Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
This work addresses the problem of poor generalization and high labeling costs in robot autonomy for robotics researchers, presenting a novel framework that could catalyze further research.
The paper tackles the challenge of data-driven methods in robot autonomy by introducing a self-supervised neuro-symbolic framework called imperative learning, which uses symbolic reasoning to improve generalization and reduce reliance on labeled data, achieving significant enhancements in tasks like path planning and optimal control.
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, labeling data for robotic tasks is often impractical and expensive. To overcome these challenges, we introduce a new self-supervised neuro-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.