Dexterous Planar Pushing under Uncertain Object Properties: A Contact-Aware Goal-Oriented Approach

Abstract

Robotic pushing is a versatile non-prehensile manipulation skill that enables robots to handle ungraspable objects without specialized tools. This paper introduces a contact-aware, goal-oriented pushing framework that achieves dexterous and robust manipulation by explicitly allowing free-motion of the end-effector. Central to our approach is the contact-aware generalized velocity–motion model (C-GVMM), which captures the relationship between pusher velocity and slider motion across all contact modes, including separation. Unlike prior methods that rely on predefined trajectories or fixed contact-mode sequences, our framework enables seamless transitions among sticking, sliding, and separating modes. Building upon C-GVMM, we employ Model Predictive Path Integral (MPPI) control to generate goal-directed actions, and UKF-based online estimation to handle the uncertain object properties in real-world setting. We validate our approach through both numerical simulations and real-robot experiments, demonstrating that the framework accomplishes diverse pushing tasks with more optimal pusher and slider motion with high success rates. These results demonstrate the practical viability of the proposed approach for real-world robotic pushing tasks.