HM3D offers 1000 building-scale 3D environments that are larger and higher-fidelity than existing datasets, enabling better-performing embodied AI agents for tasks like PointGoal navigation.
Room-across-room: Multilingual vision- and-language navigation with dense spatiotemporal ground- ing
11 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
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.
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.
NavOL collects expert trajectory labels online from a global planner during policy rollouts in simulation to train a diffusion navigation policy, mitigating distribution shift and improving performance on visual navigation tasks.
SpaAct activates spatial awareness in VLMs using action retrospection, future frame prediction, and progressive curriculum learning to reach SOTA on VLN-CE benchmarks.
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.
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.
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.
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.
citing papers explorer
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Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
HM3D offers 1000 building-scale 3D environments that are larger and higher-fidelity than existing datasets, enabling better-performing embodied AI agents for tasks like PointGoal navigation.
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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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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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NavOL: Navigation Policy with Online Imitation Learning
NavOL collects expert trajectory labels online from a global planner during policy rollouts in simulation to train a diffusion navigation policy, mitigating distribution shift and improving performance on visual navigation tasks.
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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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Dual-Anchoring: Addressing State Drift in Vision-Language Navigation
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.
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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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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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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.
- FlowDec: Temporal Conditional Flow Decorruptor for Robust Continuous Vision-Language Navigation