Imagining Locomotion: Learning a Neural World Model for Legged Robots

from Embodied AI 101 · host Shaoqing Tan

A neural dynamics world model paired with model-free RL policies for quadruped and humanoid locomotion in IsaacLab, enabling long-horizon autoregressive prediction and imagined rollouts that outperform pure model-based RL in prediction accuracy, policy learning, and sim-to-real transfer.

Episode metadata supplied by the publisher feed · Published Jul 23, 2026

Embed this episode

Attribution link and audio player

NOW PLAYING

Imagining Locomotion: Learning a Neural World Model for Legged Robots

0:00 33:01

1×

No transcript for this episode yet

We transcribe on demand. Request one and we'll notify you when it's ready — usually under 10 minutes.

Share this episode

Similar Episodes

No similar episodes found.

Similar Podcasts

CISO Perspectives (public) N2K Networks This season on CISO Perspectives, host Kim Jones explores some of the challenges of leading through uncertainty. We explore the complexity of the changing nature of regulation and working with the federal government, the evolution of privacy and fraud, and how emerging technologies like AI and quantum computing are changing cyber. When you don’t know what questions to ask, you’re afraid to ask, or don’t know who to ask, CISO Perspectives provides the foundation for learning in this brave new world.

Frequently Asked Questions

How long is this episode of Embodied AI 101?

This episode is 33 minutes long.

When was this Embodied AI 101 episode published?

This episode was published on July 23, 2026.

Can I download this Embodied AI 101 episode?

Yes. Use the download control on the episode player to save the publisher-provided media file.

URL copied to clipboard!