CWI decouples MoCap data for upper-body manipulation and lower-body locomotion, using dual discriminators and multi-critic training plus distillation to produce a policy that works from hand poses and velocity commands alone.
Learning perceptive humanoid locomotion over challenging terrain
6 Pith papers cite this work. Polarity classification is still indexing.
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CART learns a vision–proprioception terrain context and uses Temporal Sequence Selection to cut base oscillation by up to 41% in simulation and 22% on Spot outdoors, with a 5% higher sim success rate.
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.
DynaWM adds a world model as dynamics regularizer and momentum targets to teacher-student distillation, yielding better terrain encoding and smoother stair traversal for bipedal-wheeled robots.
GLAD decomposes terrain encoding via coarse-to-fine attention on elevation maps to separate broad awareness from precise foothold selection in perceptive humanoid locomotion.
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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CWI: Composite Humanoid Whole-Body Imitation System for Loco-manipulation
CWI decouples MoCap data for upper-body manipulation and lower-body locomotion, using dual discriminators and multi-critic training plus distillation to produce a policy that works from hand poses and velocity commands alone.
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CART: Context-Aware Terrain Adaptation using Temporal Sequence Selection for Legged Robots
CART learns a vision–proprioception terrain context and uses Temporal Sequence Selection to cut base oscillation by up to 41% in simulation and 22% on Spot outdoors, with a 5% higher sim success rate.
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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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DynaWM: Dynamics-Aware Distillation with World Model and Momentum Targets for Smooth Locomotion over Continuous Stairs
DynaWM adds a world model as dynamics regularizer and momentum targets to teacher-student distillation, yielding better terrain encoding and smoother stair traversal for bipedal-wheeled robots.
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Global-Local Attention Decomposition for Terrain Encoding in Humanoid Perceptive Locomotion
GLAD decomposes terrain encoding via coarse-to-fine attention on elevation maps to separate broad awareness from precise foothold selection in perceptive humanoid locomotion.
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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.