Non-Prehensile Manipulation
Overview
Non-prehensile manipulation studies how robots move objects through contact without enclosing them in a grasp, enabling tasks such as precise pushing, rearrangement, transport, and throwing. Approaches include model-based planning and model predictive control, reinforcement learning, learned dynamics, and geometric or graph-based planners that combine object-level goals with feasible robot motions. Work spans tabletop pushing and stacking as well as bimanual manipulation, mobile robots, and dynamic contact tasks, with simulation-to-real transfer and hardware demonstrations increasingly central. A key challenge is making plans reliable despite uncertain friction, object properties, and contact changes while keeping motions physically executable.
Latest papers
19 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.