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，机器人在本体、传感器和执行器方面存在显著多样性。鉴于技能集的共通性，若考虑机器人领域的广泛多样性，为每类不同机器人独立教授每种技能将导致效率低下且缺乏可扩展性。若能学习不同机器人感知运动空间之间的对应关系，则可预期一台机器人习得的技能能够更直接、更简便地迁移至其他机器人。本文提出一种方法，用于学习两台或多台可能具有不同形态的机器人之间的对应关系。具体而言，除具有不同自由度的相近形态机器人外，我们证明，在提出的框架内，可处理基于关节控制的固定基座机械臂机器人与差速驱动移动机器人。为建立所考虑机器人间的对应关系，首先通过演示向各机器人展示一项基础任务以实现相同目标。随后，在学习各自机器人策略以实现目标的同时，提取共享的潜在表征。初始学习阶段后，一台机器人对新任务执行的观察足以生成对应于其他机器人的潜在空间表征，从而完成相同任务。我们通过一系列实验验证了系统性能：包括(1)机器人需遵循相同路径完成相同任务时的对应学习，(2)机器人需采用不同轨迹完成相同任务时的对应学习，以及(3)机器人所需感知运动轨迹复杂度存在差异时的对应学习。此外，我们通过真实机械臂机器人与仿真移动机器人之间的对应学习提供了概念验证实现。