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.
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NaVILA: Legged Robot Vision-Language-Action Model for Naviga- tion
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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.
AwareVLN introduces a structural reasoning module and automatic data engine with progress division to equip VLN agents with self-awareness of agent state and task progress, outperforming prior methods on Habitat datasets.
OmniNavBench is a unified benchmark for general-purpose navigation featuring composite multi-skill instructions, support for humanoid, quadrupedal and wheeled robots, and 1779 human teleoperated trajectories across 170 environments.
VLN-Cache delivers up to 1.52x faster inference in VLN models by using view-aligned remapping for geometric consistency and a task-relevance saliency filter to manage semantic changes during navigation.
Phi-Nav generates path-level hindsight instructions from on-policy exploration trajectories to supply additional semantic supervision for vision-language navigation agents.
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.
VEGA reconstructs local geometry from monocular egocentric video to create supervised trajectories that train a flow-matching VLA policy, yielding lower collision rates on a new benchmark and in real-world tests.
FlowPilot combines anchored flow matching for multimodal action pre-training with human-in-the-loop preference learning to improve long-horizon monocular sidewalk navigation, reporting 42% success in simulation and reduced interruptions in real-world tests.
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.
A zero-shot unified agent for VLN-CE, ObjectNav, EQA and Aerial-VLN on wheeled, quadruped, humanoid and UAV platforms that translates language and vision inputs into actions via MLLMs plus TDM and SCB mechanisms, matching trained foundation models on multiple benchmarks.
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.
SpaAct activates spatial awareness in VLMs using action retrospection, future frame prediction, and progressive curriculum learning to reach SOTA on VLN-CE benchmarks.
A coupled world-agent framework uses 3D Gaussian reconstruction and first-person RGB-D perception with iterative planning to enable goal-directed, collision-avoiding humanoid behavior in novel reconstructed scenes.
HiRO-Nav adaptively triggers reasoning only on high-entropy actions via a hybrid training pipeline and shows better success-token trade-offs than always-reason or never-reason baselines on the CHORES-S benchmark.
DreamTIP adds LLM-identified task-invariant properties as auxiliary targets in Dreamer's world model plus a mixed-replay adaptation step, delivering 28.1% average simulated transfer gains and 100% real-world climb success versus 10% for baselines.
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.
Semantic progress reasoning predicts instruction-style advancement from visual history to guide policies, yielding state-of-the-art success and efficiency on R2R-CE and RxR-CE.
R2RGen introduces a simulator-free three-stage pipeline that parses, augments, and post-processes real pointcloud observation-action pairs to improve spatial generalization in robotic manipulation policies.
Real-time chunking (RTC) allows diffusion- and flow-based action chunking policies to execute smoothly and asynchronously, maintaining high success rates on dynamic tasks even with significant inference latency.
π_{0.5} is a VLA model that achieves long-horizon dexterous manipulation in entirely new homes through co-training on heterogeneous tasks and multi-source data including web and semantic predictions.
FAST applies discrete cosine transform to robot action sequences for efficient tokenization, enabling autoregressive VLAs to succeed on high-frequency dexterous tasks and scale to 10k hours of data while matching diffusion VLA performance with up to 5x faster training.
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.
SALSA aligns social features and adds future-risk signals in VLA models to cut near-collisions by 86.4% and raise social accuracy from 53% to 93% on SCAND and real robots.
citing papers explorer
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Beyond Waypoints: A Trajectory-Centric Waypointing Paradigm for Vision-Language Navigation
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.
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POINav: Benchmarking and Enhancing Final-Meters Arrival in Real-World Vision-Language Navigation
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.
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AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation
AwareVLN introduces a structural reasoning module and automatic data engine with progress division to equip VLN agents with self-awareness of agent state and task progress, outperforming prior methods on Habitat datasets.
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Beyond Isolation: A Unified Benchmark for General-Purpose Navigation
OmniNavBench is a unified benchmark for general-purpose navigation featuring composite multi-skill instructions, support for humanoid, quadrupedal and wheeled robots, and 1779 human teleoperated trajectories across 170 environments.
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Path-level Hindsight Instructions for Semantic Exploration in Vision-Language Navigation
Phi-Nav generates path-level hindsight instructions from on-policy exploration trajectories to supply additional semantic supervision for vision-language navigation agents.
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SpikeVLA: Vision-Language-Action Models with Spiking Neural Networks
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.
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VEGA: Learning Navigation VLAs from In-the-Wild Egocentric Video with Geometric Trajectory Supervision
VEGA reconstructs local geometry from monocular egocentric video to create supervised trajectories that train a flow-matching VLA policy, yielding lower collision rates on a new benchmark and in real-world tests.
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From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation
FlowPilot combines anchored flow matching for multimodal action pre-training with human-in-the-loop preference learning to improve long-horizon monocular sidewalk navigation, reporting 42% success in simulation and reduced interruptions in real-world tests.
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Goal2Pixel: Grounding Goals to Pixels for Vision-Language Navigation
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.
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Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation
A zero-shot unified agent for VLN-CE, ObjectNav, EQA and Aerial-VLN on wheeled, quadruped, humanoid and UAV platforms that translates language and vision inputs into actions via MLLMs plus TDM and SCB mechanisms, matching trained foundation models on multiple benchmarks.
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GA-VLN: Geometry-Aware BEV Representation for Efficient Vision-Language Navigation
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.
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SpaAct: Spatially-Activated Transition Learning with Curriculum Adaptation for Vision-Language Navigation
SpaAct activates spatial awareness in VLMs using action retrospection, future frame prediction, and progressive curriculum learning to reach SOTA on VLN-CE benchmarks.
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Visually-grounded Humanoid Agents
A coupled world-agent framework uses 3D Gaussian reconstruction and first-person RGB-D perception with iterative planning to enable goal-directed, collision-avoiding humanoid behavior in novel reconstructed scenes.
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HiRO-Nav: Hybrid ReasOning Enables Efficient Embodied Navigation
HiRO-Nav adaptively triggers reasoning only on high-entropy actions via a hybrid training pipeline and shows better success-token trade-offs than always-reason or never-reason baselines on the CHORES-S benchmark.
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Learning Task-Invariant Properties via Dreamer: Enabling Efficient Policy Transfer for Quadruped Robots
DreamTIP adds LLM-identified task-invariant properties as auxiliary targets in Dreamer's world model plus a mixed-replay adaptation step, delivering 28.1% average simulated transfer gains and 100% real-world climb success versus 10% for baselines.
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AstraNav-World: World Model for Foresight Control and Consistency
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.
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Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation
Semantic progress reasoning predicts instruction-style advancement from visual history to guide policies, yielding state-of-the-art success and efficiency on R2R-CE and RxR-CE.
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R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation
R2RGen introduces a simulator-free three-stage pipeline that parses, augments, and post-processes real pointcloud observation-action pairs to improve spatial generalization in robotic manipulation policies.
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Real-Time Execution of Action Chunking Flow Policies
Real-time chunking (RTC) allows diffusion- and flow-based action chunking policies to execute smoothly and asynchronously, maintaining high success rates on dynamic tasks even with significant inference latency.
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$\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
π_{0.5} is a VLA model that achieves long-horizon dexterous manipulation in entirely new homes through co-training on heterogeneous tasks and multi-source data including web and semantic predictions.
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FAST: Efficient Action Tokenization for Vision-Language-Action Models
FAST applies discrete cosine transform to robot action sequences for efficient tokenization, enabling autoregressive VLAs to succeed on high-frequency dexterous tasks and scale to 10k hours of data while matching diffusion VLA performance with up to 5x faster training.
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FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation
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.
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Act on What You See: Unlocking Safe Social Navigation in Vision-Language-Action Models
SALSA aligns social features and adds future-risk signals in VLA models to cut near-collisions by 86.4% and raise social accuracy from 53% to 93% on SCAND and real robots.
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G-DRAGON: Geospatial Reasoning and Dynamic Planning for Retrieval-Augmented Outdoor Navigation
G-DRAGON framework maps language commands to OSM coordinates via lightweight LLM for global planning and uses frontier exploration for local targets, outperforming baselines in simulation and completing real UGV person-search missions up to 500m.
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SEDualVLN: A Spatially-Enhanced Dual-System for Vision-Language Navigation
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.
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Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
Terrain-consistent reference modulation during RL training yields SE(2)-controllable humanoid locomotion policies that improve tracking in simulation and enable over 70 m closed-loop autonomous navigation on rough terrain and stairs on the Unitree G1 with onboard computation.
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Think before Go: Hierarchical Reasoning for Image-goal Navigation
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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A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
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NVILA: Efficient Frontier Visual Language Models
NVILA improves on VILA with a scale-then-compress visual token strategy and full-lifecycle efficiency optimizations, matching or exceeding leading VLMs on image and video benchmarks while reducing training cost 1.9-5.1x and latencies 1.2-2.8x.
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Vision-Language Models for Deployable Social Robot Navigation: Bridging Semantic Reasoning and Low-Level Control
Survey organizing VLM-based social robot navigation into reasoning, planning, and bridging components with a proposed roadmap for hybrid deployable systems.
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Ask When It Pays: Cost-Aware Open-Ended Interaction for Instance Goal Navigation
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.
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PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs
PEACE decouples single-pass LLM planning from PX4 execution via ROS 2 and a constraint layer, with modular 3D perception, and shows feasibility in Gazebo SITL with improved explainability and fewer LLM calls.
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Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
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Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation
HSAN integrates hierarchical semantic graphs, optimal transport-based goal selection, and graph-aware RL to claim SOTA results on VLN-CE tasks.