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.
GNM: A General Navigation Model to Drive Any Robot
5 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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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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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.