IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations

• URL: http://arxiv.org/abs/2606.08029v1
• Date: Sat, 06 Jun 2026 07:45:19 GMT
• Title: IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations
• Abstract summary: We propose a framework that learns human-like ObjectNav policies from human demonstrations.<n>IntentNav achieves state-of-the-art performance on the MP3D, HM3D-v1 and HM3D-v2 ObjectNav benchmarks.
• Score: 58.921283404811085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object navigation requires a robot to search for an unobserved target in an unknown environment by deciding where to explore next under partial observability. Effective search resembles human-like exploration: selectively probing visually promising frontiers while relying on spatial memory to avoid redundant revisits. We propose IntentNav, a spatial-visual imitation framework that learns human-like ObjectNav policies from human demonstrations. To infer high-level search intent from low-level human actions, we introduce Frontier-based Human-Intent Labeling, which looks ahead in human demonstrations and labels the frontier that best explains the demonstrator's future search direction. We construct a spatial-visual candidate space, where BEV memory tracks explored regions, unexplored frontiers, and trajectory history, while egocentric visual memory provides semantic cues for each candidate. A VLM policy is trained to select among these grounded candidates, using Intent-Aligned Objective to encourage consistent and human-like exploration. IntentNav achieves state-of-the-art performance on the MP3D, HM3D-v1 and HM3D-v2 ObjectNav benchmarks. The proposed candidate-level navigation interface transfers zero-shot to wheeled, quadruped, and humanoid robots without further VLM fine-tuning. \href{https://anonymous.4open.science/w/IntentNav/}{Project page}.

Related papers

• SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning [59.64305326980364]
  SpaceVLN is a navigation agent built around Spatial Cognitive Memory and Task-Guided Spatial Reasoning.<n>Built on this memory, Spatial-CoT integrates task-progress reasoning with spatial perception, analysis, and prediction.<n>Across R2R-CE, RxR-CE, GN-Bench, and HM3D-OVON, SpaceVLN achieves state-of-the-art zero-shot performance.
  arXiv  Detail & Related papers  (2026-06-08T03:42:08Z)
• FeudalNav: A Simple Framework for Visual Navigation [7.136542835931238]
  We develop a hierarchical framework that decomposes the navigation decision-making process into multiple levels.<n>Our method learns to select subgoals through a simple, transferable waypoint selection network.<n>We show competitive results with a suite of SOTA methods in Habitat AI environments without using any odometry in training or inference.
  arXiv  Detail & Related papers  (2026-01-15T22:10:29Z)
• ImagineNav++: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination [36.489349671649045]
  Vision-Language Models (VLMs) can achieve mapless visual navigation using only onboard RGB/RGB-D streams, unlocking their potential for spatial perception and planning.<n>We achieve this through an imagination-powered navigation framework, ImagineNav++, which imagines future observation images from candidate robot views.<n>To maintain spatial consistency, we develop a selective foveation memory mechanism, which hierarchically integrates observations via a sparse-to-dense framework.
  arXiv  Detail & Related papers  (2025-12-19T10:40:16Z)
• YOPO-Nav: Visual Navigation using 3DGS Graphs from One-Pass Videos [5.7772802828964664]
  We propose YOPO-Nav, which encodes an environment into a compact spatial representation composed of interconnected local 3D Gaussian Splatting (3DGS) models.<n>During navigation, the framework aligns the robot's current visual observation with this representation and predicts actions that guide it back toward the demonstrated trajectory.<n>We benchmark recent visual navigation methods on trajectories from YOPO-Campus using a Clearpath Jackal robot. Experimental results show YOPO-Nav provides excellent performance in image-goal navigation for real-world scenes on a physical robot.
  arXiv  Detail & Related papers  (2025-12-10T18:32:38Z)
• History-Augmented Vision-Language Models for Frontier-Based Zero-Shot Object Navigation [5.343932820859596]
  This paper introduces a novel zero-shot ObjectNav framework that pioneers the use of dynamic, history-aware prompting.<n>Our core innovation lies in providing the VLM with action history context, enabling it to generate semantic guidance scores for navigation actions.<n>We also introduce a VLM-assisted waypoint generation mechanism for refining the final approach to detected objects.
  arXiv  Detail & Related papers  (2025-06-19T21:50:16Z)
• CoNav: A Benchmark for Human-Centered Collaborative Navigation [66.6268966718022]
  We propose a collaborative navigation (CoNav) benchmark. Our CoNav tackles the critical challenge of constructing a 3D navigation environment with realistic and diverse human activities. We propose an intention-aware agent for reasoning both long-term and short-term human intention.
  arXiv  Detail & Related papers  (2024-06-04T15:44:25Z)
• Object Goal Navigation with Recursive Implicit Maps [92.6347010295396]
  We propose an implicit spatial map for object goal navigation. Our method significantly outperforms the state of the art on the challenging MP3D dataset. We deploy our model on a real robot and achieve encouraging object goal navigation results in real scenes.
  arXiv  Detail & Related papers  (2023-08-10T14:21:33Z)
• SOON: Scenario Oriented Object Navigation with Graph-based Exploration [102.74649829684617]
  The ability to navigate like a human towards a language-guided target from anywhere in a 3D embodied environment is one of the 'holy grail' goals of intelligent robots. Most visual navigation benchmarks focus on navigating toward a target from a fixed starting point, guided by an elaborate set of instructions that depicts step-by-step. This approach deviates from real-world problems in which human-only describes what the object and its surrounding look like and asks the robot to start navigation from anywhere.
  arXiv  Detail & Related papers  (2021-03-31T15:01:04Z)
• Active Visual Information Gathering for Vision-Language Navigation [115.40768457718325]
  Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments. One of the key challenges in VLN is how to conduct a robust navigation by mitigating the uncertainty caused by ambiguous instructions and insufficient observation of the environment. This work draws inspiration from human navigation behavior and endows an agent with an active information gathering ability for a more intelligent VLN policy.
  arXiv  Detail & Related papers  (2020-07-15T23:54:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.