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SkyJEPA (arXiv:2606.23444, posted 2026-06-24) is a long-horizon world model for…

2026-06-24 · 14:21 UTC ·@randall_balestr ·1 min read

Brief

SkyJEPA is a quadrotor world model (arXiv:2606.23444, 2026-06-24) that learns long-horizon latent dynamics and uses a physics-inspired prober to recover interpretable states. The paper claims accurate long-horizon prediction, reduced compounding error, real-time closed-loop outdoor flight control, and zero-shot sim-to-real generalization to propeller switches and payload changes without fine-tuning; code and project links are provided.

Why it matters

SkyJEPA (arXiv:2606.23444, posted 2026-06-24) is a long-horizon world model for quadrotor control introduced by Pratyaksh Rao with collaborators @kevinghstz, @randall_balestr, @ylecun, and @loiannog.

Key details

  • The approach learns dynamics in latent space and uses a physics-inspired prober to recover meaningful states, producing accurate long-horizon predictions, less compounding error, and smoother latent trajectories for real-time closed-loop outdoor flight control.
  • Authors claim zero-shot sim-to-real transfer without real-world fine-tuning: robustness to corrupted/noisy inputs and generalization to unseen scenarios such as propeller switching and payload changes; project page (pratyaksh10.github.io/skyjepa) and code (github.com/arplaboratory/Sky...) are available.
Source evidence

It's a bird, it's a plane, it's a JEPA! Congrats on that great work that brought SIGReg and JEPAs to the sky--in the real world! Check out the paper!
arxiv.org/abs/2606.23444

Link

SkyJEPA: Learning Long-Horizon World Models for Zero-Shot...

Accurate dynamics models are critical for informed decision-making in robotic systems, particularly for agile aerial vehicles operating under uncertainty. Neural network dynamics models are...
arxiv.org

Pratyaksh Rao (@PratyakshRao5)

What should a world model for agile quadrotor control actually provide?

📄 Arxiv: arxiv.org/pdf/2606.23444
🌐 Project: pratyaksh10.github.io/skyjep…
💻 Code: github.com/arplaboratory/Sky…

Excited to share SkyJEPA:
Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors

A useful quadrotor world model should provide:

✅ Accurate long-horizon prediction
✅ Interpretability
✅ Real-time inference for closed-loop control
✅ Zero-shot task generalization

SkyJEPA learns dynamics in latent space, uses a physics-inspired prober to recover meaningful states, and enables real-time control in outdoor flights.

🔑 Takeaways:
• Less compounding error
• Smoother latent trajectories
• Robustness to corrupted/noisy inputs
• Generalization to unseen settings like propeller switching and payload changes
• Zero-shot sim-to-real transfer without real-world fine-tuning to scenarios not seen during training such as propeller switching and payload changes.

Huge thanks to my collaborators: @kevinghstz, @randall_balestr, @ylecun, and @loiannog

Robotics #Quadrotors #WorldModels #Sim2Real #JEPA #RepresentationLearning #Drones

Video

— https://nitter.net/PratyakshRao5/status/2069462393244266638#m