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.

通过引入自我监督的神经符号化 (NeSy) 计算框架，基于符号推理能力，本文提出了基于指令学习 (IL) 的新的面向机器人自主性的方法，克服了数据驱动方法在应对不断变化的环境中泛化能力差的问题，通过三个主要组件的循环学习，验证了其在路径规划、规则归纳、最优控制、视觉测距和多机器人路径规划等五个机器人自主性任务上的有效性，并预期其能够促进各个领域的进一步研究。