A Planning Framework for Non-Prehensile Manipulation under Clutter and Uncertainty

Abstract

Robotic manipulation systems suffer from two main problems in unstructured human environments: uncertainty and clutter. We introduce a planning framework addressing these two issues. The framework plans rearrangement of clutter using non-prehensile actions, such as pushing. Pushing actions are also used to manipulate object pose uncertainty. The framework uses an action library that is derived analytically from the mechanics of pushing and is provably conservative. The framework reduces the problem to one of combinatorial search, and demonstrates planning times on the order of seconds. With the extra functionality, our planner succeeds where traditional grasp planners fail, and works under high uncertainty by utilizing the funneling effect of pushing. We demonstrate our results with experiments in simulation and on HERB, a robotic platform developed at the Personal Robotics Lab at Carnegie Mellon University.

Integrated task and motion planning in belief space

“…The work of Dogar and Srinivasa [2012] comes closest among existing systems to satisfying our goals. They tackle manipulation of multiple objects using multiple types of primitives, including grasping and pushing.…”

Pregrasp Manipulation as Trajectory Optimization

“…The general problem of nonprehensile manipulation has also been explored at length, whether by pushing in the plane [7,19], tipping [22], pivoting [1], or caging [6]. These works focus on producing a desired change in the object of interest without considering potential subsequent prehensile manipulations.…”

Physics-Based Manipulation in Human Environments

“…Stilman et al [13] [14] perform this search using the backprojection of robot actions, starting from the final action of reaching to the goal. Our planner [1] uses backprojections of pushing actions as well as pick-and-place actions.…”