Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field

2026-06-29

AI Summary

This work addresses the limitations of traditional non-prehensile manipulation methods, which rely on predefined target poses and struggle in real-world scenarios with diverse object configurations and unknown goals. The authors introduce the concept of a “graspability field,” reformulating manipulation as the optimization of an object-centric scalar field that quantifies graspability. This approach enables a closed-loop policy to autonomously reconfigure objects into graspable states without requiring predefined target poses or manual termination criteria. Leveraging reinforcement learning, the method jointly trains a policy network and a graspability field predictor using synthetic grasping data, achieving end-to-end integrated manipulation and grasping control. Experiments demonstrate that the proposed strategy reliably enhances object graspability in both simulation and real-robot settings, with predicted graspability distances strongly correlating with actual grasp success rates.

Abstract

Non-prehensile manipulation is often used as a preparatory step for robotic grasping, yet existing approaches typically require a predefined target object pose. In practice, however, objects admit multiple graspable configurations and the desired pose is not known in advance. We reformulate non-prehensile manipulation for grasping as optimizing an object centric graspability objective rather than reaching a specific pose. We construct a graspable set from synthesized grasps and define a graspability field that measures how suitable an object configuration is for successful grasp execution. The scalar measure provides a dense learning signal for reinforcement learning and determines when to terminate manipulation. This yields a closed-loop manipulation-to-grasp pipeline driven by a single policy. Experiments in simulation and on a real robot show that the policy reliably reconfigures objects into graspable states and transitions to grasping without external planners or manually specified stopping conditions. The predicted graspability distance correlates with real world grasp success, which indicates that the learned representation captures grasp feasibility of object configurations.

Problem

• non-prehensile manipulation
• graspability
• object pose
• robotic grasping
• grasp planning

Innovation

• graspability field
• non-prehensile manipulation
• reinforcement learning
• grasp-oriented manipulation
• closed-loop grasping