Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy

TL;DR

This paper introduces Imperative Learning, a self-supervised neuro-symbolic framework for robot autonomy that combines neural networks, symbolic reasoning, and memory to improve generalization and reduce data labeling needs.

Contribution

It presents a novel bilevel optimization-based neuro-symbolic framework called Imperative Learning, integrating neural modules, reasoning, and memory for enhanced robot autonomy.

Findings

IL improves performance across five robot tasks

IL reduces dependence on labeled data

IL demonstrates significant generalization capabilities

Abstract

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.