Robot Navigation

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Robot navigation combines sensing and localization with route planning and motion control, from indoor mobile robots and legged platforms to drones, vehicles, and medical devices. Common approaches include SLAM and geometric planners such as A* and model-predictive control, alongside reinforcement learning, imitation learning, and vision-language policies that use semantic maps, memory, or predicted futures. Methods are tested in simulation and on physical robots, with benchmarks such as Habitat HM3D and MP3D for object navigation and R2R-CE for language-guided routes. A central challenge is making long-horizon decisions that remain safe and efficient when perception is uncertain, environments change, and policies transfer from simulation to real robots.

Latest papers

557 papers103 papers in the last four weeks, up 29% on the four weeks before. 0.6% of all new papers.

Mar 27, 2026

DiffusionAnything: End-to-End In-context Diffusion Learning for Unified Navigation and Pre-Grasp Motion

Efficiently predicting motion plans directly from vision remains a fundamental challenge in robotics, where planning typically requires explicit goal specification and task-specific design. Recent vision-language-action (VLA) models infer actions directly from visual input but demand massive computational resources, extensive training data, and fail zero-shot in novel scenes. We present a unified image-space diffusion policy handling both meter-scale navigation and centimeter-scale manipulation via multi-scale feature modulation, with only 5 minutes of self-supervised data per task. Three key innovations drive the framework: (1) Multi-scale FiLM conditioning on task mode, depth scale, and spatial attention enables task-appropriate behavior in a single model; (2) trajectory-aligned depth prediction focuses metric 3D reasoning along generated waypoints; (3) self-supervised attention from AnyTraverse enables goal-directed inference without vision-language models and depth sensors. Operating purely from RGB input (2.0 GB memory, 10 Hz), the model achieves robust zero-shot generalization to novel scenes while remaining suitable for onboard deployment.

Iana Zhura, Yara Mahmoud, Jeffrin Sam +4

The authors are with the Intelligent Space Robotics Laboratory, Center for Digital Engineering, Skolkovo Institute of Science and Technology.

Visual Robot Navigation Diffusion-Based Planning Robot Navigation Zero-Shot Robotic Manipulation ICL for Robotics Data-Efficient Robot Learning

Mar 27, 2026

Line-of-Sight-Constrained Multi-Robot Mapless Navigation via Polygonal Visible Regions

Multi-robot systems rely on underlying connectivity to ensure reliable communication and timely coordination. This paper studies the line-of-sight (LoS) connectivity maintenance problem in multi-robot navigation with unknown obstacles. Prior works typically assume known environment maps to formulate LoS constraints between robots, which hinders their practical deployment. To overcome this limitation, we propose an inherently distributed approach where each robot only constructs an egocentric visible region based on its real-time LiDAR scans, instead of endeavoring to build a global map online. The individual visible regions are shared through distributed communication to establish inter-robot LoS constraints, which are then incorporated into a multi-robot navigation framework to ensure LoS-connectivity. Moreover, we enhance the robustness of connectivity maintenance by proposing a more accurate LoS-distance metric, which further enables flexible topology optimization that eliminates redundant and effort-demanding connections. The proposed framework is evaluated through extensive multi-robot navigation and exploration tasks in both simulation and real-world experiments. Results show that it reliably maintains LoS-connectivity between robots in challenging environments cluttered with obstacles, even under large visible ranges and fragile minimal topologies, where existing methods consistently fail. Ablation studies also reveal that topology optimization boosts navigation efficiency by around

20\%

, demonstrating the framework's potential for efficient navigation under connectivity constraints.

Ruofei Bai, Shenghai Yuan, Xinhang Xu +5

Robot Navigation Multi-Robot Coordination Multi-Robot Motion Planning

Mar 27, 2026

SpatialAnt: Autonomous Zero-Shot Robot Navigation via Active Scene Reconstruction and Visual Anticipation

Vision-and-Language Navigation (VLN) has recently benefited from Multimodal Large Language Models (MLLMs), enabling zero-shot navigation. While recent exploration-based zero-shot methods have shown promising results by leveraging global scene priors, they rely on high-quality human-crafted scene reconstructions, which are impractical for real-world robot deployment. When encountering an unseen environment, a robot should build its own priors through pre-exploration. However, these self-built reconstructions are inevitably incomplete and noisy, which severely degrade methods that depend on high-quality scene reconstructions. To address these issues, we propose SpatialAnt, a zero-shot navigation framework designed to bridge the gap between imperfect self-reconstructions and robust execution. SpatialAnt introduces a physical grounding strategy to recover the absolute metric scale for monocular-based reconstructions. Furthermore, rather than treating the noisy self-reconstructed scenes as absolute spatial references, we propose a novel visual anticipation mechanism. This mechanism leverages the noisy point clouds to render future observations, enabling the agent to perform counterfactual reasoning and prune paths that contradict human instructions. Extensive experiments in both simulated and real-world environments demonstrate that SpatialAnt significantly outperforms existing zero-shot methods. We achieve a 66% Success Rate (SR) on R2R-CE and 50.8% SR on RxR-CE benchmarks. Physical deployment on a Hello Robot further confirms the efficiency and efficacy of our framework, achieving a 52% SR in challenging real-world settings.

Jiwen Zhang, Xiangyu Shi, Siyuan Wang +3

Fudan University · Adelaide University · University of Southern California +1

Robot Navigation Vision-Language Navigation

Mar 26, 2026

Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned

Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations. Despite growing real-world deployment, existing evaluations rely almost exclusively on success rate, whether the robot reaches its goal, which conceals trajectory quality, collision behavior, and robustness to environmental change. We present a real-world evaluation of five state-of-the-art VNMs (GNM, ViNT, NoMaD, NaviBridger, and CrossFormer) across two robot platforms and five environments spanning indoor and outdoor settings. Beyond success rate, we combine path-based metrics with vision-based goal-recognition scores and assess robustness through controlled image perturbations (motion blur, sunflare). Our analysis uncovers three systematic limitations: (a) even architecturally sophisticated diffusion and transformer-based models exhibit frequent collisions, indicating limited geometric understanding; (b) models fail to discriminate between different locations that are perceptually similar, however some semantics differences are present, causing goal prediction errors in repetitive environments; and (c) performance degrades under distribution shift. We will publicly release our evaluation codebase and dataset to facilitate reproducible benchmarking of VNMs.

Maeva Guerrier, Karthik Soma, Jana Pavlasek +1

Polytechnique Montreal

Visual Navigation Robot Navigation Visual Robot Navigation Robot Navigation Benchmarks

Mar 26, 2026

RHINO-AR: An Augmented Reality Exhibit for Teaching Mobile Robotics Concepts in Museums

Understanding autonomous mobile robots is challenging for people without robotics expertise, because key processes such as sensing, mapping, and planning are invisible. We present RHINO-AR, an interactive Augmented Reality (AR) museum exhibit that reintroduces the historical mobile robot RHINO into its original exhibition environment at the Deutsches Museum Bonn. To make the autonomous behavior that once made RHINO historically significant accessible again, our previous work, RHINO-VR, reconstructed the robot and its environment in a fully virtual museum scene. While engaging, this fully virtual presentation separated visitors from the real exhibition space and from the physical robot on display. RHINO-AR closes this reality gap by placing a virtual reconstruction of RHINO directly into the real museum space. Implemented on a Magic Leap 2 headset using Unity, our system combines real-time environment meshing with interactive visualizations of LiDAR sensing, traversability, and path planning to make otherwise invisible robotics processes understandable to non-expert visitors. We evaluated RHINO-AR in a two-day museum study with 22 participants, assessing usability, technical performance, satisfaction, conceptual understanding, and preference compared to RHINO-VR. The results indicate that RHINO-AR was well received, conveyed the targeted navigation concepts to a non-expert audience, and was generally preferred over the VR exhibit due to its stronger physical grounding and increased realism.

Nils Dengler, Tim Graf, Leif Van Holland +3

Humanoid Robots Lab, University of Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence and the Center for Robotics, Bonn, Germany

Augmented Reality Mobile Robotics Robot Navigation Robotics Robotics Education

Mar 26, 2026

Intelligent Navigation and Obstacle-Aware Fabrication for Mobile Additive Manufacturing Systems

As the demand for mass customization increases, manufacturing systems must become more flexible and adaptable to produce personalized products efficiently. Additive manufacturing (AM) enhances production adaptability by enabling on-demand fabrication of customized components directly from digital models, but its flexibility remains constrained by fixed equipment layouts. Integrating mobile robots addresses this limitation by allowing manufacturing resources to move and adapt to changing production requirements. Mobile AM Robots (MAMbots) combine AM with mobile robotics to produce and transport components within dynamic manufacturing environments. However, the dynamic manufacturing environments introduce challenges for MAMbots. Disturbances such as obstacles and uneven terrain can disrupt navigation stability, which in turn affects printing accuracy and surface quality. This work proposes a universal mobile printing-and-delivery platform that couples navigation and material deposition, addressing the limitations of earlier frameworks that treated these processes separately. A real-time control framework is developed to plan and control the robot's navigation, ensuring safe motion, obstacle avoidance, and path stability while maintaining print quality. The closed-loop integration of sensing, mobility, and manufacturing provides real-time feedback for motion and process control, enabling MAMbots to make autonomous decisions in dynamic environments. The framework is validated through simulations and real-world experiments that test its adaptability to trajectory variations and external disturbances. Coupled navigation and printing together enable MAMbots to plan safe, adaptive trajectories, improving flexibility and adaptability in manufacturing.

Yifei Li, Ruizhe Fu, Huihang Liu +3

The Pennsylvania State University, University Park, PA · Arizona State University, Tempe, AZ

Additive Manufacturing Robot Navigation Mobile Robotics Robot Motion Planning

Mar 26, 2026

IntentReact: Guiding Reactive Object-Centric Navigation via Topological Intent

Object-goal visual navigation requires robots to reason over semantic structure and act effectively under partial observability. Recent approaches based on object-level topological maps enable long-horizon navigation without dense geometric reconstruction, but their execution remains limited by the gap between global topological guidance and local perception-driven control. In particular, local decisions are made solely from the current egocentric observation, without access to information beyond the robot's field of view. As a result, the robot may persist along its current heading even when initially oriented away from the goal, moving toward directions that do not decrease the global topological distance. In this work, we propose IntentReact, an intent-conditioned object-centric navigation framework that introduces a compact interface between global topological planning and reactive object-centric control. Our approach encodes global topological guidance as a low-dimensional directional signal, termed intent, which conditions a learned waypoint prediction policy to bias navigation toward topologically consistent progression. This design enables the robot to promptly reorient when local observations are misleading, guiding motion toward directions that decrease global topological distance while preserving the reactivity and robustness of object-centric control. We evaluate the proposed framework through extensive experiments, demonstrating improved navigation success and execution quality compared to prior object-centric navigation methods.

Yanmei Jiao, Anpeng Lu, Wenhan Hu +4

Yanmei Jiao, Anpeng Lu and Wenhan Hu are with the School of Information Science and Engineering, Hangzhou Normal University, Hangzhou, P.R. China. · Rong Xiong and Yue Wang are with the State Key Laboratory of Industrial Control and Technology, Zhejiang University, Hangzhou, P.R. China. · Huajin Tang is with Neuromorphic Computing and Robotic Cognition Lab, Zhejiang University, Hangzhou, P.R. China. +1

Robot Navigation Object-Goal Navigation

Mar 26, 2026

Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics

Autonomous object search is challenging for mobile robots operating in indoor environments due to partial observability, perceptual uncertainty, and the need to trade off exploration and navigation efficiency. Classical probabilistic approaches explicitly represent uncertainty but typically rely on handcrafted action-selection heuristics, while deep reinforcement learning enables adaptive policies but often suffers from slow convergence and limited interpretability. This paper proposes a hybrid object-search framework that integrates Bayesian inference with deep reinforcement learning. The method maintains a spatial belief map over target locations, updated online through Bayesian inference from calibrated object detections, and trains a reinforcement learning policy to select navigation actions directly from this probabilistic representation. The approach is evaluated in realistic indoor simulation using Habitat 3.0 and compared against developed baseline strategies. Across two indoor environments, the proposed method improves success rate while reducing search effort. Overall, the results support the value of combining Bayesian belief estimation with learned action selection to achieve more efficient and reliable objectsearch behavior under partial observability.

João Castelo-Branco, José Santos-Victor, Alexandre Bernardino

Instituto Superior Técnico, Lisbon, Portugal · Institute For Systems and Robotics, Lisbon, Portugal

Object-Goal Navigation Robot Navigation Mobile Robotics Partially Observable RL Bayesian RL

Mar 25, 2026

Saranga: MilliWatt Ultrasound for Navigation in Visually Degraded Environments on Palm-Sized Aerial Robots

Tiny palm-sized aerial robots possess exceptional agility and cost-effectiveness in navigating confined and cluttered environments. However, their limited payload capacity directly constrains the sensing suite on-board the robot, thereby limiting critical navigational tasks in Global Positioning System (GPS)-denied wild scenes. Common methods for obstacle avoidance use cameras and LIght Detection And Ranging (LIDAR), which become ineffective in visually degraded conditions such as low visibility, dust, fog or darkness. Other sensors, such as RAdio Detection And Ranging (RADAR), have high power consumption, making them unsuitable for tiny aerial robots. Inspired by bats, we propose Saranga, a low-power ultrasound-based perception stack that localizes obstacles using a dual sonar array. We present two key solutions to combat the low Peak Signal-to-Noise Ratio of

-4.9

decibels: physical noise reduction and a deep learning based denoising method. Firstly, we present a practical way to block propeller induced ultrasound noise on the weak echoes. The second solution is to train a neural network to utilize the \textcolor{black}{long horizon of ultrasound echoes} for finding signal patterns under high amounts of uncorrelated noise where classical methods were insufficient. We generalize to the real world by using a synthetic data generation pipeline and limited real noise data for training. We enable a palm-sized aerial robot to navigate in visually degraded conditions of dense fog, darkness, and snow in a cluttered environment with thin and transparent obstacles using only on-board sensing and computation. We provide extensive real world results to demonstrate the efficacy of our approach.

Manoj Velmurugan, Phillip Brush, Colin Balfour +2

Perception and Autonomous Robotics (PeAR) Group, Worcester Polytechnic Institute, Worcester, MA 01609, USA. · TDK InvenSense, Berkeley, CA 94710, USA.

Robot Navigation Robotic Ultrasound Robot Collision Avoidance Aerial Robotics

Mar 25, 2026

ReMemNav: Memory-Based Decision Correction and Target Verification for Zero-Shot Object Navigation

Zero-shot object navigation requires agents to locate unseen targets in unfamiliar environments without prior maps or task-specific training. Despite the commonsense reasoning ability of vision-language models (VLMs), existing mapless navigators often suffer from repeated exploration and premature stopping caused by limited historical context and false-positive target predictions. We propose ReMemNav, a training-free framework that combines lightweight semantic grounding, memory-based decision correction, and target verification. RAM-derived semantic priors guide panoramic direction selection, while a geometry-triggered correction mechanism retrieves historical scene descriptions when the selected direction points toward previously visited regions. When a target is predicted to be visible, ReMemNav verifies the corresponding single-view observation before the final approach. Depth-based action sampling then converts the high-level decision into collision-free motion. Experiments on HM3D and MP3D show that ReMemNav achieves higher success rates and path efficiency than existing training-free zero-shot baselines. Specifically, ReMemNav achieves absolute SR/SPL gains of 1.7%/5.3% on HM3D v0.1, 18.2%/11.1% on HM3D v0.2, and 8.7%/6.9% on MP3D.

Feng Wu, Wei Zuo, Wenliang Yang +3

Fudan University · Tongji University

Robot Navigation Vision-Language Navigation Object-Goal Navigation VLMs for Robotics

Mar 24, 2026

Task-Aware Positioning for Improvisational Tasks in Mobile Construction Robots via an AI Agent with Multi-LMM Modules

Due to the ever-changing nature of construction, many tasks on sites occur in an improvisational manner. Existing mobile construction robot studies remain limited in addressing improvisational tasks, where task-required locations, timing of task occurrence, and contextual information required for task execution are not known in advance. We propose an agent that understands improvisational tasks given in natural language, identifies the task-required location, and positions itself. The agent's functionality was decomposed into three Large Multimodal Model (LMM) modules operating in parallel, enabling the application of LMMs for task interpretation and breakdown, construction drawing-based navigation, and visual reasoning to identify non-predefined task-required locations. The agent was implemented with a quadruped robot and achieved a 92.2% success rate for identifying and positioning at task-required locations across three tests designed to assess improvisational task handling. This study enables mobile construction robots to perform non-predefined tasks autonomously.

Seongju Jang, Francis Baek, SangHyun Lee

Ph.D. Student, Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward St, Ann Arbor, MI 48109 · Assistant Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology, North Avenue, Atlanta GA 30332 · Robert B. Harris Collegiate Professor, Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward St, Ann Arbor, MI 48109

Robotic Construction Robot Navigation Multimodal Agents Large Language Model-Based Robot Planning

Mar 24, 2026

CATNAV: Cached Vision-Language Traversability for Efficient Zero-Shot Robot Navigation

Navigating unstructured environments requires assessing traversal risk relative to a robot's physical capabilities, a challenge that varies across embodiments. We present CATNAV, a cost-aware traversability navigation framework that leverages multimodal LLMs for zero-shot, embodiment-aware costmap generation without task-specific training. We introduce a visuosemantic caching mechanism that detects scene novelty and reuses prior risk assessments for semantically similar frames, reducing online VLM queries by 85.7%. Furthermore, we introduce a VLM-based trajectory selection module that evaluates proposals through visual reasoning to choose the safest path given behavioral constraints. We evaluate CATNAV on a quadruped robot across indoor and outdoor unstructured environments, comparing against state-of-the-art vision-language-action baselines. Across five navigation tasks, CATNAV achieves 10 percentage point higher average goal-reaching rate and 33% fewer behavioral constraint violations.

Aditya Potnis, Francisco Affonso, Shreya Gummadi +2

Field Robotics Engineering and Science Hub (FRESH), Illinois Autonomous Farm, University of Illinois at Urbana-Champaign (UIUC), IL

Robot Traversability Estimation Robot Navigation Visual Robot Navigation VLMs for Robotics Efficient VLM Inference

Mar 24, 2026

Variable-Resolution Virtual Maps for Autonomous Exploration with Unmanned Surface Vehicles (USVs)

Autonomous exploration by unmanned surface vehicles (USVs) in near-shore waters requires reliable localisation and consistent mapping over extended areas, but this is challenged by GNSS degradation, environment-induced localisation uncertainty, and limited on-board computation. Virtual map-based methods explicitly model localisation and mapping uncertainty by tightly coupling factor-graph SLAM with a map uncertainty criterion. However, their storage and computational costs scale poorly with fixed-resolution workspace discretisations, leading to inefficiency in large near-shore environments. Moreover, overvaluing feature-sparse open-water regions can increase the risk of SLAM failure as a result of imbalance between exploration and exploitation. To address these limitations, we propose a Variable-Resolution Virtual Map (VRVM), a computationally efficient method for representing map uncertainty using bivariate Gaussian virtual landmarks placed in the cells of an adaptive quadtree. The adaptive quadtree enables an area-weighted uncertainty representation that keeps coarse, far-field virtual landmarks deliberately uncertain while allocating higher resolution to information-dense regions, and reduces the sensitivity of the map valuation to local refinements of the tree. An expectation-maximisation (EM) planner is adopted to evaluate pose and map uncertainty along frontiers using the VRVM, balancing exploration and exploitation. We evaluate VRVM against several state-of-the-art exploration algorithms in the VRX Gazebo simulator, using a realistic marina environment across different testing scenarios with an increasing level of exploration difficulty. The results indicate that our method offers safer behaviour and better utilisation of on-board computation in GNSS-degraded near-shore environments.

Ye Li, Yewei Huang, Yongchang Xie +4

Department of Mechanical Engineering, University College London, London, UK · Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA · Department of Computer Science, Dartmouth College, Hanover, NH, USA

Simultaneous Localization and Mapping Mobile Robotics Robot Navigation Autonomous Robot Exploration Unmanned Surface Vehicle

Mar 23, 2026

Cross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements

This paper presents a cross-modal learning framework that exploits complementary information from depth and grayscale images for robust navigation. We introduce a Cross-Modal Wasserstein Autoencoder that learns shared latent representations by enforcing cross-modal consistency, enabling the system to infer depth-relevant features from grayscale observations when depth measurements are corrupted. The learned representations are integrated with a Reinforcement Learning-based policy for collision-free navigation in unstructured environments when depth sensors experience degradation due to adverse conditions such as poor lighting or reflective surfaces. Simulation and real-world experiments demonstrate that our approach maintains robust performance under significant depth degradation and successfully transfers to real environments.

Omkar Sawant, Luca Zanatta, Grzegorz Malczyk +1

Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Norway

Robot Navigation Cross-Modal Learning Multimodal Robustness Cross-Modal Representation Learning RL for Robotics

Mar 23, 2026

Reasoning Systems for Semantic Navigation in Mobile Robots

Semantic navigation is the navigation paradigm in which environmental semantic concepts and their relationships are taken into account to plan the route of a mobile robot. This paradigm facilitates the interaction with humans and the understanding of human environments in terms of navigation goals and tasks. At the high level, a semantic navigation system requires two main components: a semantic representation of the environment, and a reasoner system. This paper is focused on develop a model of the environment using semantic concepts. This paper presents two solutions for the semantic navigation paradigm. Both systems implement an ontological model. Whilst the first one uses a relational database, the second one is based on KnowRob. Both systems have been integrated in a semantic navigator. We compare both systems at the qualitative and quantitative levels, and present an implementation on a mobile robot as a proof of concept.

Jonathan Crespo, Ramón Barber, O. M. Mozos +2

University of Carlos III of Madrid, 28911, Spain. · Technical University of Cartagena, 30202, Spain. · Institute of Artificial Intelligent at the University of Bremen, 28359, Germany.

Robot Navigation Semantic Mapping

Mar 23, 2026

IGV-RRT: Prior-Real-Time Observation Fusion for Active Object Search in Changing Environments

Object Goal Navigation (ObjectNav) in temporally changing indoor environments is challenging because object relocation can invalidate historical scene knowledge. To address this issue, we propose a probabilistic planning framework that combines uncertainty-aware scene priors with online target relevance estimates derived from a Vision Language Model (VLM). The framework contains a dual-layer semantic mapping module and a real-time planner. The mapping module includes an Information Gain Map (IGM) built from a 3D scene graph (3DSG) during prior exploration to model object co-occurrence relations and provide global guidance on likely target regions. It also maintains a VLM score map (VLM-SM) that fuses confidence-weighted semantic observations into the map for local validation of the current scene. Based on these two cues, we develop a planner that jointly exploits information gain and semantic evidence for online decision making. The planner biases tree expansion toward semantically salient regions with high prior likelihood and strong online relevance (IGV-RRT), while preserving kinematic feasibility through gradient-based analysis. Simulation and real-world experiments demonstrate that the proposed method effectively mitigates the impact of object rearrangement, achieving higher search efficiency and success rates than representative baselines in complex indoor environments.

Wei Zhang, Ping Gong, Yujie Wang +7

The School of Control Science and Engineering, Shandong University · Department of Data and Systems Engineering, HKU

Robot Navigation Object-Goal Navigation Motion Planning Sampling-Based Motion Planning Active Perception Semantic Mapping

Mar 23, 2026

Can a Robot Walk the Robotic Dog: Triple-Zero Collaborative Navigation for Heterogeneous Multi-Agent Systems

We present Triple Zero Path Planning (TZPP), a collaborative framework for heterogeneous multi-robot systems that requires zero training, zero prior knowledge, and zero simulation. TZPP employs a coordinator--explorer architecture: a humanoid robot handles task coordination, while a quadruped robot explores and identifies feasible paths using guidance from a multimodal large language model. We implement TZPP on Unitree G1 and Go2 robots and evaluate it across diverse indoor and outdoor environments, including obstacle-rich and landmark-sparse settings. Experiments show that TZPP achieves robust, human-comparable efficiency and strong adaptability to unseen scenarios. By eliminating reliance on training and simulation, TZPP offers a practical path toward real-world deployment of heterogeneous robot cooperation. Our code and video are provided at: https://github.com/triple-zeropp/Triple-zero-robot-agent

Yaxuan Wang, Yifan Xiang, Ke Li +5

Yuanpei College, Peking University · School of Computer Science, Peking University · School of Computer Science, Beijing University of Posts and Telecommunications +2

Robot Navigation Multi-Robot Systems Multi-Robot Coordination Multi-Robot Motion Planning Large Language Model-Based Robot Planning

Mar 21, 2026

Enhancing Vision-Based Policies with Omni-View and Cross-Modality Knowledge Distillation for Mobile Robots

Vision-based policies are widely applied in robotics for tasks such as manipulation and locomotion. On lightweight mobile robots, however, they face a trilemma of limited scene transferability, restricted onboard computation resources, and sensor hardware cost. To address these issues, we propose a knowledge distillation approach that transfers knowledge from an information-rich, appearance invariant omniview depth policy to a lightweight monocular policy. The key idea is to train the student not only to mimic the expert actions but also to align with the latent embeddings of the omni view depth teacher. Experiments demonstrate that omni-view and depth inputs improve the scene transfer and navigation performance, and that the proposed distillation method enhances the performance of a singleview monocular policy, compared with policies solely imitating actions. Real world experiments further validate the effectiveness and practicality of our approach. Code will be released publicly.

Kai Li, Shiyu Zhao

College of Computer Science and Technology at Zhejiang University, Hangzhou, China. · School of Engineering at Westlake University, Hangzhou, China.

Vision-Based Robot Control Mobile Robotics Robot Navigation Cross-Modal Knowledge Distillation Feature-Based Knowledge Distillation

Mar 20, 2026

Semantic Audio-Visual Navigation in Continuous Environments

Audio-visual navigation enables embodied agents to navigate toward sound-emitting targets by leveraging both auditory and visual cues. However, most existing approaches rely on precomputed room impulse responses (RIRs) for binaural audio rendering, restricting agents to discrete grid positions and leading to spatially discontinuous observations. To establish a more realistic setting, we introduce Semantic Audio-Visual Navigation in Continuous Environments (SAVN-CE), where agents can move freely in 3D spaces and perceive temporally and spatially coherent audio-visual streams. In this setting, targets may intermittently become silent or stop emitting sound entirely, causing agents to lose goal information. To tackle this challenge, we propose MAGNet, a multimodal transformer-based model that jointly encodes spatial and semantic goal representations and integrates historical context with self-motion cues to enable memory-augmented goal reasoning. Comprehensive experiments demonstrate that MAGNet significantly outperforms state-of-the-art methods, achieving up to a 12.1% absolute improvement in success rate. These results also highlight its robustness to short-duration sounds and long-distance navigation scenarios. The code is available at https://github.com/yichenzeng24/SAVN-CE.

Yichen Zeng, Hebaixu Wang, Meng Liu +4

Wuhan University · Zhongguancun Academy · Shandong Jianzhu University +4

Audio-Visual Navigation Multimodal Transformer Embodied Navigation Robot Navigation Memory-Augmented Agents Multimodal Learning

Mar 20, 2026

CeRLP: A Cross-embodiment Robot Local Planning Framework for Visual Navigation

Visual navigation for cross-embodiment robots is challenging due to variations in robot and camera configurations, which can lead to the failure of navigation tasks. Previous approaches typically rely on collecting massive datasets across different robots, which is highly data-intensive, or fine-tuning models, which is time-consuming. Furthermore, both methods often lack explicit consideration of robot geometry. In this paper, we propose a Cross-embodiment Robot Local Planning (CeRLP) framework for general visual navigation, which abstracts visual information into a unified geometric formulation and applies to heterogeneous robots with varying physical dimensions, camera parameters, and camera types. CeRLP introduces a depth estimation scale correction method that utilizes offline pre-calibration to resolve the scale ambiguity of monocular depth estimation, thereby recovering precise metric depth images. Furthermore, CeRLP designs a visual-to-scan abstraction module that projects varying visual inputs into height-adaptive laser scans, making the policy robust to heterogeneous robots. Experiments in simulation environments demonstrate that CeRLP outperforms comparative methods, validating its robust obstacle avoidance capabilities as a local planner. Additionally, extensive real-world experiments verify the effectiveness of CeRLP in tasks such as point-to-point navigation and vision-language navigation, demonstrating its generalization across varying robot and camera configurations.

Haoyu Xi, Mingao Tan, Xinming Zhang +5

School of Computer Science, Shanghai Jiao Tong University, Shanghai, P. R. China · College of Information Science and Technology, Eastern Institute of Technology, Ningbo, P. R. China

Visual Robot Navigation Robot Navigation Robot Motion Planning Cross-Embodiment Robot Learning Vision-Based Robot Control

Mar 19, 2026

SR-Nav: Spatial Relationships Matter for Zero-shot Object Goal Navigation

Zero-shot object-goal navigation aims to find target objects in unseen environments using only egocentric observation. Recent methods leverage foundation models' comprehension and reasoning capabilities to enhance navigation performance. However, when faced with poor viewpoints or weak semantic cues, foundation models often fail to support reliable reasoning in both perception and planning, resulting in inefficient or failed navigation. We observe that inherent relationships among objects and regions encode structured scene priors, which help agents infer plausible target locations even under partial observations. Motivated by this insight, we propose Spatial Relation-aware Navigation (SR-Nav), a framework that models both observed and experience-based spatial relationships to enhance both perception and planning. Specifically, SR-Nav first constructs a Dynamic Spatial Relationship Graph (DSRG) that encodes the target-centered spatial relationships through the foundation models and updates dynamically with real-time observations. We then introduce a Relation-aware Matching Module. It utilizes relationship matching instead of naive detection, leveraging diverse relationships in the DSRG to verify and correct errors, enhancing visual perception robustness. Finally, we design a Dynamic Relationship Planning Module to reduce the planning search space by dynamically computing the optimal paths based on the DSRG from the current position, thereby guiding planning and reducing exploration redundancy. Experiments on HM3D show that our method achieves state-of-the-art performance in both success rate and navigation efficiency. The code will be publicly available at https://github.com/Mzyw-1314/SR-Nav

Leyuan Fang, Zan Mao, Zijing Wang +1

School of Artificial Intelligence and Robotics, Hunan University, Changsha, China, 410114, China

Object-Goal Navigation Robot Navigation Visual Spatial Reasoning

Mar 18, 2026

AERR-Nav: Adaptive Exploration-Recovery-Reminiscing Strategy for Zero-Shot Object Navigation

Zero-Shot Object Navigation (ZSON) in unknown multi-floor environments presents a significant challenge. Recent methods, mostly based on semantic value greedy waypoint selection, spatial topology-enhanced memory, and Multimodal Large Language Model (MLLM) as a decision-making framework, have led to improvements. However, these architectures struggle to balance exploration and exploitation for ZSON when encountering unseen environments, especially in multi-floor settings, such as robots getting stuck at narrow intersections, endlessly wandering, or failing to find stair entrances. To overcome these challenges, we propose AERR-Nav, a Zero-Shot Object Navigation framework that dynamically adjusts its state based on the robot's environment. Specifically, AERR-Nav has the following two key advantages: (1) An Adaptive Exploration-Recovery-Reminiscing Strategy, enables robots to dynamically transition between three states, facilitating specialized responses to diverse navigation scenarios. (2) An Adaptive Exploration State featuring Fast and Slow-Thinking modes helps robots better balance exploration, exploitation, and higher-level reasoning based on evolving environmental information. Extensive experiments on the HM3D and MP3D benchmarks demonstrate that our AERR-Nav achieves state-of-the-art performance among zero-shot methods. Comprehensive ablation studies further validate the efficacy of our proposed strategy and modules.

Jingzhi Huang, Junkai Huang, Haoyang Yang +2

Hong Kong Polytechnic University · Institute of automation, Chinese Academy of Sciences · Hong Kong University of Science and Technology (Guangzhou)

Robot Navigation Object-Goal Navigation

Mar 18, 2026

AgentVLN: Towards Agentic Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) requires an embodied agent to ground complex natural-language instructions into long-horizon navigation in unseen environments. While Vision-Language Models (VLMs) offer strong 2D semantic understanding, current VLN systems remain constrained by limited spatial perception, 2D-3D representation mismatch, and monocular scale ambiguity. In this paper, we propose AgentVLN, a novel and efficient embodied navigation framework that can be deployed on edge computing platforms. We formulate VLN as a Partially Observable Semi-Markov Decision Process (POSMDP) and introduce a VLM-as-Brain paradigm that decouples high-level semantic reasoning from perception and planning via a plug-and-play skill library. To resolve multi-level representation inconsistency, we design a cross-space representation mapping that projects perception-layer 3D topological waypoints into the image plane, yielding pixel-aligned visual prompts for the VLM. Building on this bridge, we integrate a context-aware self-correction and active exploration strategy to recover from occlusions and suppress error accumulation over long trajectories. To further address the spatial ambiguity of instructions in unstructured environments, we propose a Query-Driven Perceptual Chain-of-Thought (QD-PCoT) scheme, enabling the agent with the metacognitive ability to actively seek geometric depth information. Finally, we construct AgentVLN-Instruct, a large-scale instruction-tuning dataset with dynamic stage routing conditioned on target visibility. Extensive experiments show that AgentVLN consistently outperforms prior state-of-the-art methods (SOTA) on long-horizon VLN benchmarks, offering a practical paradigm for lightweight deployment of next-generation embodied navigation models. Code: https://github.com/Allenxinn/AgentVLN.

Zihao Xin, Wentong Li, Yixuan Jiang +6

Nanjing University of Aeronautics and Astronautics · Shandong University · Zhejiang University

Vision-Language Navigation Robot Navigation VLMs for Robotics

Mar 18, 2026

Interpreting Context-Aware Human Preferences for Multi-Objective Robot Navigation

Robots operating in human-shared environments must not only achieve task-level navigation objectives such as safety and efficiency, but also adapt their behavior to human preferences. However, as human preferences are typically expressed in natural language and depend on environmental context, it is difficult to directly integrate them into low-level robot control policies. In this work, we present a pipeline that enables robots to understand and apply context-dependent navigation preferences by combining foundational models with a Multi-Objective Reinforcement Learning (MORL) navigation policy. Thus, our approach integrates high-level semantic reasoning with low-level motion control. A Vision-Language Model (VLM) extracts structured environmental context from onboard visual observations, while Large Language Models (LLM) convert natural language user feedback into interpretable, context-dependent behavioral rules stored in a persistent but updatable rule memory. A preference translation module then maps contextual information and stored rules into numerical preference vectors that parameterize a pretrained MORL policy for real-time navigation adaptation. We evaluate the proposed framework through quantitative component-level evaluations, a user study, and real-world robot deployments in various indoor environments. Our results demonstrate that the system reliably captures user intent, generates consistent preference vectors, and enables controllable behavior adaptation across diverse contexts. Overall, the proposed pipeline improves the adaptability, transparency, and usability of robots operating in shared human environments, while maintaining safe and responsive real-time control.

Tharun Sethuraman, Subham Agrawal, Nils Dengler +3

Hochschule Bonn-Rhein-Sieg, Germany · University of Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Germany

Robot Navigation Robot Policy Adaptation Social Robot Navigation Language-Conditioned Robot Control

Mar 18, 2026

OmniVLN: Omnidirectional 3D Perception and Token-Efficient LLM Reasoning for Visual-Language Navigation across Air and Ground Platforms

Language-guided embodied navigation requires an agent to interpret object-referential instructions, search across multiple rooms, localize the referenced target, and execute reliable motion toward it. Existing systems remain limited in real indoor environments because narrow field-of-view sensing exposes only a partial local scene at each step, often forcing repeated rotations, delaying target discovery, and producing fragmented spatial understanding; meanwhile, directly prompting LLMs with dense 3D maps or exhaustive object lists quickly exceeds the context budget. We present OmniVLN, a zero-shot visual-language navigation framework that couples omnidirectional 3D perception with token-efficient hierarchical reasoning for both aerial and ground robots. OmniVLN fuses a rotating LiDAR and panoramic vision into a hardware-agnostic mapping stack, incrementally constructs a five-layer Dynamic Scene Graph (DSG) from mesh geometry to room- and building-level structure, and stabilizes high-level topology through persistent-homology-based room partitioning and hybrid geometric/VLM relation verification. For navigation, the global DSG is transformed into an agent-centric 3D octant representation with multi-resolution spatial attention prompting, enabling the LLM to progressively filter candidate rooms, infer egocentric orientation, localize target objects, and emit executable navigation primitives while preserving fine local detail and compact long-range memory. Experiments show that the proposed hierarchical interface improves spatial referring accuracy from 77.27% to 93.18%, reduces cumulative prompt tokens by up to 61.7% in cluttered multi-room settings, and improves navigation success by up to 11.68% over a flat-list baseline. We will release the code and an omnidirectional multimodal dataset to support reproducible research.

Zhongyuang Liu, Min He, Shaonan Yu +5

CertaintyX. · the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798. · the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA. +1

Vision-Language Navigation Robot Navigation Aerial VLN Large Language Model-Based Robot Planning Efficient Language Model Reasoning 3D Scene Understanding

Mar 18, 2026

Full Stack Navigation, Mapping, and Planning for the Lunar Autonomy Challenge

We present a modular, full-stack autonomy system for lunar surface navigation and mapping developed for the Lunar Autonomy Challenge. Operating in a GNSS-denied, visually challenging environment, our pipeline integrates semantic segmentation, stereo visual odometry, pose graph SLAM with loop closures, and layered planning and control. We leverage lightweight learning-based perception models for real-time segmentation and feature tracking and use a factor-graph backend to maintain globally consistent localization. High-level waypoint planning is designed to promote mapping coverage while encouraging frequent loop closures, and local motion planning uses arc sampling with geometric obstacle checks for efficient, reactive control. We evaluate our approach in the competition's high-fidelity lunar simulator, demonstrating centimeter-level localization accuracy, high-fidelity map generation, and strong repeatability across random seeds and rock distributions. Our solution achieved first place in the final competition evaluation.

Adam Dai, Asta Wu, Keidai Iiyama +4

Stanford University

Robot Navigation Space Robotics Simultaneous Localization and Mapping GNSS-Denied Localization

Mar 18, 2026

Visual SLAM with DEM Anchoring for Lunar Surface Navigation

Future lunar missions will require autonomous rovers capable of traversing tens of kilometers across challenging terrain while maintaining accurate localization and producing globally consistent maps. However, the absence of global positioning systems, extreme illumination, and low-texture regolith make long-range navigation on the Moon particularly difficult, as visual-inertial odometry pipelines accumulate drift over extended traverses. To address this challenge, we present a stereo visual simultaneous localization and mapping (SLAM) system that integrates learned feature detection and matching with global constraints from digital elevation models (DEMs). Our front-end employs learning-based feature extraction and matching to achieve robustness to illumination extremes and repetitive terrain, while the back-end incorporates DEM-derived height and surface-normal factors into a pose graph, providing absolute surface constraints that mitigate long-term drift. We validate our approach using both simulated lunar traverse data generated in Unreal Engine and real Moon/Mars analog data collected from Mt. Etna. Results demonstrate that DEM anchoring consistently reduces absolute trajectory error compared to baseline SLAM methods, lowering drift in long-range navigation even in repetitive or visually aliased terrain.

Adam Dai, Guillem Casadesus Vila, Grace Gao

Stanford University William F. Durand Building 496 Lomita Mall Stanford, CA 94305

Simultaneous Localization and Mapping Visual SLAM Robot Navigation Map-Based Localization

Mar 17, 2026

Contingency-Aware Planning via Certified Neural Hamilton-Jacobi Reachability

Hamilton-Jacobi (HJ) reachability provides formal safety guarantees for dynamical systems, but solving high-dimensional HJ partial differential equations limits its use in real-time planning. This paper presents a contingency-aware multi-goal navigation framework that integrates learning-based reachability with sampling-based planning in unknown environments. We use Fourier Neural Operator (FNO) to approximate the solution operator of the Hamilton-Jacobi-Isaacs variational inequality under varying obstacle configurations. We first provide a theoretical under-approximation guarantee on the safe backward reach-avoid set, which enables formal safety certification of the learned reachable sets. Then, we integrate the certified reachable sets with an incremental multi-goal planner, which enforces reachable-set constraints and a recovery policy that guarantees finite-time return to a safe region. Overall, we demonstrate that the proposed framework achieves asymptotically optimal navigation with provable contingency behavior, and validate its performance through real-time deployment on KUKA's youBot in Webots simulation.

Kasidit Muenprasitivej, Derya Aksaray

Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA, 02115

Safe Motion Planning Robot Navigation Reachability Analysis

Mar 16, 2026

FlatLands: Generative Floormap Completion From a Single Egocentric View

A single egocentric image typically captures only a small portion of the floor, yet a complete metric traversability map of the surroundings would better serve applications such as indoor navigation. We introduce FlatLands, a dataset and benchmark for single-view bird's-eye view (BEV) floor completion. The dataset contains 270,575 observations from 17,656 real metric indoor scenes drawn from six existing datasets, with aligned observation, visibility, validity, and ground-truth BEV maps, and the benchmark includes both in- and out-of-distribution evaluation protocols. We compare training-free approaches, deterministic models, ensembles, and stochastic generative models. Finally, we instantiate the task as an end-to-end monocular RGB-to-floormaps pipeline. FlatLands provides a rigorous testbed for uncertainty-aware indoor mapping and generative completion for embodied navigation.

Subhransu S. Bhattacharjee, Dylan Campbell, Rahul Shome

School of Computing, The Australian National University

Robot Navigation Bird's-Eye-View Representation Generative Modeling

Mar 16, 2026

NavGSim: High-Fidelity Gaussian Splatting Simulator for Large-Scale Navigation

Simulating realistic environments for robots is widely recognized as a critical challenge in robot learning, particularly in terms of rendering and physical simulation. This challenge becomes even more pronounced in navigation tasks, where trajectories often extend across multiple rooms or entire floors. In this work, we present NavGSim, a Gaussian Splatting-based simulator designed to generate high-fidelity, large-scale navigation environments. Built upon a hierarchical 3D Gaussian Splatting framework, NavGSim enables photorealistic rendering in expansive scenes spanning hundreds of square meters. To simulate navigation collisions, we introduce a Gaussian Splatting-based slice technique that directly extracts navigable areas from reconstructed Gaussians. Additionally, for ease of use, we provide comprehensive NavGSim APIs supporting multi-GPU development, including tools for custom scene reconstruction, robot configuration, policy training, and evaluation. To evaluate NavGSim's effectiveness, we train a Vision-Language-Action (VLA) model using trajectories collected from NavGSim and assess its performance in both simulated and real-world environments. Our results demonstrate that NavGSim significantly enhances the VLA model's scene understanding, enabling the policy to handle diverse navigation queries effectively.

Jiahang Liu, Yuanxing Duan, Jiazhao Zhang +4

Peking University · Galbot · BAAI

Robotics Simulation Robot Navigation 3D Gaussian Splatting Robot Learning