Robotic World Model: Learning to Simulate for Robust Robot Control

from Embodied AI 101 · host 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.

Episode metadata supplied by the publisher feed · Published Jun 12, 2026

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Robotic World Model: Learning to Simulate for Robust Robot Control

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