We observe a large variety of robots in terms of their bodies, sensors, and actuators. Given the commonalities in the skill sets, teaching each skill to each different robot independently is inefficient and not scalable when the large variety in the robotic landscape is considered. If we can learn the correspondences between the sensorimotor spaces of different robots, we can expect a skill that is learned in one robot can be more directly and easily transferred to other robots. In this paper, we propose a method to learn correspondences among two or more robots that may have different morphologies. To be specific, besides robots with similar morphologies with different degrees of freedom, we show that a fixed-based manipulator robot with joint control and a differential drive mobile robot can be addressed within the proposed framework. To set up the correspondence among the robots considered, an initial base task is demonstrated to the robots to achieve the same goal. Then, a common latent representation is learned along with the individual robot policies for achieving the goal. After the initial learning stage, the observation of a new task execution by one robot becomes sufficient to generate a latent space representation pertaining to the other robots to achieve the same task. We verified our system in a set of experiments where the correspondence between robots is learned (1) when the robots need to follow the same paths to achieve the same task, (2) when the robots need to follow different trajectories to achieve the same task, and (3) when complexities of the required sensorimotor trajectories are different for the robots. We also provide a proof-of-the-concept realization of correspondence learning between a real manipulator robot and a simulated mobile robot.

翻译：我们观察到机器人种类繁多，其本体、传感器和执行器各不相同。考虑到技能集的共通性，若针对每种不同机器人独立教授每项技能，在机器人种类繁多的背景下效率低下且难以扩展。若能学习不同机器人感觉运动空间之间的对应关系，则可预期在一台机器人上学会的技能能够更直接、更容易地迁移至其他机器人。本文提出一种学习两个或多个可能具有不同形态的机器人之间对应关系的方法。具体而言，除了具有相似形态但自由度数不同的机器人外，我们展示了一种基于关节控制的固定基座机械臂机器人和差分驱动移动机器人可在所提框架内被统一处理。为建立所考虑机器人之间的对应关系，首先通过示教让各机器人完成初始基础任务以实现相同目标。随后学习共同隐空间表征及个体机器人策略以达到该目标。初始学习阶段后，仅需观察一台机器人执行新任务，即可生成其他机器人实现相同任务所需的隐空间表征。我们通过一系列实验验证了该系统：包括(1)机器人需沿相同路径完成相同任务；(2)机器人需沿不同轨迹完成相同任务；(3)各机器人所需感觉运动轨迹复杂度不同等情形。此外，我们提供了真实机械臂机器人与仿真移动机器人之间对应学习的概念验证实现。