Rearrangement Planning via Heuristic Search
This work addresses a domain-specific problem in robotics for more efficient object manipulation in cluttered environments, representing an incremental improvement over prior methods.
The paper tackled the problem of rearrangement planning for robots pushing objects through clutter by developing a heuristic search method that dynamically generates goal-directed primitives to maintain contact, resulting in improved success rates and faster, shorter paths compared to existing planners.
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.