TAKO demonstrates real-time adversarial takeover of robotic diffusion policies via reusable universal patches on visual inputs, achieving 100% success in steering attacker-chosen trajectories across multiple tasks, encoders, and diffusion methods.
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ViNT: A foundation model for visual navigation
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representative citing papers
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.
Introduces Sentinel Challenge benchmark and CoSaR framework for cooperative spatial reasoning and planning among 3-5 decentralized embodied agents across 14 city-scale scenes.
Formalizes video world models as group actions on states and uses latent regularization with synthesized supervision to enforce consistency, introducing GAC and GAR metrics that improve structural correctness in SOTA models.
Large multimodal models display emerging but limited spatial action capabilities in goal-oriented urban 3D navigation, remaining far from human-level performance with errors diverging rapidly after critical decision points.
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.
AID trains diffusion policies via behavior cloning on existing MAIPP planners followed by RL fine-tuning to achieve faster execution and higher information gain in multi-agent coordination.
UniSim learns a universal real-world simulator from orchestrated diverse datasets, enabling zero-shot deployment of policies trained purely in simulation.
NavWM unifies latent world tokens and anchor-based multimodal trajectory forecasting into a closed-loop planner that improves future state generation and zero-shot 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.
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 VLM-based method for selecting exploration frontiers in robotics achieves up to 24% better map coverage than standard geometric heuristics in simulated indoor environments.
RAE v2 reaches gFID 1.06 on ImageNet-256 in 80 epochs by combining multi-layer encoder sums, complementary REPA targets, and free guidance via output reparameterization.
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.
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
Splatblox creates a traversability-aware ESDF from RGB-LiDAR fusion via Gaussian Splatting, enabling semantic navigation that outperforms prior methods by over 50% success rate in vegetated field trials on quadruped and wheeled robots.
MATT-Diff uses a diffusion model with vision transformer and attention to generate multimodal actions for active multi-target tracking from expert planner demonstrations.
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.
DreamerV3 enables pixel-to-control policies for drone racing that reach 9 m/s in both simulation and real hardware-in-the-loop tests.
OpenVLA achieves 16.5% higher task success than the 55B RT-2-X model across 29 tasks with 7x fewer parameters while enabling effective fine-tuning and quantization without performance loss.
Octo is an open-source transformer-based generalist robot policy pretrained on 800k trajectories that serves as an effective initialization for finetuning across diverse robotic platforms.
DROID is a new 76k-trajectory in-the-wild robot manipulation dataset spanning 564 scenes and 84 tasks that improves policy performance and generalization when used for training.
iCrowdNav encodes egocentric visual observations with occupancy features and human pose intentions to improve DRL policies for crowd navigation, showing better performance than baselines in experiments and real-world tests.
citing papers explorer
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Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies
TAKO demonstrates real-time adversarial takeover of robotic diffusion policies via reusable universal patches on visual inputs, achieving 100% success in steering attacker-chosen trajectories across multiple tasks, encoders, and diffusion methods.
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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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Sentinel: Embodied Cooperative Spatial Reasoning and Planning
Introduces Sentinel Challenge benchmark and CoSaR framework for cooperative spatial reasoning and planning among 3-5 decentralized embodied agents across 14 city-scale scenes.
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World Models as Group Actions
Formalizes video world models as group actions on states and uses latent regularization with synthesized supervision to enforce consistency, introducing GAC and GAR metrics that improve structural correctness in SOTA models.
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How Far Are Large Multimodal Models from Human-Level Spatial Action? A Benchmark for Goal-Oriented Embodied Navigation in Urban Airspace
Large multimodal models display emerging but limited spatial action capabilities in goal-oriented urban 3D navigation, remaining far from human-level performance with errors diverging rapidly after critical decision points.
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STRNet: Visual Navigation with Spatio-Temporal Representation through Dynamic Graph Aggregation
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.
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AID: Agent Intent from Diffusion for Multi-Agent Informative Path Planning
AID trains diffusion policies via behavior cloning on existing MAIPP planners followed by RL fine-tuning to achieve faster execution and higher information gain in multi-agent coordination.
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Learning Interactive Real-World Simulators
UniSim learns a universal real-world simulator from orchestrated diverse datasets, enabling zero-shot deployment of policies trained purely in simulation.
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NavWM: A Unified Navigation World Model for Foresight-Driven Planning
NavWM unifies latent world tokens and anchor-based multimodal trajectory forecasting into a closed-loop planner that improves future state generation and zero-shot navigation.
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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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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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Autonomous Frontier-Based Exploration with VLM Guidance
A VLM-based method for selecting exploration frontiers in robotics achieves up to 24% better map coverage than standard geometric heuristics in simulated indoor environments.
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Improved Baselines with Representation Autoencoders
RAE v2 reaches gFID 1.06 on ImageNet-256 in 80 epochs by combining multi-layer encoder sums, complementary REPA targets, and free guidance via output reparameterization.
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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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Human Cognition in Machines: A Unified Perspective of World Models
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
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Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation
Splatblox creates a traversability-aware ESDF from RGB-LiDAR fusion via Gaussian Splatting, enabling semantic navigation that outperforms prior methods by over 50% success rate in vegetated field trials on quadruped and wheeled robots.
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MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy
MATT-Diff uses a diffusion model with vision transformer and attention to generate multimodal actions for active multi-target tracking from expert planner demonstrations.
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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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Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight
DreamerV3 enables pixel-to-control policies for drone racing that reach 9 m/s in both simulation and real hardware-in-the-loop tests.
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OpenVLA: An Open-Source Vision-Language-Action Model
OpenVLA achieves 16.5% higher task success than the 55B RT-2-X model across 29 tasks with 7x fewer parameters while enabling effective fine-tuning and quantization without performance loss.
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Octo: An Open-Source Generalist Robot Policy
Octo is an open-source transformer-based generalist robot policy pretrained on 800k trajectories that serves as an effective initialization for finetuning across diverse robotic platforms.
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DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
DROID is a new 76k-trajectory in-the-wild robot manipulation dataset spanning 564 scenes and 84 tasks that improves policy performance and generalization when used for training.
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Learning Robot Visual Navigation in Crowds via Intention-Aware Scene Representations
iCrowdNav encodes egocentric visual observations with occupancy features and human pose intentions to improve DRL policies for crowd navigation, showing better performance than baselines in experiments and real-world tests.
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Slow Brain, Fast Planner: Latency-Resilient VLM-Augmented Urban Navigation
A training-free fusion layer enables stale VLM selections to improve a real-time planner's trajectory scoring for urban sidewalk navigation, yielding 30% ADE reduction in challenging scenarios.
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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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Look Further: Socially-Compliant Navigation System in Residential Buildings
Proactive lane-changing at eight meters improves human ratings of robot motion in frontal hallway approaches according to a 42-participant study, with no advantage shown at intersections.
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Drift-Resistant Navigation World Model with Anchored Epipolar Guidance
A generative navigation world model that uses sparse anchored rollout with epipolar constraints to reduce perceptual and geometric drift.
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NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
NavRL++ improves sim-to-real transfer for RL navigation via empirical analysis of perturbations, perturbation-aware fine-tuning, and a Transformer temporal policy, with real-world validation showing outperformance over learning baselines and parity with optimization planners in static cases.
- Rectified Schr\"odinger Bridge Matching for Few-Step Visual Navigation