MotionPyramid learns a stack of latent decoders from motion tracking data to create multi-resolution action interfaces for RL policies in humanoid control, with residual interfaces allowing coarse programs and fine corrections to coexist.
Perpetual humanoid control for real-time simulated avatars
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A multi-condition latent diffusion model transfers human motion styles to diverse humanoid robot contents with physics regularizations, achieving 96% success in real-robot trials on Unitree G1.
A single learned controller called MHC enables real humanoid robots to execute diverse whole-body behaviors from multi-modal inputs via masked target trajectories.
citing papers explorer
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MotionPyramid: Hierarchical Motion Representation and Residual Interfaces
MotionPyramid learns a stack of latent decoders from motion tracking data to create multi-resolution action interfaces for RL policies in humanoid control, with residual interfaces allowing coarse programs and fine corrections to coexist.
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Bionic Human-Motion Style Transfer for Physically Executable Whole-Body Control of Humanoid Robots
A multi-condition latent diffusion model transfers human motion styles to diverse humanoid robot contents with physics regularizations, achieving 96% success in real-robot trials on Unitree G1.
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Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots
A single learned controller called MHC enables real humanoid robots to execute diverse whole-body behaviors from multi-modal inputs via masked target trajectories.