Two Models, One Gradient: Making World-Model RL Work for Contact-Rich Robots
from Embodied AI 101 Hosted by Shaoqing Tan
Breaks intractable full world-model RL into a heavy global trajectory model combined with a lightweight latent local dynamics approximator, enabling scalable RL for contact-rich humanoid skills without backpropagating through the full model. In this episode of Embodied AI 101, we explore "Two Models, One Gradient: Making World-Model RL Work for Contact-Rich Robots". We break down the research, methodology, and real-world implications for robotics, AI, and physical intelligence. Embodied AI 101 covers the latest research at the intersection of AI and physical intelligence — robotics, manipulation, world models, and the path from digital intelligence to embodied agents.
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Breaks intractable full world-model RL into a heavy global trajectory model combined with a lightweight latent local dynamics approximator, enabling scalable RL for contact-rich humanoid skills without backpropagating through the full model.
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