AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations

• URL: http://arxiv.org/abs/2607.11739v1
• Date: Mon, 13 Jul 2026 16:00:41 GMT
• Title: AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations
• Abstract summary: Real-time navigation in dynamic environments requires collision-free and dynamically feasible motion under limited perception.<n>We formulate navigation as learning a transferable goal-aligned path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations.<n>We develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning.
• Score: 103.61864552260316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time navigation in cluttered and dynamic environments requires collision-free and dynamically feasible motion under limited perception. However, feasible navigation behaviors are inherently multimodal because multiple paths may exist around obstacles. In this paper, we formulate navigation as learning a transferable goal-conditioned stochastic path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations. This formulation enables structured sampling of navigation candidates, allowing multiple feasible paths to be explored through sampling without relying on robot-specific motion constraints. To this end, we introduce a goal-aligned canonical state representation that removes in-plane rotational ambiguity and normalizes local geometry with respect to the goal, enabling rotation-invariant path distribution learning. We further develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning. Extensive experiments in dense static environments and dynamic pedestrian scenarios demonstrate that the proposed method achieves consistently high success rates with competitive efficiency while enabling cross-platform transfer of a single path prior learned on differential-drive robots to quadruped platforms without retraining.

Related papers

• EgoNav: Bridging Learned Waypoints and Geometry-Aware Local Control for Robust Indoor Navigation [7.207055231363046]
  Image-goal navigation using lightweight topological maps is a practical paradigm for indoor robot deployment.<n>EgoNav is a hierarchical system that generates candidates from semantically segmented traversable regions.<n>EgoNav consistently outperforms contemporary baselines in both success rate and path efficiency.
  arXiv  Detail & Related papers  (2026-08-26T11:13:07Z)
• Beyond Waypoints: A Trajectory-Centric Waypointing Paradigm for Vision-Language Navigation [79.12557132788139]
  Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions while navigating in real-world-like environments.<n>Most VLN-CE approach-es adopt a three-stage framework: a waypoint predictor proposes waypoints, and a navigator selects the best waypoint.<n>We introduce a novel paradigm called Trajectory Waypoint, which grounds each candidate waypoint in an executable trajectory.
  arXiv  Detail & Related papers  (2026-06-05T13:11:39Z)
• Beyond Waypoints: Dual-Heatmap Grounding for Cross-Embodiment Semantic Navigation [13.081539889943906]
  In this work, we focus on the practical setting of in-FOV semantic navigation.<n>We propose a unified Vision-Language framework that abandons single-point regression in favor of a Dual-Heatmap representation.<n>Our framework achieves state-of-the-art performance among comparable 8B baselines.
  arXiv  Detail & Related papers  (2026-05-19T06:12:59Z)
• Risk-Aware Obstacle Avoidance Algorithm for Real-Time Applications [0.6299766708197881]
  This study introduces a hybrid risk-aware navigation architecture that integrates probabilistic modeling of obstacles along the vehicle path with smooth trajectory optimization for autonomous surface vessels.<n>The proposed method shows improvements in operational safety and autonomy, establishing it as a promising solution for risk-aware autonomous vehicle missions in uncertain and dynamic environments.
  arXiv  Detail & Related papers  (2026-02-09T21:15:33Z)
• MetricNet: Recovering Metric Scale in Generative Navigation Policies [51.90872764552077]
  MetricNet is an effective add-on for generative navigation that predicts the metric distance between waypoints.<n>We show that executing MetricNet-scaled waypoints significantly improves both navigation and exploration performance.<n>We also propose MetricNav, which integrates MetricNet into a navigation policy to guide the robot away from obstacles while still moving towards the goal.
  arXiv  Detail & Related papers  (2025-09-17T13:37:13Z)
• DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation [73.80968452950854]
  Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces.<n>Existing VLN-CE approaches typically use a two-stage waypoint planning framework.<n>We propose DAgger Diffusion Navigation (DifNav) as an end-to-end optimized VLN-CE policy.
  arXiv  Detail & Related papers  (2025-08-13T02:51:43Z)
• Navigating Motion Agents in Dynamic and Cluttered Environments through LLM Reasoning [69.5875073447454]
  This paper advances motion agents empowered by large language models (LLMs) toward autonomous navigation in dynamic and cluttered environments.<n>Our training-free framework supports multi-agent coordination, closed-loop replanning, and dynamic obstacle avoidance without retraining or fine-tuning.
  arXiv  Detail & Related papers  (2025-03-10T13:39:09Z)
• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
  This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
  arXiv  Detail & Related papers  (2023-10-11T21:07:14Z)
• Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation [1.2783783498844021]
  A typical SOTA system is composed of four main modules -- mapper, global planner, local planner, and command-tracking controller. We build a robust and safe local planner which is designed to generate a velocity plan to track a coarsely planned path from the global planner. Using our framework, a quadruped robot can autonomously navigate in various complex environments without a collision and generate a smoother command plan compared to the baseline method.
  arXiv  Detail & Related papers  (2022-04-19T04:01:44Z)
• Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs) [61.94975011711275]
  We propose a geometrically based motion planning technique textquotedblleft RRT*textquotedblright; for this purpose. In the proposed technique, we modified original RRT* introducing an adaptive search space and a steering function. We have tested the proposed technique in various simulated environments.
  arXiv  Detail & Related papers  (2020-08-29T09:55:49Z)

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.