合成データで学習した深層ネットワークと複数視点画像による不確実な穴への高速ペグ挿入
Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data
ドメインランダム化で生成した合成データを用いて穴位置を予測する深層ネットワークを学習し、視覚サーボ・スパイラル探索・インピーダンス制御を組み合わせて初期位置不確実性のあるペグ挿入を高速化した研究。
著者: Joshua C. Triyonoputro, Weiwei Wan, Kensuke Harada
分類: cs.RO
原文アブストラクト
This paper uses robots to assemble pegs into holes on surfaces with different colors and textures. It especially targets at the problem of peg-in-hole assembly with initial position uncertainty. Two in-hand cameras and a force-torque sensor are used to account for the position uncertainty. A program sequence comprising learning-based visual servoing, spiral search, and impedance control is implemented to perform the peg-in-hole task with feedback from the above sensors. Contributions are mainly made in the learning-based visual servoing of the sequence, where a deep neural network is trained with various sets of synthetic data generated using the concept of domain randomization to predict where a hole is. In the experiments and analysis section, the network is analyzed and compared, and a real-world robotic system to insert pegs to holes using the proposed method is implemented. The results show that the implemented peg-in-hole assembly system can perform successful peg-in-hole insertions on surfaces with various colors and textures. It can generally speed up the entire peg-in-hole process.
