Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept -- reusing prior knowledge to learn in and from novel situations -- is successfully leveraged by humans to handle novel situations. In recent years, transfer learning has received renewed interest from the community from different perspectives, including imitation learning, domain adaptation, and transfer of experience from simulation to the real world, among others. In this paper, we unify the concept of transfer learning in robotics and provide the first taxonomy of its kind considering the key concepts of robot, task, and environment. Through a review of the promises and challenges in the field, we identify the need of transferring at different abstraction levels, the need of quantifying the transfer gap and the quality of transfer, as well as the dangers of negative transfer. Via this position paper, we hope to channel the effort of the community towards the most significant roadblocks to realize the full potential of transfer learning in robotics.

通过一个综述性论文，我们统一了机器人学领域中的迁移学习概念，并提供了首个考虑机器人、任务和环境关键概念的分类法，同时也强调了在实现迁移学习的全部潜力方面最重要的障碍，包括迁移的不同抽象层级，迁移差距和质量的量化需求，以及负向迁移所带来的危险。希望通过本文引导学术界在实现机器人学中迁移学习的全部潜力方面集中努力。

机器人中的迁移学习：即将到来的突破？承诺与挑战综述