DIPN: Deep Interaction Prediction Network with Application to Clutter Removal

Autor:	Huang, Baichuan, Han, Shuai D., Boularias, Abdeslam, Yu, Jingjin
Rok vydání:	2020
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	We propose a Deep Interaction Prediction Network (DIPN) for learning to predict complex interactions that ensue as a robot end-effector pushes multiple objects, whose physical properties, including size, shape, mass, and friction coefficients may be unknown a priori. DIPN "imagines" the effect of a push action and generates an accurate synthetic image of the predicted outcome. DIPN is shown to be sample efficient when trained in simulation or with a real robotic system. The high accuracy of DIPN allows direct integration with a grasp network, yielding a robotic manipulation system capable of executing challenging clutter removal tasks while being trained in a fully self-supervised manner. The overall network demonstrates intelligent behavior in selecting proper actions between push and grasp for completing clutter removal tasks and significantly outperforms the previous state-of-the-art. Remarkably, DIPN achieves even better performance on the real robotic hardware system than in simulation. Videos, code, and experiments log are available at https://github.com/rutgers-arc-lab/dipn. Comment: ICRA 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.04692 Zobrazit plný text záznamu View this record from Arxiv