高解像度触覚センシングを統合した把持安定性予測
Integrating High-Resolution Tactile Sensing into Grasp Stability Prediction
視覚・深度・高解像度触覚を組み合わせて把持安定性を予測するニューラルネットをTACTOシミュレータで訓練し、既知物体では高精度だが未知物体への汎化が課題であることを示した。
著者: Lachlan Chumbley, Morris Gu, Rhys Newbury, Jurgen Leitner, Akansel Cosgun
分類: cs.RO
原文アブストラクト
We investigate how high-resolution tactile sensors can be utilized in combination with vision and depth sensing, to improve grasp stability prediction. Recent advances in simulating high-resolution tactile sensing, in particular the TACTO simulator, enabled us to evaluate how neural networks can be trained with a combination of sensing modalities. With the large amounts of data needed to train large neural networks, robotic simulators provide a fast way to automate the data collection process. We expand on the existing work through an ablation study and an increased set of objects taken from the YCB benchmark set. Our results indicate that while the combination of vision, depth, and tactile sensing provides the best prediction results on known objects, the network fails to generalize to unknown objects. Our work also addresses existing issues with robotic grasping in tactile simulation and how to overcome them.
