好奇心駆動による未知物体の触覚自己教師あり探索
Curiosity Driven Self-supervised Tactile Exploration of Unknown Objects
触覚のみを頼りに未知物体を探索し、6秒以内に形状を再構成するtSLAMを提案。家庭用品の暗黙的理解を活用し、ADROITハンドで汎用性を実証した。
著者: Yujie Lu, Jianren Wang, Vikash Kumar
分類: cs.RO
原文アブストラクト
Intricate behaviors an organism can exhibit is predicated on its ability to sense and effectively interpret complexities of its surroundings. Relevant information is often distributed between multiple modalities, and requires the organism to exhibit information assimilation capabilities in addition to information seeking behaviors. While biological beings leverage multiple sensing modalities for decision making, current robots are overly reliant on visual inputs. In this work, we want to augment our robots with the ability to leverage the (relatively under-explored) modality of touch. To focus our investigation, we study the problem of scene reconstruction where touch is the only available sensing modality. We present Tactile Slam (tSLAM) -- which prepares an agent to acquire information seeking behavior and use implicit understanding of common household items to reconstruct the geometric details of the object under exploration. Using the anthropomorphic `ADROIT' hand, we demonstrate that tSLAM is highly effective in reconstructing objects of varying complexities within 6 seconds of interactions. We also established the generality of tSLAM by training only on 3D Warehouse objects and testing on ContactDB objects.
