用扩散模型学机器人推、分组、抓取多物体，自动适应复杂场景。
Learning to Push, Group, and Grasp: A Diffusion Policy Approach for Multi-Object Delivery
- 通过遥操作收集专家数据，训练扩散策略网络生成动作序列。
- 在不同物体数量和真实场景中均实现高效多物体分组与抓取。
- 适合需要灵活处理多物体重构的机器人应用场景。
同时抓取并运送多个物体可显著提升机器人作业效率，是数十年来的研究重点。核心挑战在于如何根据物体分布和机器人硬件限制，动态决定推移、分组及同步抓取策略。传统规则方法难以适应多样场景。本文提出一种基于模仿学习的方法：通过遥操作收集一系列专家示范，训练扩散策略网络，使机器人能动态生成推、分组、抓取的动作序列，实现高效的多物体抓取与配送。我们在不同训练数据量、物体数量及真实物体场景下进行了实验评估。结果表明，该方法能有效且自适应地生成多物体分组与抓取策略。随着训练数据增加，模仿学习有望成为解决多物体抓取问题的有效途径。
原文摘要 · Abstract (English)
Simultaneously grasping and delivering multiple objects can significantly enhance robotic work efficiency and has been a key research focus for decades. The primary challenge lies in determining how to push objects, group them, and execute simultaneous grasping for respective groups while considering object distribution and the hardware constraints of the robot. Traditional rule-based methods struggle to flexibly adapt to diverse scenarios. To address this challenge, this paper proposes an imitation learning-based approach. We collect a series of expert demonstrations through teleoperation and train a diffusion policy network, enabling the robot to dynamically generate action sequences for pushing, grouping, and grasping, thereby facilitating efficient multi-object grasping and delivery. We conducted experiments to evaluate the method under different training dataset sizes, varying object quantities, and real-world object scenarios. The results demonstrate that the proposed approach can effectively and adaptively generate multi-object grouping and grasping strategies. With the support of more training data, imitation learning is expected to be an effective approach for solving the multi-object grasping problem.
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