Autonomous manipulation systems operating in domains where human intervention
is difficult or impossible (e.g., underwater, extraterrestrial or hazardous
environments) require a high degree of robustness to sensing and communication
failures. Crucially, motion planning and control algorithms require a stream of
accurate joint angle data provided by joint encoders, the failure of which may
result in an unrecoverable loss of functionality. In this paper, we present a
novel method for retrieving the joint angles of a robot manipulator using only
a single RGB image of its current configuration, opening up an avenue for
recovering system functionality when conventional proprioceptive sensing is
unavailable. Our approach, based on a distance-geometric representation of the
configuration space, exploits the knowledge of a robot's kinematic model with
the goal of training a shallow neural network that performs a 2D-to-3D
regression of distances associated with detected structural keypoints. It is
shown that the resulting Euclidean distance matrix uniquely corresponds to the
observed configuration, where joint angles can be recovered via
multidimensional scaling and a simple inverse kinematics procedure. We evaluate
the performance of our approach on real RGB images of a Franka Emika Panda
manipulator, showing that the proposed method is efficient and exhibits solid
generalization ability. Furthermore, we show that our method can be easily
combined with a dense refinement technique to obtain superior results.

提出了一种利用单个 RGB 图像来检索机器人手臂的关节角度的新方法，该方法基于运动规划和控制算法所需的准确联合角度数据的收集，将机器人运动与物理空间中的距离几何表示联系起来，利用机器人的运动学模型训练一个浅层神经网络来预测结构关键点间的距离，并通过多维缩小和简单的逆运动学过程来恢复关节角度。本文在 Franka Emika Panda 手臂的真实 RGB 图像上展示了该方法的高效性和稳健的泛化能力，以及该方法可以轻松地与密集的细化技术相结合以获得更好的结果。