BiNoMaP: Learning Category-Level Bimanual Non-Prehensile Manipulation Primitives

Non-prehensile manipulation, encompassing ungraspable actions such as pushing, poking, and pivoting, represents a critical yet underexplored domain in robotics due to its contact-rich and analytically intractable nature. In this work, we revisit this problem from two novel perspectives. First, we move beyond the usual single-arm setup and the strong assumption of favorable external dexterity such as walls, ramps, or edges. Instead, we advocate a generalizable dual-arm configuration and establish a suite of Bimanual Non-prehensile Manipulation Primitives (BiNoMaP). Second, we depart from the prevailing RL-based paradigm and propose a three-stage, RL-free framework to learn non-prehensile skills. Specifically, we begin by extracting bimanual hand motion trajectories from video demonstrations. Due to visual inaccuracies and morphological gaps, these coarse trajectories are difficult to transfer directly to robotic end-effectors. To address this, we propose a geometry-aware post-optimization algorithm that refines raw motions into executable manipulation primitives that conform to specific motion patterns. Beyond instance-level reproduction, we further enable category-level generalization by parameterizing the learned primitives with object-relevant geometric attributes, particularly size, resulting in adaptable and general parameterized manipulation primitives. We validate BiNoMaP across a range of representative bimanual tasks and diverse object categories, demonstrating its effectiveness, efficiency, versatility, and superior generalization capability. Non-prehensile manipulation refers to a class of robotic actions that do not rely on firm grasping but instead leverage physical interactions such as poking, or pivoting, or pushing to achieve manipulation goals Zhou et al. (2019); Hogan & Rodriguez (2020); Sun et al. (2020); Zhou & Held (2023); Zhang et al. (2023). These skills are not merely complementary to traditional grasp-based tasks; they are often essential in scenarios where grasping is physically infeasible or inefficient. In dual-arm robotic systems Liu et al. (2022); Wu & Kruse (2024); Y amada et al. (2025); Lu et al. (2025), non-prehensile manipulation becomes especially relevant when dealing with objects that are too fragile, too flat, or lack sufficient geometry for reliable grasping. Despite its importance, current non-prehensile manipulation faces two core bottlenecks.