DIJE: ロボットの自己認識と視覚サーボのための高密度画像ヤコビアン推定
DIJE: Dense Image Jacobian Estimation for Robust Robotic Self-Recognition and Visual Servoing
光学流と簡易カルマンフィルタを用いて画像の各ピクセルに対するヤコビアンをリアルタイム推定し、マーカーやロボット構造の知識なしに自己認識と視覚サーボを実現する手法を提案。
詳しい要約
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著者: Yasunori Toshimitsu, Kento Kawaharazuka, Akihiro Miki, Kei Okada, Masayuki Inaba
分類: cs.RO
原文アブストラクト
For robots to move in the real world, they must first correctly understand the state of its own body and the tools that it holds. In this research, we propose DIJE, an algorithm to estimate the image Jacobian for every pixel. It is based on an optical flow calculation and a simplified Kalman Filter that can be efficiently run on the whole image in real time. It does not rely on markers nor knowledge of the robotic structure. We use the DIJE in a self-recognition process which can robustly distinguish between movement by the robot and by external entities, even when the motion overlaps. We also propose a visual servoing controller based on DIJE, which can learn to control the robot's body to conduct reaching movements or bimanual tool-tip control. The proposed algorithms were implemented on a physical musculoskeletal robot and its performance was verified. We believe that such global estimation of the visuomotor policy has the potential to be extended into a more general framework for manipulation.
