光学式触覚センサのシミュレーションから実機への転移
Sim-to-Real Transfer for Optical Tactile Sensing
TacTip光学触覚センサの柔軟体シミュレーションをUnityで構築し、ドメインランダム化により実機データなしで実世界のエッジ位置・角度を1mm未満の誤差で予測できることを示した。
著者: Zihan Ding, Nathan F. Lepora, Edward Johns
分類: cs.RO
原文アブストラクト
Deep learning and reinforcement learning methods have been shown to enable learning of flexible and complex robot controllers. However, the reliance on large amounts of training data often requires data collection to be carried out in simulation, with a number of sim-to-real transfer methods being developed in recent years. In this paper, we study these techniques for tactile sensing using the TacTip optical tactile sensor, which consists of a deformable tip with a camera observing the positions of pins inside this tip. We designed a model for soft body simulation which was implemented using the Unity physics engine, and trained a neural network to predict the locations and angles of edges when in contact with the sensor. Using domain randomisation techniques for sim-to-real transfer, we show how this framework can be used to accurately predict edges with less than 1 mm prediction error in real-world testing, without any real-world data at all.
