⚡ In the last few weeks, I had the great chance to present our recent work on embodiment-aware reinforcement learning for robot control and design 🤖

Learning five-ball juggling on the second attempt, with two Barrett WAMs. Most humans take years of practice. Paper and videos: kai-ploeger.com/residual-juggling #robotics #juggling

I had a great time visiting Prof. Kevin S. Luck at @vuamsterdam.bsky.social and to present our recent works on embodiment-aware learning for robot control and design! So much cool work here in Amsterdam about Robot Co-Design! There is huge potential in the data-driven way of designing robots!

⚡️What if we could design robots with gradients? 🤖 Introducing Shape Your Body: we train one multi-embodiment policy + value function, then optimize new robot designs through value gradients. 🔗 Try out our interactive demo here: nico-bohlinger.github.io/shape-your-b...

I'm presenting four different works at IROS 2025 this week in Hangzhou 🤖

⚡️ Can one unified policy control 10 million different robots and zero-shot transfer to completely unseen robots, even humanoids? 🔗 Yes! Checkout our paper: arxiv.org/abs/2509.02815

🇰🇷 Conferences are about finally meeting your collaborators from all around the world! Check out our work on Embodiment Scaling Laws @CoRL2025 We investigate cross-embodiment learning as the next axis of scaling for truly generalist policies 📈 🔗 All details: embodiment-scaling-laws.github.io

Need for Speed or: How I Learned to Stop Worrying About Sample Efficiency Part II of my blog series "Getting SAC to Work on a Massive Parallel Simulator" is out! I've included everything I tried that didn't work (and why Jax PPO was different from PyTorch PPO) araffin.github.io/post/tune-sa...

Getting SAC to Work on a Massive Parallel Simulator: Tuning for Speed (Part II) | Antonin Raffin | Homepage

This second post details how I tuned the Soft-Actor Critic (SAC) algorithm to learn as fast as PPO in the context of a massively parallel simulator (thousands of robots simulated in parallel).

araffin.github.io

Robot Randomization is fun!

🚀 Checkout our new work at @rldmdublin2025.bsky.social today at poster#16! We're showing how to make Explicit Policy-conditioned Value Functions V(θ) (originating from Faccio & Schmidhuber) work for more complex control tasks. The secret? Massive scaling!

IAS is at RLDM 2025! We have many exiting works to share (see 👇), so come to our posters and talk to us!

⚡️ Do you think training robot locomotion needs large scale simulation? Think again! We train an omnidirectional locomotion policy directly on a real quadruped in just a few minutes 🚀 Top speeds of 0.85 m/s, two different control approaches, indoor and outdoor experiments, and more! 🤖🏃‍♂️

"As researchers, we tend to publish only positive results, but I think a lot of valuable insights are lost in our unpublished failures." New blog post: Getting SAC to Work on a Massive Parallel Simulator (part I) araffin.github.io/post/sac-mas...

Getting SAC to Work on a Massive Parallel Simulator: An RL Journey With Off-Policy Algorithms (Part I) | Antonin Raffin | Homepage

This post details how I managed to get the Soft-Actor Critic (SAC) and other off-policy reinforcement learning algorithms to work on massively parallel simulators (think Isaac Sim with thousands of ro...

araffin.github.io