USS is an end-to-end framework for embodied visual tracking that fuses text, point, box, and mask prompts via modality-specific encoders and hybrid attention, augmented by a latent world model, and demonstrates higher success rates with spatial cues on real robots and competitive simulation performa
Dreamwalker: Mental planning for continuous vision-language navigation
11 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 11representative citing papers
RescueBench is a new diagnostic benchmark for multi-stage embodied search-and-rescue that shows no tested baselines complete the hardest tasks and identifies exploration and memory as independent failure modes.
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.
GuardVLA embeds a stealthy backdoor watermark in VLAs via secret messages in visual data and uses a swap-and-detect mechanism for post-release ownership verification that preserves task performance.
OA-VAT improves visual active tracking by combining instance-level prototype discrimination with occlusion-aware diffusion planning, reporting gains over prior SOTA on simulated and real drone benchmarks.
AsyncShield restores VLA geometric intent from latency via kinematic pose mapping and uses PPO-Lagrangian to balance tracking with LiDAR safety constraints in a plug-and-play module.
A VLA model with Cross-Depth Fusion tracking head and TraCon register unifies needle tracking and adaptive insertion control, outperforming prior trackers and manual operation in experiments.
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.
EffiNav combines depth and vision-language inputs for efficient object goal navigation, matching or exceeding baselines on success rate and path-length-weighted success across simulation benchmarks and real-robot tests.
GN0 curates GN-Matrix dataset, builds 3DGS simulator and GN-Bench, and trains BAE model via supervised learning plus DAgger and RL to unify VLN tasks and outperform prior methods on GN-Bench and VLN-CE.
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
citing papers explorer
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USS: Unified Spatial-Semantic Prompts for Embodied Visual Tracking with Latent Dynamics Learning
USS is an end-to-end framework for embodied visual tracking that fuses text, point, box, and mask prompts via modality-specific encoders and hybrid attention, augmented by a latent world model, and demonstrates higher success rates with spatial cues on real robots and competitive simulation performa
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RescueBench: Can Embodied Agents Save Lives in the Wild ?
RescueBench is a new diagnostic benchmark for multi-stage embodied search-and-rescue that shows no tested baselines complete the hardest tasks and identifies exploration and memory as independent failure modes.
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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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Towards Backdoor-Based Ownership Verification for Vision-Language-Action Models
GuardVLA embeds a stealthy backdoor watermark in VLAs via secret messages in visual data and uses a swap-and-detect mechanism for post-release ownership verification that preserves task performance.
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Instance-level Visual Active Tracking with Occlusion-Aware Planning
OA-VAT improves visual active tracking by combining instance-level prototype discrimination with occlusion-aware diffusion planning, reporting gains over prior SOTA on simulated and real drone benchmarks.
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AsyncShield: A Plug-and-Play Edge Adapter for Asynchronous Cloud-based VLA Navigation
AsyncShield restores VLA geometric intent from latency via kinematic pose mapping and uses PPO-Lagrangian to balance tracking with LiDAR safety constraints in a plug-and-play module.
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A Vision-Language-Action Model for Adaptive Ultrasound-Guided Needle Insertion and Needle Tracking
A VLA model with Cross-Depth Fusion tracking head and TraCon register unifies needle tracking and adaptive insertion control, outperforming prior trackers and manual operation in experiments.
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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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EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
EffiNav combines depth and vision-language inputs for efficient object goal navigation, matching or exceeding baselines on success rate and path-length-weighted success across simulation benchmarks and real-robot tests.
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GN0: Toward a Unified Paradigm for Generation, Evaluation, and Policy Learning in Visual-Language Navigation
GN0 curates GN-Matrix dataset, builds 3DGS simulator and GN-Bench, and trains BAE model via supervised learning plus DAgger and RL to unify VLN tasks and outperform prior methods on GN-Bench and VLN-CE.
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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.