把持・操作のためのマルチモーダル幾何学習
Multi-Modal Geometric Learning for Grasping and Manipulation
深度と触覚情報を3D CNNで統合し、遮蔽領域を補完して物体形状を推定する手法を提案。触覚の少量追加が幾何推論と把持成功率の向上に有効であることを示した。
著者: David Watkins, Jacob Varley, Peter Allen
分類: cs.RO
原文アブストラクト
This work provides an architecture that incorporates depth and tactile information to create rich and accurate 3D models useful for robotic manipulation tasks. This is accomplished through the use of a 3D convolutional neural network (CNN). Offline, the network is provided with both depth and tactile information and trained to predict the object's geometry, thus filling in regions of occlusion. At runtime, the network is provided a partial view of an object. Tactile information is acquired to augment the captured depth information. The network can then reason about the object's geometry by utilizing both the collected tactile and depth information. We demonstrate that even small amounts of additional tactile information can be incredibly helpful in reasoning about object geometry. This is particularly true when information from depth alone fails to produce an accurate geometric prediction. Our method is benchmarked against and outperforms other visual-tactile approaches to general geometric reasoning. We also provide experimental results comparing grasping success with our method.
