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.
Learning to explore using active neural slam
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
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.
CoCoSI is a training-free multi-agent system for collaborative cognitive map construction that improves spatial understanding in arbitrary pretrained MLLMs.
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.
PLMD applies a denoising diffusion model to predict labels for unknown map regions, allowing goal localization in unexplored environments by substituting completed labels into existing navigation pipelines.
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.
BrainMem equips LLM-based embodied planners with working, episodic, and semantic memory that evolves interaction histories into retrievable knowledge graphs and guidelines, raising success rates on long-horizon 3D benchmarks.
Develops a game-theoretic estimator for true information gain in active perception that achieves sub-linear regret and shows average gains of 7% information gain and 42% error reduction across simulated and real robot experiments.
Proposes a hierarchical navigation framework with viewpoint-aware action nodes, cross-graph memory, and learning-based policy for quadrotor InstanceImageNav, claiming improvements over baselines in simulation and real-world validation.
citing papers explorer
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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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CoCoSI: Collaborative Cognitive Map Construction for Spatial Intelligence
CoCoSI is a training-free multi-agent system for collaborative cognitive map construction that improves spatial understanding in arbitrary pretrained MLLMs.
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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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Plug-and-Play Label Map Diffusion for Universal Goal-Oriented Navigation
PLMD applies a denoising diffusion model to predict labels for unknown map regions, allowing goal localization in unexplored environments by substituting completed labels into existing navigation pipelines.
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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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BrainMem: Brain-Inspired Evolving Memory for Embodied Agent Task Planning
BrainMem equips LLM-based embodied planners with working, episodic, and semantic memory that evolves interaction histories into retrievable knowledge graphs and guidelines, raising success rates on long-horizon 3D benchmarks.
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An Active Perception Game for Robust Exploration
Develops a game-theoretic estimator for true information gain in active perception that achieves sub-linear regret and shows average gains of 7% information gain and 42% error reduction across simulated and real robot experiments.
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Flying to Image-Specified Objects: 3D Quadrotor Navigation via Cross-Graph Memory and Viewpoint Planning
Proposes a hierarchical navigation framework with viewpoint-aware action nodes, cross-graph memory, and learning-based policy for quadrotor InstanceImageNav, claiming improvements over baselines in simulation and real-world validation.