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.
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Instructnav: Zero-shot system for generic instruction navigation in unexplored environment
28 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
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.
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.
SurveilNav integrates robot local perception with multi-view surveillance for improved collaborative object goal navigation and reports SOTA results on HM3D.
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.
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.
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.
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.
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.
SpaAct activates spatial awareness in VLMs using action retrospection, future frame prediction, and progressive curriculum learning to reach SOTA on VLN-CE benchmarks.
FreqCache uses frequency domain properties to adaptively select, refresh, and budget token caches in VLN models, delivering 1.59x speedup with negligible overhead.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries
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.