Learning to Group and Grasp Multiple Objects

要約

同時に、複数のオブジェクトを把握して輸送することは、ロボットの作業効率を大幅に向上させることができ、何十年もの間重要な研究焦点となっています。
主な課題は、オブジェクトの分布とロボットのハードウェア制約を検討しながら、オブジェクトをプッシュし、グループをグループ化し、それぞれのグループの同時把持を実行する方法を決定することにあります。
従来のルールベースの方法は、多様なシナリオに柔軟に適応するのに苦労しています。
この課題に対処するために、このペーパーでは、模倣学習ベースのアプローチを提案しています。
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 transportation.
We conducted experiments to evaluate the method under different training dataset sizes, varying object quantities, and real-world object scenarios.
結果は、提案されたアプローチがマルチオブジェクトのグループ化と把握戦略を効果的かつ適応的に生成できることを示しています。
With the support of more training data, imitation learning is expected to be an effective approach for solving the multi-object grasping problem.

要約(オリジナル)

Simultaneously grasping and transporting 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 transportation. 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.

arxiv情報

提供元, 利用サービス

arxiv.jp, Google