4. Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization -> Monday Workshop on Foundation Models for Robotic Design & Friday Workshop on Challenges and Applications Prospects for

Paper: ias.informatik.tu-darmstadt.de/uploads/Team...

3. Evaluation of an Actuated Spine in Agile Quadruped Locomotion -> Friday Workshop on Climbing Robotics

Paper: ias.informatik.tu-darmstadt.de/uploads/Team...

2. Bridge the Gap: Enhancing Quadruped Locomotion with Vertical Ground Perturbations -> Wednesday Session, Bioinspired Robot Learning

Website: nico-bohlinger.github.io/bridge_the_g...

1. Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion -> Wednesday Session, Legged Robots 1 - Locomotion

Website: nico-bohlinger.github.io/gait_in_eigh...

Many thanks to @jan-peters.bsky.social and @ias-tudarmstadt.bsky.social for all the support!

If you are interested in massive multi-embodiment learning, come and chat with me at:
- Today, WS Sim-to-Real Transfer for Humanoid Robots
at Humanoids2025
- Oct 20th, WS Foundation Models for Robotic Design
at IROS2025
- Oct 24th, WS Reconfigurable Modular Robots
at IROS2025

👏 Huge thanks to everyone involved:
Bo Ai, Liu Dai, Dichen Li, Tongzhou Mu, Zhanxin Wu, K. Fay, Henrik I. Christensen, @jan-peters.bsky.social and Hao Su

Or come to my talk @ International Symposium on Adaptive Motion of Animals and Machines and LokoAssist Symposium (AMAM) on Friday at TU Darmstadt

Thanks to @ias-tudarmstadt.bsky.social, @jan-peters.bsky.social

If you want to know how to create a neural network architecture to train one policy to control any robot embodiment, check out: nico-bohlinger.github.io/one_policy_t...

🦾 By combining EPVFs with massive parallelism and careful regularization, we close the gap with state-of-the-art DRL in complex environments.
🔗 Full paper: arxiv.org/abs/2502.11949
✨ Finally, many thanks to @jan-peters.bsky.social and
@ias-tudarmstadt.bsky.social for the support!

⚙️ Architecture matters
We also explored architectures like Universal Neural Functionals (UNF) and action-based representations ("Probing").
And yes, our scaled EPVFs are competitive with PPO and SAC in their final performance.

⚡ But it's not just about size
Key ingredients for stability and performance are weight clipping and using uniform noise scaled to the parameter magnitudes.
Our ablation studies show just how critical these components are. Without them, performance collapses.

📈 Massive Scaling Pays Off
We see strong scaling effects when using MJX to rollout up to 4000 differently perturbed policies in parallel.
This explores the policy space effectively and large batches drastically reduce the variance of the resulting gradients.

🧠 Simple & Powerful RL
This unlocks fully off-policy learning and policy parameter space exploration using any policy data and leads to the probably most simple DRL algorithm one can imagine:

🔍 What are EPVFs?
Imagine a value function that understands the policy's parameters directly: V(θ).
This allows for direct, gradient-based policy updates:

Many thanks to my colleagues and collaborators: Daniel Palenicek, Łukasz Antczak, @jan-peters.bsky.social and most importantly Jonathan Kinzel (@ibims1jfk.bsky.social), who interned at MAB Robotics and did the experiments.
Also thanks to MAB Robotics for providing the hardware and constant support!

We build on the efficient CrossQ DRL algorithm and combine it with two control architectures — Joint Target Prediction for agile maneuvers and Central Pattern Generators for stable, natural gaits — to train locomotion policies directly on the HoneyBadger quadruped robot from MAB Robotics.