Happy 2025! May the robots finally get smarter and start helping humans. Robot trained to skateboard by Hang Liu and Sangli Teng

RL is notoriously sample inefficient. How can we scale RL on tasks much slower to simulate than rigid body physics, such as soft bodies? In our #ICLR2025 spotlight, we introduce both a new first-order RL algorithm, SAPO, and differentiable simulation platform, Rewarped. 1/n

THERE'S NO PEOPLE NEAR THAT ROBOT😭

Atlas is demonstrating reinforcement learning policies developed using a motion capture suit. This demonstration was developed in partnership with Boston Dynamics and RAI Institute.

1/ While most RL methods use shallow MLPs (~2–5 layers), we show that scaling up to 1000-layers for contrastive RL (CRL) can significantly boost performance, ranging from doubling performance to 50x on a diverse suite of robotic tasks. Webpage+Paper+Code: wang-kevin3290.github.io/scaling-crl/

Introducing OceanSim: A High-Fidelity, GPU-Accelerated Underwater Robotics Simulator 🌊 Explore our project website: umfieldrobotics.github.io/OceanSim/ OceanSim is built for realistic & efficient underwater robot simulation. #Robotics #UnderwaterRobotics #OpenSource

Command humanoids *directly* with natural language? Introducing LangWBC, a generative, end-to-end policy that turns natural language into real-world whole-body humanoid control! 💬→🦿Smooth, robust, surprisingly intuitive! See more 👉 LangWBC.github.io #RSS2025

Meet 𝐀𝐌𝐎 — our universal whole‑body controller that unleashes the 𝐟𝐮𝐥𝐥  kinematic workspace of humanoid robots to the physical world. AMO is a single policy trained with RL + Hybrid Mocap & Trajectory‑Opt. Accepted to #RSS2025. Try our open models & more 👉

🤖Introducing TWIST: Teleoperated Whole-Body Imitation System. We develop a humanoid teleoperation system to enable coordinated, versatile, whole-body movements, using a single neural network. This is our first step toward general-purpose robots. 🌐humanoid-teleop.github.io