初期画像からタスク・動作計画の行動系列を予測する深層視覚推論
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image
初期シーン画像から畳み込み再帰ネットワークで離散行動系列を直接予測し、TAMPの組合せ爆発を回避して動作計画の求解回数を大幅に削減する手法を提案。
著者: Danny Driess, Jung-Su Ha, Marc Toussaint
分類: cs.LG, cs.AI, cs.CV, cs.RO, stat.ML
原文アブストラクト
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: https://youtu.be/i8yyEbbvoEk
