Axis Robotics

Jianfei Yang Verified account @Jianfei_AI · Nov 5

🚀 Nature does not move in discrete jumps. Why should robot policies?

Super excited to release ODE Policy — perhaps our most exciting breakthrough in my lab this year! 🤖🔥

Think about how humans reach for a cup. Our movements evolve continuously, governed by underlying dynamics. Yet, most robot policies — from Diffusion and Flow Matching to VLAs — learn to predict explicit actions.

What if robots learned how actions evolve, instead of predicting actions?

💡 That's ODE Policy for Embodied AI.

We use a neural Ordinary Differential Equation (ODE) to learn continuous action dynamics, rather than directly generating actions.

One simple idea. Five types of robots. Multiple learning paradigms.

✅ Single-arm manipulation
✅ Dual-arm manipulation
✅ Dexterous hands
✅ Humanoid whole-body control
✅ Aerial robots

And it goes far beyond imitation learning:

⚡ VLA (π0.5): 96.0% vs. 75.5% success after one epoch.
🌍 4.5B World Action Model: 82.6% vs. 24.6% success, with ~4× faster action generation.
🏃 RL: More efficient humanoid whole-body motion learning.

For years, we've focused on scaling robot data, models, and compute.

But what if the next fundamental breakthrough comes from rethinking WHAT robots learn, rather than just HOW MUCH they learn?

Huge congratulations to Jindou Jia and our amazing team! 👏

📄 Paper: arxiv.org/abs/2611.01729
🌐 Project: ntumars.github.io/ODE/

#ODEPolicy #EmbodiedAI #PhysicalAI #RobotLearning #WorldModels #VLA #MARSLab

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