无需标记或结构信息，实时估计像素级图像雅可比，实现机器人自识别与视觉伺服。
DIJE: Dense Image Jacobian Estimation for Robust Robotic Self-Recognition and Visual Servoing
- 基于光流与简化卡尔曼滤波，全图实时计算像素级图像雅可比。
- 在运动重叠时仍能准确区分机器人自身动作与外部干扰。
- 适用于无标记、非结构化环境下的机器人自识别与双臂操作控制。
为使机器人在真实世界中移动，必须准确理解其自身状态及所持工具。本文提出DIJE算法，用于估算每个像素的图像雅可比。该方法基于光流计算和简化的卡尔曼滤波器，可在整幅图像上高效实时运行，无需依赖标记或机器人结构先验知识。我们将DIJE应用于自识别过程，可在运动重叠情况下稳健区分机器人自身运动与外部干扰。此外，提出一种基于DIJE的视觉伺服控制器，能够学习控制机器人本体完成抓取动作或双手工具端点控制。算法在具身肌肉骨骼机器人上实现并验证性能。我们认为，这种全局视觉-运动策略估计具有扩展为通用操作框架的潜力。
原文摘要 · Abstract (English)
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.
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