Robotic World Model: Learning to Simulate for Robust Robot Control

from Embodied AI 101 Hosted by Shaoqing Tan

Presents a neural network-based world model for model-based reinforcement learning in robotics, focusing on sim-to-real transfer for quadrupedal and humanoid robots. Enables robust policy optimization through learned environment simulation.

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Robotic World Model: Learning to Simulate for Robust Robot Control

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How long is this episode of Embodied AI 101?

This episode is 19 minutes long.

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This episode was published on June 12, 2026.

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