Decision Making in Joint Push-Grasp Action Space for Large-Scale Object Sorting
This addresses efficient manipulation in robotics for sorting tasks, but it is incremental as it builds on existing push-grasp methods with specific algorithmic improvements.
The paper tackles the problem of large-scale object sorting by developing a planner that uses both grasping and pushing actions, achieving near-optimal performance for up to 200 objects with 10 seconds of computation on a desktop machine.
We present a planner for large-scale (un)labeled object sorting tasks, which uses two types of manipulation actions: overhead grasping and planar pushing. The grasping action offers completeness guarantee under mild assumptions, and planar pushing is an acceleration strategy that moves multiple objects at once. Our main contribution is twofold: (1) We propose a bilevel planning algorithm. Our high-level planner makes efficient, near-optimal choices between pushing and grasping actions based on a cost model. Our low-level planner computes one-step greedy pushing or grasping actions. (2) We propose a novel low-level push planner that can find one-step greedy pushing actions in a semi-discrete search space. The structure of the search space allows us to efficient We show that, for sorting up to $200$ objects, our planner can find near-optimal actions with $10$ seconds of computation on a desktop machine.