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 the other robots. In this paper, we propose a method to learn correspondences between robots that have significant differences in their morphologies: a fixed-based manipulator robot with joint control and a differential drive mobile robot. For this, both robots are first given demonstrations that achieve the same tasks. A common latent representation is formed while learning the corresponding policies. After this 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 robot to achieve the same task. We verified our system in a set of experiments where the correspondence between two simulated 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 considered. 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身体结构、传感器和执行器方面存在巨大差异。鉴于技能集的共通性，若考虑机器人领域的多样性，为每个不同机器人独立教授每种技能既低效又缺乏可扩展性。如果能够学习不同机器人感知运动空间之间的对应关系，那么可以预期，在一种机器人上习得的技能将能更直接、更轻松地迁移至其他机器人。本文提出一种方法来学习形态差异显著的机器人之间的对应关系：包括固定基座关节控制机械臂机器人与差动驱动移动机器人。为此，首先让两种机器人执行相同任务的演示。在学习相应策略的过程中形成共有的潜在表征。经过初始学习阶段后，仅需观察一种机器人执行新任务，即可生成另一机器人完成相同任务所需的潜在空间表征。我们通过一组实验验证了该系统：(1) 当机器人需沿相同路径完成相同任务时；(2) 当机器人需沿不同轨迹完成相同任务时；(3) 当所需感知运动轨迹的复杂度对不同机器人存在差异时。此外，我们还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。