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

2024-06-23

arXiv.org

AI Summary

Robot learning suffers from poor generalization and heavy reliance on labeled data. Method: This paper proposes Imperative Learning (IL), a self-supervised neuro-symbolic framework that embeds logic-, physics-, and geometry-based symbolic reasoning into a bilevel optimization (BLO) architecture. IL integrates a differentiable symbolic reasoning engine, neural modules, and a memory system to enable real-time, bidirectional neuro-symbolic co-learning—without human annotations. Contribution/Results: Key innovations include a unified neuro-symbolic architecture, a self-supervised memory mechanism, and a multimodal task adapter supporting path planning, rule induction, and optimal control. Evaluated on five representative robotic tasks, IL achieves significant zero-shot transfer improvements, reduces training data requirements by over 70%, and enhances both reasoning robustness and interpretability.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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, collecting large datasets 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.

Problem

• Robot Learning
• Adaptability
• Data Efficiency

Innovation

• Imperative Learning
• Adaptive Environment
• Collaborative Robots