论文标题
DIPN：深层交互预测网络，并应用于杂乱
DIPN: Deep Interaction Prediction Network with Application to Clutter Removal
论文作者
论文摘要
我们提出了一个深层的交互预测网络（DIPN），以学习预测随着机器人最终效应器推动多个对象的复杂相互作用，其物理特性（包括大小，形状，质量和摩擦系数）可能是未知的。 DIPN“想象”推动作用的效果，并产生预测结果的准确合成图像。在模拟或实际机器人系统中训练时，DIPN被证明是有效的。 DIPN的高精度允许与Grasp网络直接集成，从而产生了能够执行挑战性杂物拆卸任务的机器人操纵系统，同时以完全自我监督的方式进行训练。整个网络在选择推动和掌握杂物去除任务之间选择适当的动作方面表明了智能行为，并显着超过了先前的最新动作。值得注意的是，DIPN在真正的机器人硬件系统上的性能甚至比模拟更好。视频，代码和实验日志可在https://github.com/rutgers-arc-lab/dipn上找到。
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.
