近似物理シミュレータで学習する産業用バラ積みピッキング
Learning Based Industrial Bin-picking Trained with Approximate Physics Simulator
衝突判定を近似した物理シミュレータでCNN向けの学習データを効率的に収集し、ランダムに積み重なった物体の把持姿勢を学習する手法を提案した。正確な3Dモデルで深度画像を生成すれば近似の影響を緩和できることを示した。
著者: Ryo Matsumura, Kensuke Harada, Yukiyasu Domae, Weiwei Wan
分類: cs.RO
原文アブストラクト
In this research, we tackle the problem of picking an object from randomly stacked pile. Since complex physical phenomena of contact among objects and fingers makes it difficult to perform the bin-picking with high success rate, we consider introducing a learning based approach. For the purpose of collecting enough number of training data within a reasonable period of time, we introduce a physics simulator where approximation is used for collision checking. In this paper, we first formulate the learning based robotic bin-picking by using CNN (Convolutional Neural Network). We also obtain the optimum grasping posture of parallel jaw gripper by using CNN. Finally, we show that the effect of approximation introduced in collision checking is relaxed if we use exact 3D model to generate the depth image of the pile as an input to CNN.
