introduce a new problem named "grasping the invisible", where a robot is tasked to grasp an initially invisible target object via a sequence of nonprehensile (e.g., pushing) and prehensile (e.g., grasping) actions. In this problem, nonprehensile actions are needed to search for the target and rearrange cluttered objects around it. We propose to solve the problem by formulating a deep reinforcement learning approach in an actor-critic format. A critic that maps both the visual observations and the target information to expected rewards of actions is learned via deep Q-learning for instance pushing and grasping. Two actors are proposed to take in the critic predictions and the domain knowledge for two subtasks: a Bayesian-based actor accounting for past experience performs explorational pushing to search for the target; once the target is found, a classifier-based actor coordinates the target-oriented pushing and grasping to grasp the target in clutter . The model is entirely self-supervised through the robot-environment interactions. Our system achieves 93% and 87% task success rate on the two subtasks in simulation and 85% task success rate in real robot experiments, which outperforms several baselines by large margins. Supplementary material is available at: https://sites.google.com/umn.edu/grasping-invisible. Index T erms -- Dexterous Manipulation, Deep Learning in Robotics and Automation, Computer Vision for Automation I. INTRODUCTION Imagine what happens when a young kid is looking for a specific toy block buried in clutter, as shown in Figure 1a. He or she may first push down the pile of the blocks and luckily spot the target block in clutter, then push around it to make a space for the fingers (we refer to this type of motion as "singulation" [1]) and finally grasp it. We have wondered if an intelligent agent can perform such a task.