Rearrangement Planning via Heuristic Search

TL;DR

This paper introduces a heuristic search method for rearrangement planning that uses contact-based primitives to efficiently solve pushing problems, outperforming existing approaches in success rate and path length.

Contribution

The paper presents a novel heuristic search approach utilizing contact-driven primitives for rearrangement planning, improving efficiency and success over prior discretization and RRT methods.

Findings

Improved success rate over primitive-only planners.

Shorter paths achieved faster than RRT-based methods.

Effective in simulation and on a 7-DOF robotic arm.

Abstract

We present a method to apply heuristic search algorithms to solve rearrangement planning by pushing problems. In these problems, a robot must push an object through clutter to achieve a goal. To do this, we exploit the fact that contact with objects in the environment is critical to goal achievement. We dynamically generate goal-directed primitives that create and maintain contact between robot and object at each state expansion during the search. These primitives focus exploration toward critical areas of state-space, providing tractability to the high-dimensional planning problem. We demonstrate that the use of these primitives, combined with an informative yet simple to compute heuristic, improves success rate when compared to a planner that uses only primitives formed from discretizing the robot's action space. In addition, we show our planner outperforms RRT-based approaches by producing shorter paths faster. We demonstrate our algorithm both in simulation and on a 7-DOF arm pushing objects on a table.