Learning general-purpose models from diverse datasets has achieved great
success in machine learning. In robotics, however, existing methods in
multi-task learning are typically constrained to a single robot and workspace,
while recent work such as RT-X requires a non-trivial action normalization
procedure to manually bridge the gap between different action spaces in diverse
environments. In this paper, we propose the visual kinematics chain as a
precise and universal representation of quasi-static actions for robot learning
over diverse environments, which requires no manual adjustment since the visual
kinematic chains can be automatically obtained from the robot's model and
camera parameters. We propose the Visual Kinematics Transformer (VKT), a
convolution-free architecture that supports an arbitrary number of camera
viewpoints, and that is trained with a single objective of forecasting
kinematic structures through optimal point-set matching. We demonstrate the
superior performance of VKT over BC transformers as a general agent on Calvin,
RLBench, Open-X, and real robot manipulation tasks. Video demonstrations can be
found at this https URL

提出了一种基于视觉运动链的精确和通用的机器人学习表示方法，该方法不需要手动调整，可以从机器人的模型和相机参数自动获取。通过使用单个目标进行最优点集匹配的训练，演示了 Visual Kinematics Transformer (VKT) 在多个环境中的卓越性能。