Physics-Based Grasp Planning Through Clutter
We propose a planning method for grasping in cluttered environments, a method where the robot can make simultaneous contact with multiple objects. With this method, the robot reaches for and grasps the target while simultaneously contacting and moving aside objects to clear a desired path. We use a physics-based analysis of pushing to compute the motion of each object in the scene in response to a set of possible robot motions. Our method enables multiple robot-object interactions, interactions that can be pre-computed and cached. However, our method avoids object-object interactions to make the problem computationally tractable. Through tests on large sets of simulated scenes, we show that our planner produces more successful grasps in more complex scenes than versions that avoid any interaction with surrounding clutter. We validate our method on a real robot, a PR2, and show that it accurately predicts the outcome of a grasp. We also show that our approach, in conjunction with state-of-the-art object recognition tools, is applicable in real-life scenes that are highly cluttered and constrained.