In this paper, we propose a deep convolutional recurrent neural network that
predicts action sequences for task and motion planning (TAMP) from an initial
scene image. Typical TAMP problems are formalized by combining reasoning on a
symbolic, discrete level (e.g. first-order logic) with continuous motion
planning such as nonlinear trajectory optimization. Due to the great
combinatorial complexity of possible discrete action sequences, a large number
of optimization/motion planning problems have to be solved to find a solution,
which limits the scalability of these approaches.
To circumvent this combinatorial complexity, we develop a neural network
which, based on an initial image of the scene, directly predicts promising
discrete action sequences such that ideally only one motion planning problem
has to be solved to find a solution to the overall TAMP problem. A key aspect
is that our method generalizes to scenes with many and varying number of
objects, although being trained on only two objects at a time. This is possible
by encoding the objects of the scene in images as input to the neural network,
instead of a fixed feature vector. Results show runtime improvements of several
magnitudes. Video: this https URL

本文提出了一种基于场景图像的深度卷积循环神经网络，可用于从一个初始化的场景图像中预测任务与运动规划中的动作序列。该网络可避免组合复杂性并具有很好的泛化性能，可加速任务与运动规划的运行时间。