Self-Predictive Representation for Autonomous UAV Object-Goal Navigation

2026-04-22

AI Summary

This work addresses the challenges of low sample efficiency and perceptual-decision difficulties in unmanned aerial vehicle navigation toward targets with unknown locations in 3D object environments. To this end, the authors propose an end-to-end navigation framework that integrates state representation learning with an Actor-Critic reinforcement learning architecture. The core innovation lies in the design of a novel self-predictive perception model, AmelPred, along with its stochastic variant, AmelPredSto, which substantially enhances the self-supervised learning capability of the perception module. Experimental results demonstrate that AmelPredSto significantly improves both sample efficiency and navigation success rate in 3D object goal navigation tasks, outperforming existing state-of-the-art methods.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation

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
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Autonomous Unmanned Aerial Vehicles (UAVs) have revolutionized industries through their versatility with applications including aerial surveillance, search and rescue, agriculture, and delivery. Their autonomous capabilities offer unique advantages, such as operating in large open space environments. Reinforcement Learning (RL) empowers UAVs to learn intricate navigation policies, enabling them to optimize flight behavior autonomously. However, one of its main challenge is the inefficiency in using data sample to achieve a good policy. In object-goal navigation (OGN) settings, target recognition arises as an extra challenge. Most UAV-related approaches use relative or absolute coordinates to move from an initial position to a predefined location, rather than to find the target directly. This study addresses the data sample efficiency issue in solving a 3D OGN problem, in addition to, the formalization of the unknown target location setting as a Markov decision process. Experiments are conducted to analyze the interplay of different state representation learning (SRL) methods for perception with a model-free RL algorithm for planning in an autonomous navigation system. The main contribution of this study is the development of the perception module, featuring a novel self-predictive model named AmelPred. Empirical results demonstrate that its stochastic version, AmelPredSto, is the best-performing SRL model when combined with actor-critic RL algorithms. The obtained results show substantial improvement in RL algorithms' efficiency by using AmelPredSto in solving the OGN problem.

Problem

• Object-Goal Navigation
• Sample Efficiency
• Autonomous UAV
• Markov Decision Process
• 3D Navigation

Innovation

• Self-Predictive Representation
• Object-Goal Navigation
• Sample Efficiency
• AmelPredSto
• UAV Autonomy