DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
Learning perceptive humanoid locomotion over challenging terrain
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CART integrates vision and body sensing via temporal sequences to improve legged robot stability and success rates on complex terrain by 5% in simulation and up to 45% in real-world tests.
An end-to-end policy learns robust humanoid locomotion directly from noisy depth images via high-fidelity sensor simulation, vision-aware distillation from privileged maps, and terrain-specific multi-critic reward shaping.
citing papers explorer
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DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion
DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
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CART: Context-Aware Terrain Adaptation using Temporal Sequence Selection for Legged Robots
CART integrates vision and body sensing via temporal sequences to improve legged robot stability and success rates on complex terrain by 5% in simulation and up to 45% in real-world tests.
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Now You See That: Learning End-to-End Humanoid Locomotion from Raw Pixels
An end-to-end policy learns robust humanoid locomotion directly from noisy depth images via high-fidelity sensor simulation, vision-aware distillation from privileged maps, and terrain-specific multi-critic reward shaping.