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.
GNM: A General Navigation Model to Drive Any Robot
9 Pith papers cite this work. Polarity classification is still indexing.
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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.
AR Forcing trains diffusion world models by integrating standard noise prediction loss into an autoregressive loop that uses self-generated predictions as context, reducing train-inference mismatch for improved long-horizon image consistency and trajectory accuracy on navigation datasets.
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.
MVAdapt conditions end-to-end autonomous driving policies on explicit vehicle physics to achieve better zero-shot transfer and few-shot calibration across different vehicles in CARLA simulation.
DreamerV3 enables pixel-to-control policies for drone racing that reach 9 m/s in both simulation and real hardware-in-the-loop tests.
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.
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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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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AR Forcing: Towards Long-Horizon Robot Navigation World Model
AR Forcing trains diffusion world models by integrating standard noise prediction loss into an autoregressive loop that uses self-generated predictions as context, reducing train-inference mismatch for improved long-horizon image consistency and trajectory accuracy on navigation datasets.
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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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MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving
MVAdapt conditions end-to-end autonomous driving policies on explicit vehicle physics to achieve better zero-shot transfer and few-shot calibration across different vehicles in CARLA simulation.
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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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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.