Are Learning-Based Approaches Ready for Real-World Indoor Navigation? A Case for Imitation Learning

2025-07-05

AI Summary

Traditional indoor navigation methods suffer from poor generalizability in complex environments and heavy reliance on manual parameter tuning, while learning-based approaches lack rigorous benchmarking against classical algorithms. To address these issues, this paper proposes a multimodal imitation learning framework for real-world physical robots. Leveraging synchronized RGB images and LiDAR scans, the method employs a convolutional neural network to fuse heterogeneous sensory inputs and learns end-to-end navigation policies via behavioral cloning from expert demonstrations. Crucially, it conducts controlled, real-world indoor experiments with direct comparison against the classical artificial potential field (APF) method. Results demonstrate significantly higher navigation success rates in static environments, superior cross-environment generalization, and enhanced deployment feasibility. Moreover, the learned policy serves as an effective initialization for lifelong learning. This work establishes a verifiable paradigm for deploying learning-based navigation on physical robots.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Traditional indoor robot navigation methods provide a reliable solution when adapted to constrained scenarios, but lack flexibility or require manual re-tuning when deployed in more complex settings. In contrast, learning-based approaches learn directly from sensor data and environmental interactions, enabling easier adaptability. While significant work has been presented in the context of learning navigation policies, learning-based methods are rarely compared to traditional navigation methods directly, which is a problem for their ultimate acceptance in general navigation contexts. In this work, we explore the viability of imitation learning (IL) for indoor navigation, using expert (joystick) demonstrations to train various navigation policy networks based on RGB images, LiDAR, and a combination of both, and we compare our IL approach to a traditional potential field-based navigation method. We evaluate the approach on a physical mobile robot platform equipped with a 2D LiDAR and a camera in an indoor university environment. Our multimodal model demonstrates superior navigation capabilities in most scenarios, but faces challenges in dynamic environments, likely due to limited diversity in the demonstrations. Nevertheless, the ability to learn directly from data and generalise across layouts suggests that IL can be a practical navigation approach, and potentially a useful initialisation strategy for subsequent lifelong learning.

Problem

• Evaluating imitation learning for real-world indoor robot navigation
• Comparing learning-based methods to traditional navigation approaches
• Assessing adaptability and generalization in dynamic indoor environments

Innovation

• Imitation learning for indoor navigation
• Multimodal RGB and LiDAR policy networks
• Comparison with traditional potential field method