面向可泛化富接触双臂操作的规划引导扩散策略学习
Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation
AI总结：
提出 GLIDE 框架，利用基于模型的运动规划在高保真仿真中生成大规模双臂接触操作演示，并训练任务条件化扩散策略，结合关键设计缩小仿真到现实差距，实现对多样未见物体的鲁棒泛化操作。
摘要：
富接触双臂操作需要两条机械臂的精确协调，以通过策略性选择的接触和运动来改变物体状态。由于这些任务固有的复杂性，获取充足的演示数据并训练能够泛化到未见场景的策略，仍是一个在很大程度上尚未解决的挑战。基于接触规划的最新进展，我们提出了 Generalizable Planning-Guided Diffusion Policy Learning（GLIDE，可泛化规划引导扩散策略学习），该方法通过利用基于模型的运动规划器在高保真物理仿真中生成演示数据，有效学习解决富接触双臂操作任务。通过在随机化环境中进行高效规划，我们的方法为涉及多样物体和变换的任务生成了大规模、高质量的合成运动轨迹。随后，我们使用这些演示数据，通过行为克隆训练一个任务条件化扩散策略。为应对仿真到现实的差距，我们提出了一组在特征提取、任务表示、动作预测和数据增强方面的关键设计选择，使模型能够学习对平滑动作序列的鲁棒预测，并泛化到未见场景。通过仿真和真实世界中的实验，我们证明该方法能够使双臂机器人系统有效操作具有不同几何形状、尺寸和物理属性的物体。网站：https://glide-manip.github.io/
英文摘要：
Contact-rich bimanual manipulation involves precise coordination of two arms to change object states through strategically selected contacts and motions. Due to the inherent complexity of these tasks, acquiring sufficient demonstration data and training policies that generalize to unseen scenarios remain a largely unresolved challenge. Building on recent advances in planning through contacts, we introduce Generalizable Planning-Guided Diffusion Policy Learning (GLIDE), an approach that effectively learns to solve contact-rich bimanual manipulation tasks by leveraging model-based motion planners to generate demonstration data in high-fidelity physics simulation. Through efficient planning in randomized environments, our approach generates large-scale and high-quality synthetic motion trajectories for tasks involving diverse objects and transformations. We then train a task-conditioned diffusion policy via behavior cloning using these demonstrations. To tackle the sim-to-real gap, we propose a set of essential design options in feature extraction, task representation, action prediction, and data augmentation that enable learning robust prediction of smooth action sequences and generalization to unseen scenarios. Through experiments in both simulation and the real world, we demonstrate that our approach can enable a bimanual robotic system to effectively manipulate objects of diverse geometries, dimensions, and physical properties. Website: https://glide-manip.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
