Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from generalizing well to ever-changing environments. Moreover, collecting large datasets for robotic tasks is often impractical and expensive. To overcome these challenges, we introduce a new self-supervised neural-symbolic (NeSy) computational framework, imperative learning (IL), for robot autonomy, leveraging the generalization abilities of symbolic reasoning. The framework of IL consists of three primary components: a neural module, a reasoning engine, and a memory system. We formulate IL as a special bilevel optimization (BLO), which enables reciprocal learning over the three modules. This overcomes the label-intensive obstacles associated with data-driven approaches and takes advantage of symbolic reasoning concerning logical reasoning, physical principles, geometric analysis, etc. We discuss several optimization techniques for IL and verify their effectiveness in five distinct robot autonomy tasks including path planning, rule induction, optimal control, visual odometry, and multi-robot routing. Through various experiments, we show that IL can significantly enhance robot autonomy capabilities and we anticipate that it will catalyze further research across diverse domains.

翻译：强化学习与模仿学习等数据驱动方法在机器人自主性领域已取得显著成就。然而，其以数据为中心的特性仍阻碍其在持续变化环境中的泛化能力。此外，为机器人任务收集大规模数据集通常不切实际且成本高昂。为克服这些挑战，我们提出一种新型自监督神经符号计算框架——指令式学习，通过利用符号推理的泛化能力来提升机器人自主性。该框架包含三个核心组件：神经模块、推理引擎与记忆系统。我们将指令式学习建模为特殊的双层优化问题，实现三个模块间的相互学习机制。这种方法克服了数据驱动方法对标注数据的重度依赖，并能充分利用符号推理在逻辑推理、物理原理、几何分析等方面的优势。我们探讨了指令式学习的多种优化技术，并在路径规划、规则归纳、最优控制、视觉里程计及多机器人路由等五项机器人自主任务中验证了其有效性。通过系列实验，我们证明指令式学习能显著增强机器人自主能力，并预期该框架将推动跨领域研究的进一步发展。