Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between local and remote workspaces, such as object locations, it is particularly challenging to derive smooth motion mappings that fulfill different goals (e.g., picking objects with different poses on the two sides or passing through key points). Indeed, most state-of-the-art methods rely on mode switches, leading to a discontinuous, low-transparency experience. In this letter, we propose a unified formulation for position, orientation and velocity mappings based on the poses of objects of interest in the operator and robot workspaces. We apply it in the context of bilateral teleoperation. Two possible implementations to achieve the proposed mappings are studied: an iterative approach based on locally-weighted translations and rotations, and a neural network approach. Evaluations are conducted both in simulation and using two torque-controlled Franka Emika Panda robots. Our results show that, despite longer training time, the neural network approach provides faster mapping evaluations and lower interaction forces for the operator, which are crucial for continuous, real-time teleoperation.

中文翻译：

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连续双边远程操作的运动映射

将操作员的动作映射到机器人是远程操作中的关键问题。由于本地和远程工作空间之间的差异（例如对象位置），要获得实现不同目标（例如，选择两侧姿势不同或通过关键点的对象）的平滑运动映射尤其具有挑战性。实际上，大多数最先进的方法都依赖于模式切换，从而导致不连续的，低透明度的体验。在这封信中，我们基于操作员和机器人工作空间中感兴趣的对象的姿势，为位置，方向和速度映射提出了统一的公式表示。我们将其应用在双边遥操作的背景下。研究了实现建议的映射的两种可能的实现方式：一种基于局部加权平移和旋转的迭代方法，和神经网络方法。评估可以在仿真中进行，也可以使用两个扭矩控制的Franka Emika Panda机器人进行。我们的结果表明，尽管训练时间更长，但是神经网络方法为操作员提供了更快的映射评估和更低的交互作用力，这对于连续，实时的远程操作至关重要。