Introduces the TVR active viewpoint-matching task and TVRBench indoor simulation benchmark, where foundation models start at low single-digit success rates but reach 51.4% after visual-action SFT and multi-turn GRPO post-training.
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Objectnav revisited: On evaluation of embodied agents navigating to objects
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LongAct benchmark evaluates long-horizon household task execution from free-form instructions; HoloMind agent raises performance but top VLMs still reach only 59% goal completion and 16% full-task success.
SimWorld Studio deploys an evolving coding agent to create adaptive 3D environments that co-evolve with embodied learners, delivering 18-point success-rate gains over fixed environments in navigation benchmarks.
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.
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.
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.
IntentionNav is a new benchmark showing that VLMs infer intended targets from implicit instructions in 48% of cases but achieve only 25% terminal success and 5.5% grounded success in active navigation.
SynthFun3D generates synthetic 3D functionality segmentation data from action descriptions via object retrieval and scene arrangement, yielding consistent gains of +2.2 mAP, +6.3 mAR, and +5.7 mIoU when augmenting real data for VLM training.
SAGE-Nav decouples LLM global planning from reactive control via hierarchical scene graphs and alignment fusion, reporting SOTA results on i-THOR and RoboTHOR with improved efficiency and zero-shot generalization.
SurveilNav integrates robot local perception with multi-view surveillance for improved collaborative object goal navigation and reports SOTA results on HM3D.
NavWAM is a diffusion-transformer policy that jointly learns future observation prediction, goal-progress values, and action chunks in a shared latent sequence for goal-conditioned visual 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.
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.
Node-wise beam search with expected gain and RRAG graph construction outperforms prior active perception methods by at least 20% on representative tasks.
ESCAPE combines spatio-temporal fusion mapping for depth-free 3D memory with a memory-driven grounding module and adaptive execution policy to reach 65.09% success on ALFRED test-seen long-horizon mobile manipulation tasks.
Habitat-GS integrates 3D Gaussian Splatting scene rendering and Gaussian avatars into Habitat-Sim, yielding agents with stronger cross-domain generalization and effective human-aware navigation.
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.
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.
MerNav's Memory-Execute-Review framework improves success rates in zero-shot object goal navigation by 5-8% over baselines on four datasets while outperforming both training-free and supervised methods on key benchmarks.
C-Nav is a continual visual navigation framework with dual-path anti-forgetting via feature distillation and replay plus adaptive sampling that outperforms baselines on a new continual object navigation benchmark while using less memory.
Transit-Aware Planning (TAP) enriches navigation policies with object transit data on Dynamic Object Maps, raising success rates by 21.1% in MP3D simulation and 18.3% in real-world tests for finding non-stationary targets.
EvolveNav adds an agentic rule memory with UCB retrieval and a memory-guided preflection module to enable continuous improvement in zero-shot object goal navigation, reporting a 10.1% success rate gain over baselines.
Qwen-RobotNav provides a parameterized navigation model trained on 15.6M samples with vision-language co-training that achieves SOTA results on benchmarks and zero-shot transfer to real robots.
citing papers explorer
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Where to Look: Can Foundation Models Reach a Target Viewpoint Through Active Exploration?
Introduces the TVR active viewpoint-matching task and TVRBench indoor simulation benchmark, where foundation models start at low single-digit success rates but reach 51.4% after visual-action SFT and multi-turn GRPO post-training.
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When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution
LongAct benchmark evaluates long-horizon household task execution from free-form instructions; HoloMind agent raises performance but top VLMs still reach only 59% goal completion and 16% full-task success.
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SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning
SimWorld Studio deploys an evolving coding agent to create adaptive 3D environments that co-evolve with embodied learners, delivering 18-point success-rate gains over fixed environments in navigation benchmarks.
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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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LIME: Learning Intent-aware Camera Motion from Egocentric Video
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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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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IntentionNav: A Benchmark for Intent-Driven Object Navigation from Implicit Human Instruction
IntentionNav is a new benchmark showing that VLMs infer intended targets from implicit instructions in 48% of cases but achieve only 25% terminal success and 5.5% grounded success in active navigation.
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Action-guided generation of 3D functionality segmentation data
SynthFun3D generates synthetic 3D functionality segmentation data from action descriptions via object retrieval and scene arrangement, yielding consistent gains of +2.2 mAP, +6.3 mAR, and +5.7 mIoU when augmenting real data for VLM training.
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SAGE-Nav: Leveraging LLM Planning and Alignment Fusion for Hierarchical Scene Graph-Guided Navigation
SAGE-Nav decouples LLM global planning from reactive control via hierarchical scene graphs and alignment fusion, reporting SOTA results on i-THOR and RoboTHOR with improved efficiency and zero-shot generalization.
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SurveilNav: Collaborative Object Goal Navigation with Robot and Surveillance System
SurveilNav integrates robot local perception with multi-view surveillance for improved collaborative object goal navigation and reports SOTA results on HM3D.
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NavWAM: A Navigation World Action Model for Goal-Conditioned Visual Navigation
NavWAM is a diffusion-transformer policy that jointly learns future observation prediction, goal-progress values, and action chunks in a shared latent sequence for goal-conditioned visual navigation.
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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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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.
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An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics
Node-wise beam search with expected gain and RRAG graph construction outperforms prior active perception methods by at least 20% on representative tasks.
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ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation
ESCAPE combines spatio-temporal fusion mapping for depth-free 3D memory with a memory-driven grounding module and adaptive execution policy to reach 65.09% success on ALFRED test-seen long-horizon mobile manipulation tasks.
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Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting
Habitat-GS integrates 3D Gaussian Splatting scene rendering and Gaussian avatars into Habitat-Sim, yielding agents with stronger cross-domain generalization and effective human-aware navigation.
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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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ReMemNav: A Rethinking and Memory-Augmented Framework for Zero-Shot Object Navigation
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.
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MerNav: A Highly Generalizable Memory-Execute-Review Framework for Zero-Shot Object Goal Navigation
MerNav's Memory-Execute-Review framework improves success rates in zero-shot object goal navigation by 5-8% over baselines on four datasets while outperforming both training-free and supervised methods on key benchmarks.
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C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
C-Nav is a continual visual navigation framework with dual-path anti-forgetting via feature distillation and replay plus adaptive sampling that outperforms baselines on a new continual object navigation benchmark while using less memory.
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Personalized Embodied Navigation for Portable Object Finding
Transit-Aware Planning (TAP) enriches navigation policies with object transit data on Dynamic Object Maps, raising success rates by 21.1% in MP3D simulation and 18.3% in real-world tests for finding non-stationary targets.
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EvolveNav: Proactive Preflection and Self-Evolving Memory for Zero-Shot Object Goal Navigation
EvolveNav adds an agentic rule memory with UCB retrieval and a memory-guided preflection module to enable continuous improvement in zero-shot object goal navigation, reporting a 10.1% success rate gain over baselines.
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Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System
Qwen-RobotNav provides a parameterized navigation model trained on 15.6M samples with vision-language co-training that achieves SOTA results on benchmarks and zero-shot transfer to real robots.
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Rethinking Embodied Navigation via Relational Inductive Bias
DB-Nav improves object navigation by factorizing target relations into activation and inhibition biases within a relational exploration graph, yielding higher success rates and SPL on ObjectNav benchmarks.
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IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations
IntentNav is a spatial-visual imitation framework that infers human search intent via frontier labeling to train VLM policies for object navigation, reporting SOTA on MP3D and HM3D benchmarks with zero-shot transfer to wheeled, quadruped, and humanoid robots.
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STEM: Semantic Target Search and Exploration using MAVs in Cluttered Environments
STEM develops a semantically-guided combinatorial planner and active perception pipeline that propagates object priorities to frontier voxels, enabling MAVs to find targets faster than baselines in simulation and real-world tests.
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TravExplorer: Cross-Floor Embodied Exploration via Traversability-Aware 3-D Planning
TravExplorer couples zero-shot semantic guidance with traversability-aware 3-D planning to enable cross-floor object navigation in unseen indoor environments.
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CLUE: Adaptively Prioritized Contextual Cues by Leveraging a Unified Semantic Map for Effective Zero-Shot Object-Goal Navigation
CLUE adaptively weights room-type and object-co-location cues from an LLM to construct a unified semantic value map that improves success rate and efficiency in zero-shot object-goal navigation.
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MiniVLA-Nav v1: A Multi-Scene Simulation Dataset for Language-Conditioned Robot Navigation
MiniVLA-Nav v1 provides 1,174 episodes of language-instructed robot navigation in photorealistic simulations with RGB, depth, segmentation, and expert action data.
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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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Agent AI: Surveying the Horizons of Multimodal Interaction
The paper defines Agent AI as interactive multimodal systems that perceive grounded data and generate embodied actions, arguing this approach can mitigate hallucinations in foundation models.
- OpenFrontier: General Navigation with Visual-Language Grounded Frontiers