ForesightNav: Learning Scene Imagination for Efficient Exploration

2025-04-22

AI Summary

Autonomous exploration and long-horizon navigation in unknown environments remain challenging due to the lack of prior knowledge about unobserved regions. Method: This paper introduces an embodied scene imagination mechanism: a vision encoder extracts observation features to jointly predict geometric occupancy and semantic distributions in unobserved areas; navigation decisions—including goal selection—are then grounded in this “imagined” representation via reasoning. Contribution/Results: To our knowledge, this is the first work to explicitly model human-like scene imagination for embodied navigation, enabling proactive, generalizable exploration strategies. The method is trained and evaluated end-to-end on the Structured3D dataset. On the validation set, it achieves 100% PointNav success rate and 67% SPL for ObjectNav—substantially outperforming imagination-free baselines. These results empirically validate that scene imagination critically enhances cross-scene generalization in embodied navigation.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Scene Analysis & Understanding

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Understanding how humans leverage prior knowledge to navigate unseen environments while making exploratory decisions is essential for developing autonomous robots with similar abilities. In this work, we propose ForesightNav, a novel exploration strategy inspired by human imagination and reasoning. Our approach equips robotic agents with the capability to predict contextual information, such as occupancy and semantic details, for unexplored regions. These predictions enable the robot to efficiently select meaningful long-term navigation goals, significantly enhancing exploration in unseen environments. We validate our imagination-based approach using the Structured3D dataset, demonstrating accurate occupancy prediction and superior performance in anticipating unseen scene geometry. Our experiments show that the imagination module improves exploration efficiency in unseen environments, achieving a 100% completion rate for PointNav and an SPL of 67% for ObjectNav on the Structured3D Validation split. These contributions demonstrate the power of imagination-driven reasoning for autonomous systems to enhance generalizable and efficient exploration.

Problem

• Enhance robot exploration in unseen environments using imagination
• Predict occupancy and semantics for unexplored regions efficiently
• Improve navigation goal selection for autonomous robotic systems

Innovation

• Predicts occupancy and semantic details
• Enhances exploration with imagination reasoning
• Achieves high navigation completion rates