We achieved high-speed rough-terrain locomotion on ANYmal with an automatic curriculum

Summary

The team at ETH Robotic Systems Lab demonstrated high-speed rough-terrain locomotion on ANYmal using an automatic curriculum learning approach called LP-ACRL. This method dynamically samples terrain types, difficulty levels, and velocity commands based on policy performance, without requiring pre-defined training sequences. The work was presented at RAL 2026.

Editorial context

The work introduces LP-ACRL, a curriculum learning framework that automatically samples terrain types, difficulty levels, and velocity commands based on policy performance, eliminating the need for pre-defined training sequences.

What the source reports

Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.

What remains unknown

Not established in the collected evidence: Environment, Control, Data origin.

Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.

Source excerpts and review record

Automatically extracted; no manual editorial approval recorded.

We achieved high-speed rough-terrain locomotion on ANYmal with an automatic curriculum.
Open source E1

Implications for data suppliers

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Text

We achieved high-speed rough-terrain locomotion on ANYmal with an automatic curriculum.

LP-ACRL automatically samples terrain types, levels, and velocity commands at the correct time based on policy performance, without predefining an order.

🔗https://sites.google.com/view/lp-acrl
🧵RAL 2026