Uni-Mo generates 7,488 language-annotated quadruped motions via LLM prompts and video diffusion, lifts them to 3D trajectories, and trains policies achieving 96.7% real-robot success on 392 sampled motions.
Imitate and repurpose: Learning reusable robot movement skills from human and animal behaviors, 2022
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
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.
Neuromechanical digital twins—simulated bodies with artificial neural controllers—are maturing into tools that can infer unmeasurable physiological variables, test hypotheses, and link neuroscience with robotics and rehabilitation.
citing papers explorer
-
Unleashing Infinite Motion: Scaling Expressive Quadrupedal Motion via Generative Video Priors
Uni-Mo generates 7,488 language-annotated quadruped motions via LLM prompts and video diffusion, lifts them to 3D trajectories, and trains policies achieving 96.7% real-robot success on 392 sampled motions.
-
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.
-
The embodied brain: Bridging the brain, body, and behavior with biorealistic neuromechanical models
Neuromechanical digital twins—simulated bodies with artificial neural controllers—are maturing into tools that can infer unmeasurable physiological variables, test hypotheses, and link neuroscience with robotics and rehabilitation.