{"total":28,"items":[{"citing_arxiv_id":"2607.02417","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LIME: Learning Intent-aware Camera Motion from Egocentric Video","primary_cat":"cs.RO","submitted_at":"2026-07-02T16:48:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"LIME formulates language-conditioned camera motion as predicting SE(3) target poses from RGB and intent text, using mined multi-intent supervision from egocentric video and a flow-matching pose head.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.30367","ref_index":69,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation","primary_cat":"cs.RO","submitted_at":"2026-06-29T14:33:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"FutureNav proposes a 4B-scale VLM that jointly optimizes action prediction, inverse/forward dynamics, and future state generation for VLN and reports SOTA results on multiple benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.27807","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SpikeVLA: Vision-Language-Action Models with Spiking Neural Networks","primary_cat":"cs.RO","submitted_at":"2026-06-26T07:45:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SpikeVLA replaces transformer components in VLA models with spiking vision encoder, multi-modal LLM, and action policy network to reduce energy consumption while maintaining competitive performance on navigation tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.25119","ref_index":43,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SurveilNav: Collaborative Object Goal Navigation with Robot and Surveillance System","primary_cat":"cs.RO","submitted_at":"2026-06-23T19:45:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SurveilNav integrates robot local perception with multi-view surveillance for improved collaborative object goal navigation and reports SOTA results on HM3D.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22424","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"FlowDec: Temporal Conditional Flow Decorruptor for Robust Continuous Vision-Language Navigation","primary_cat":"cs.CV","submitted_at":"2026-06-21T10:24:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"FlowDec is a novel image restoration framework using hybrid temporal conditioning and action-centroid filtering that claims to outperform prior decorruption methods on navigation accuracy and latency in VLN-CE.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10927","ref_index":51,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AllDayNav: Lifelong Navigation via Real-World Reinforcement Learning","primary_cat":"cs.RO","submitted_at":"2026-06-09T14:35:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"AllDayNav encodes scene dynamics into a large model's parameters via RL and a multimodal memory, achieving near-100% success rates in lifelong navigation and outperforming map-based and VLM baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07244","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Beyond Waypoints: A Trajectory-Centric Waypointing Paradigm for Vision-Language Navigation","primary_cat":"cs.RO","submitted_at":"2026-06-05T13:11:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The paper introduces a Trajectory Waypoint paradigm with a TSDF-guided diffusion policy and trajectory-enhanced navigator that achieves better performance on VLN-CE benchmarks by ensuring waypoint reachability and planning-execution consistency.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.03175","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Ask When It Pays: Cost-Aware Open-Ended Interaction for Instance Goal Navigation","primary_cat":"cs.CV","submitted_at":"2026-06-02T05:31:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Proposes cost-aware question selection for ambiguous object navigation via information-gain analysis on corpora, a cost-penalizing benchmark, and a zero-shot MLLM agent.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01621","ref_index":31,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Goal2Pixel: Grounding Goals to Pixels for Vision-Language Navigation","primary_cat":"cs.CV","submitted_at":"2026-06-01T03:12:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Goal2Pixel grounds VLN-CE goals to image pixels via VLM prediction plus keyframe memory, reaching 54.1% SR on R2R-CE Val-Unseen with 7.75 calls per episode versus 46.62 for action prediction.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.28237","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"POINav: Benchmarking and Enhancing Final-Meters Arrival in Real-World Vision-Language Navigation","primary_cat":"cs.RO","submitted_at":"2026-05-27T09:50:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"POINav-Bench provides the first high-fidelity real-world benchmark for POI-goal VLN using 3DGS reconstructions of 126k m² with 163 POIs, supported by a Brain-Action framework and 70K real signage-entrance dataset.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22036","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GA-VLN: Geometry-Aware BEV Representation for Efficient Vision-Language Navigation","primary_cat":"cs.CV","submitted_at":"2026-05-21T06:20:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GA-VLN builds a geometry-aware BEV representation from RGB-D inputs plus 3D foundation model features to deliver state-of-the-art vision-language navigation using only navigation data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19634","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"P2DNav: Panorama-to-Downview Reasoning for Zero-shot Vision-and-Language Navigation","primary_cat":"cs.CV","submitted_at":"2026-05-19T10:18:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"P2DNav proposes a three-part hierarchical framework (panorama-to-downview reasoning, sliding-window dialogue memory, and reflective reorientation) that reports large success-rate gains on the R2R-CE zero-shot VLN benchmark.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17249","ref_index":23,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SEDualVLN: A Spatially-Enhanced Dual-System for Vision-Language Navigation","primary_cat":"cs.RO","submitted_at":"2026-05-17T04:12:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SEDualVLN introduces a spatially-enhanced dual-system VLN architecture that achieves state-of-the-art results on VLN-CE benchmarks through coordinated VLM action generation and MLLM waypoint planning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16979","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints","primary_cat":"cs.RO","submitted_at":"2026-05-16T12:58:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"NORM-Nav is a zero-shot framework that parses natural language behavioral constraints with an LLM, grounds them via vision-LiDAR, and encodes them as multi-layer costmaps for grid-based robot navigation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06223","ref_index":34,"ref_count":3,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries","primary_cat":"cs.AI","submitted_at":"2026-05-07T13:19:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ProCompNav builds a candidate pool from ambiguous queries then uses pool-splitting binary questions for disambiguation, improving success rate and shortening responses on CoIN-Bench and TextNav.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27620","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SpaAct: Spatially-Activated Transition Learning with Curriculum Adaptation for Vision-Language Navigation","primary_cat":"cs.CV","submitted_at":"2026-04-30T09:09:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SpaAct activates spatial awareness in VLMs using action retrospection, future frame prediction, and progressive curriculum learning to reach SOTA on VLN-CE benchmarks.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Instructnav: Zero-shot system for generic instruction navigation in unexplored environment.arXiv preprint arXiv:2406.04882(2024). [45] Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, et al. 2024. Deepseek-vl: towards real- world vision-language understanding.arXiv preprint arXiv:2403.05525(2024). [46] Kailin Lyu, Kangyi Wu, Pengna Li, Xiuyu Hu, Qingyi Si, Cui Miao, Ning Yang, Zi- hang Wang, Long Xiao, Lianyu Hu, et al. 2026. HiMemVLN: Enhancing Reliability of Open-Source Zero-Shot Vision-and-Language Navigation with Hierarchical Memory System.arXiv preprint arXiv:2603.14807(2026). [47] Chih-Yao Ma, Jiasen Lu, Zuxuan Wu, Ghassan AlRegib, Zsolt Kira, Richard Socher,"},{"citing_arxiv_id":"2604.24391","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching","primary_cat":"cs.RO","submitted_at":"2026-04-27T12:20:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FreqCache uses frequency domain properties to adaptively select, refresh, and budget token caches in VLN models, delivering 1.59x speedup with negligible overhead.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17473","ref_index":76,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Dual-Anchoring: Addressing State Drift in Vision-Language Navigation","primary_cat":"cs.CV","submitted_at":"2026-04-19T15:03:38+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Privatar partitions VR avatar reconstruction via frequency-domain decomposition, keeping sensitive components local and offloading the rest with distribution-aware minimal perturbation noise, achieving 2.37x throughput with provable privacy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17407","ref_index":87,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Think before Go: Hierarchical Reasoning for Image-goal Navigation","primary_cat":"cs.RO","submitted_at":"2026-04-19T12:30:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"HRNav decomposes image-goal navigation into VLM-based short-horizon planning and RL-based execution with a wandering suppression penalty to improve performance in complex unseen settings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12872","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"OVAL: Open-Vocabulary Augmented Memory Model for Lifelong Object Goal Navigation","primary_cat":"cs.RO","submitted_at":"2026-04-14T15:22:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"OVAL introduces an open-vocabulary memory model with structured descriptors and multi-value frontier scoring to enable efficient lifelong object goal navigation in unseen settings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.02829","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"STRNet: Visual Navigation with Spatio-Temporal Representation through Dynamic Graph Aggregation","primary_cat":"cs.CV","submitted_at":"2026-04-03T07:50:53+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"STRNet improves goal-conditioned visual navigation by replacing simplistic encoders and pooling with a spatio-temporal fusion module that performs spatial graph reasoning and hybrid temporal modeling.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"context pooling encoder (NoMaD [39]) produces entangled em- beddings, mixing near- and far-to-goal states. (b) Proposed STR- Net, using graph-based spatial aggregation and hybrid spatio- temporal fusion, yields clearly separated embeddings, effectively capturing spatial and temporal cues. conditioned policies [51], behavior cloning strategies [37], or instruction-following frameworks [23]. While these methods have achieved impressive results, they often rely on visual encoders originally designed for generic com- puter vision or video understanding tasks rather than for the rapid, fine-grained control decisions demanded by mobile robots. In practice, these encoders are typically ImageNet- pretrained CNNs followed by simple temporal pooling,"},{"citing_arxiv_id":"2603.26788","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ReMemNav: A Rethinking and Memory-Augmented Framework for Zero-Shot Object Navigation","primary_cat":"cs.RO","submitted_at":"2026-03-25T09:07:32+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ReMemNav improves zero-shot object navigation success and efficiency by integrating episodic memory and rethinking with VLMs, achieving SR/SPL gains of 1.7%/7.0% on HM3D v0.1, 18.2%/11.1% on HM3D v0.2, and 8.7%/7.9% on MP3D.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.20530","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Memory Over Maps: 3D Object Localization Without Reconstruction","primary_cat":"cs.RO","submitted_at":"2026-03-20T21:57:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A map-free localization method stores posed RGB-D keyframes, retrieves and re-ranks them with a VLM, then fuses sparse depth for on-demand 3D target estimates, matching reconstruction-based performance on navigation benchmarks with far lower build cost.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.16947","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LightZeroNav: Zero-Shot Vision Language Navigation in Continuous Environments Based on Lightweight VLMs","primary_cat":"cs.CV","submitted_at":"2026-03-16T14:07:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LightZeroNav decomposes zero-shot VLN-CE into modules that reduce input redundancy, improve progress tracking from noisy memory, and separate action execution from stage transitions, allowing an 8B VLM to match GPT-4o performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.21714","ref_index":15,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AstraNav-World: World Model for Foresight Control and Consistency","primary_cat":"cs.CV","submitted_at":"2025-12-25T15:31:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AstraNav-World unifies diffusion video generation and vision-language action planning in a single bidirectional model that improves trajectory accuracy, success rates, and zero-shot real-world adaptation in embodied navigation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.17207","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SING3R-SLAM: Submap-based Indoor Monocular Gaussian SLAM with 3D Reconstruction Priors","primary_cat":"cs.CV","submitted_at":"2025-11-21T12:40:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SING3R-SLAM adds submap-level global alignment and reconstruction priors to a Gaussian map to reduce drift and improve local geometry in monocular indoor SLAM.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.13451","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MapNav: A Novel Memory Representation via Annotated Semantic Maps for Vision-and-Language Navigation","primary_cat":"cs.RO","submitted_at":"2025-02-19T05:52:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MapNav uses annotated semantic maps as memory for VLN agents, claiming SOTA results in simulation and real-world tests while promising code and data release.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2412.06224","ref_index":61,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks","primary_cat":"cs.RO","submitted_at":"2024-12-09T05:55:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Uni-NaVid unifies diverse embodied navigation tasks into one video-based vision-language-action model trained on 3.6 million samples from four sub-tasks, achieving state-of-the-art performance on benchmarks and real-world tests.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"tasks are distinct from each other, with varying task settings and objectives. Specifically, for the human-following task, we construct a new language-guided human-following benchmark for data collection and evaluation. Finally, we collect 3.6M Methods Action Embodied Navigation Tasks D.E. C.E.VLN [44] ObjNav [76] EQA [90] Follow [68] VLMaps [34]✓ ✓ ✓ NaviLLM [114]✓ ✓ ✓ ✓ InstructNav [61]✓ ✓ ✓ Poliformer [106] ✓ ✓ ✓ Uni-NaVid ✓ ✓ ✓ ✓ ✓ TABLE I: Task and setting comparison. Uni-NaVid is de- veloped to address four embodied navigation tasks, generating action outputs in continuous environments. C.E.: Continuous Environment; D.E.: Discrete Environment. navigation samples based on diverse navigation tasks with different simulation environments."}],"limit":50,"offset":0}