MRReP: Mixed Reality-based Hand-drawn Reference Path Editing Interface for Mobile Robot Navigation

• URL: http://arxiv.org/abs/2604.00059v1
• Date: Tue, 31 Mar 2026 04:49:53 GMT
• Title: MRReP: Mixed Reality-based Hand-drawn Reference Path Editing Interface for Mobile Robot Navigation
• Abstract summary: This paper presents MRReP, a Mixed Reality-based interface that enables users to draw a Hand-drawn Reference Path (HRP) directly on the physical floor using hand gestures.<n>The results demonstrate that MRReP enhanced path specification accuracy, usability, and perceived workload, while enabling more stable path specification in the physical environment.
• Score: 1.552282932199974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous mobile robots operating in human-shared indoor environments often require paths that reflect human spatial intentions, such as avoiding interference with pedestrian flow or maintaining comfortable clearance. However, conventional path planners primarily optimize geometric costs and provide limited support for explicit route specification by human operators. This paper presents MRReP, a Mixed Reality-based interface that enables users to draw a Hand-drawn Reference Path (HRP) directly on the physical floor using hand gestures. The drawn HRP is integrated into the robot navigation stack through a custom Hand-drawn Reference Path Planner, which converts the user-specified point sequence into a global path for autonomous navigation. We evaluated MRReP in a within-subject experiment against a conventional 2D baseline interface. The results demonstrated that MRReP enhanced path specification accuracy, usability, and perceived workload, while enabling more stable path specification in the physical environment. These findings suggest that direct path specification in MR is an effective approach for incorporating human spatial intention into mobile robot navigation. Additional material is available at https://mertcookimg.github.io/mrrep

Related papers

• CrossTracer: Cross-Embodiment Navigation via VLA Model Reasoning and Trace Residuals Adapting [51.52109090141233]
  CrossTracer is a hierarchical framework for cross-embodiment navigation.<n>It forms a unified pixel-space interface between semantic reasoning and physical grounding.<n>We evaluate CrossTracer on the NaviTrace benchmark, which tests whether a model can generate embodiment-consistent navigation traces.
  arXiv  Detail & Related papers  (2026-08-07T01:24:53Z)
• AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations [103.61864552260316]
  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.
  arXiv  Detail & Related papers  (2026-07-13T16:00:41Z)
• NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints [46.6378130545666]
  This paper presents NORM-Nav, a framework that integrates natural language behavioral constraints into costmap-based planning.<n>An LLM parses each instruction into structured constraints and grounds them using real-time vision--LiDAR perception.<n> Simulation and real-world experiments indicate that NORM-Nav improves task success rates and produces trajectories closer to human references than representative baselines.
  arXiv  Detail & Related papers  (2026-05-16T12:58:03Z)
• MRPoS: Mixed Reality-Based Robot Navigation Interface Using Spatial Pointing and Speech with Large Language Model [1.552282932199974]
  This paper proposes the Mixed Reality-Based Robot Navigation Interface using Spatial Pointing and Speech (MRPoS)<n>This novel framework replaces complex hand gestures with a natural, multimodal interface combining spatial pointing with Large Language Model (LLM)-based speech interaction.<n>By leveraging both information, the system translates verbal intent into navigation goals visualized by MR technology.
  arXiv  Detail & Related papers  (2026-03-04T08:45:25Z)
• Pro-HOI: Perceptive Root-guided Humanoid-Object Interaction [57.252599851031505]
  We introduce Perceptive Root-guided Humanoid-Object Interaction, Pro-HOI.<n>Pro-HOI is a generalizable framework for robust humanoid loco-manipulation.<n> Empirical validation on a Unitree G1 robot demonstrates that Pro-HOI significantly outperforms baselines in generalization and robustness.
  arXiv  Detail & Related papers  (2026-03-01T14:19:59Z)
• Temporally-Sampled Efficiently Adaptive State Lattices for Autonomous Ground Robot Navigation in Partially Observed Environments [2.2188568806866287]
  In traditional autonomous navigation architectures, a regional motion planner will consume the environment map and output a trajectory for the local motion planner to use as a reference.<n>We propose Temporally-Sampled Efficiently Adaptive State Lattices (TSEASL) to consider updated and optimized versions of previously generated trajectories against the currently generated trajectory.<n>Results indicate that when running TSEASL, the robot did not require manual interventions in the same locations where the robot was running the baseline planner.
  arXiv  Detail & Related papers  (2026-02-13T18:14:56Z)
• From Obstacles to Etiquette: Robot Social Navigation with VLM-Informed Path Selection [57.74400052368147]
  This paper presents a social robot navigation framework that integrates geometric planning with contextual social reasoning.<n>The system first extracts obstacles and human dynamics to generate geometrically feasible candidate paths, then leverages a fine-tuned vision-language model (VLM) to evaluate these paths.<n>Experiments in four social navigation contexts demonstrate that our method achieves the best overall performance with the lowest personal space violation duration, the minimal pedestrian-facing time, and no social zone intrusions.
  arXiv  Detail & Related papers  (2026-02-09T18:46:12Z)
• RSRNav: Reasoning Spatial Relationship for Image-Goal Navigation [57.197881161006904]
  Recent image-goal navigation (ImageNav) methods learn a perception-action policy by separately capturing semantic features of the goal and egocentric images.<n>We propose RSRNav, a simple yet effective method that reasons spatial relationships between the goal and current observations as navigation guidance.
  arXiv  Detail & Related papers  (2025-04-25T00:22:17Z)
• Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach [5.009635912655658]
  Hand-drawn maps can often contain inaccuracies such as scale distortions and missing landmarks.<n>This paper introduces a novel Hand-drawn Map Navigation (HAM-Nav) architecture that leverages pre-trained vision language models.<n>Ham-Nav integrates a unique Selective Visual Association Prompting approach for topological map-based position estimation and navigation planning.
  arXiv  Detail & Related papers  (2025-01-31T19:03:33Z)
• 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)
• Gesture2Path: Imitation Learning for Gesture-aware Navigation [54.570943577423094]
  We present Gesture2Path, a novel social navigation approach that combines image-based imitation learning with model-predictive control. We deploy our method on real robots and showcase the effectiveness of our approach for the four gestures-navigation scenarios.
  arXiv  Detail & Related papers  (2022-09-19T23:05:36Z)
• Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping [7.332652485849632]
  This study aims to realize a hierarchical spatial representation using a topometric semantic map and path planning with speech instructions, including waypoints. We conducted experiments in home environments using the Toyota Human Support Robot on the SIGVerse simulator and in a lab-office environment with the real robot, Albert. Navigation experiments using speech instructions with a waypoint demonstrated a performance improvement of SpCoTMHP over the baseline hierarchical path planning method with path costs.
  arXiv  Detail & Related papers  (2022-03-21T09:15:25Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)

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.