Goal-Driven Robotic Pushing Manipulation Under Uncertain Object Properties

Abstract

Robotic pushing is one of the intuitive nonprehensile manipulation skills that can handle ungraspable objects without any complex task-specific tools. In this paper, we proposed a goal-driven accurate robotic pushing framework to achieve the robotic pushing tasks in practice that can operate under uncertain object properties. We employed a model predictive path integral (MPPI) as a goal-driven pushing controller building upon our prior work to operate pushing tasks under uncertain object properties. Unlike our prior work, the proposed framework can push the object toward the goal pose without predefined trajectories. The results of the numerical experiments demonstrated that the proposed framework can accomplish the pushing task with a significantly shorter total length, smaller total step, and a higher success rate even though the model parameters are unknown. Moreover, we demonstrated the proposed framework also works well in the real world through real-robot demonstrations.