Very impressive! We need more force involved in learning-based robot policies.

Vision-language-action models (VLAs) need to REASON, but more importantly, they need to know WHEN to reason (or not)! Thrilled to introduce OneTwoVLA, a single, unified model that combines acting (System One) ⚡ and reasoning (System Two) 🤔, and can adaptively switch between

I’m not just dancing all day, ok

If you are impressed by Tesla Optimus, also check out Roger Qiu 's talk on leveraging human videos for humanoid bimanual manipulation. Paper: Humanoid Policy ~ Human Policy Link: human-as-robot.github.io

The future of robotics isn't in the lab – it's in your hands. Can we teach robots to act in the real world without a single robot demonstration? Introducing EgoZero. Train real-world robot policies from human-first egocentric data. No robots. No teleop. Just Aria glasses and

For years, I’ve been tuning parameters for robot designs and controllers on specific tasks. Now we can automate this on dataset-scale. Introducing Co-Design of Soft Gripper with Neural Physics - a soft gripper trained in simulation to deform while handling load.

Super cool!

🤖Can a humanoid robot carry a full cup of beer without spilling while walking 🍺? Hold My Beer ! Introducing Hold My Beer🍺: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control Project: lecar-lab.github.io/SoFTA/ See more details below👇

How to learn dexterous manipulation for any robot hand from a single human demonstration? Check out DexMachina, our new RL algorithm that learns long-horizon, bimanual dexterous policies for a variety of dexterous hands, articulated objects, and complex motions.

🤖 Ever dreamed of controlling a humanoid robot to perform complex, long-horizon tasks — using just a single Vision Pro? 🎉 Meet CLONE: a holistic, closed-loop, whole-body teleoperation system for long-horizon humanoid control! 🏃‍♂️🧍 CLONE enables rich and coordinated

🚀Introducing GMT — a general motion tracking framework that enables high-fidelity motion tracking on humanoid robots by training a single policy from large, unstructured human motion datasets. 🤖A step toward general humanoid controllers. Project Website:

This work is not about a new technique. GMT (General Motion Tracking) shows good engineering practices that you can actually train a single unified whole-body control policy for all agile motion, and it works in the real world, directly with sim2real without adaptation. This is

Today we're excited to share a glimpse of what we're building at Generalist. As a first step towards our mission of making general-purpose robots a reality, we're pushing the frontiers of what end-to-end AI models can achieve in the real world. Here's a preview of our early

Your bimanual manipulators might need a Robot Neck 🤖🦒 Introducing Vision in Action: Learning Active Perception from Human Demonstrations ViA learns task-specific, active perceptual strategies—such as searching, tracking, and focusing—directly from human demos, enabling robust

🚀Heading to #RSS2025? Swing by EEB 248 on Wednesday, June 25 at 3:30 PM for a live demo of our data-driven, co-design soft gripper 🥢 at the workshop Robot Hardware-Aware Intelligence!

How to generate billion-scale manipulation demonstrations easily? Let us leverage generative models! 🤖✨ We introduce Dex1B, a framework that generates 1 BILLION diverse dexterous hand demonstrations for both grasping 🖐️and articulation 💻 tasks using a simple C-VAE model.

Great work! Scaling up data for dexterous manipulation in sim is really promising. Can’t wait to see more progress.

🚀 Introducing LeVERB, the first 𝗹𝗮𝘁𝗲𝗻𝘁 𝘄𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗵𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗩𝗟𝗔 (upper- & lower-body), trained on sim data and zero-shot deployed. Addressing interactive tasks: navigation, sitting, locomotion with verbal instruction. 🧵 ember-lab-berkeley.github.io/LeVERB-Website/

🚀 Meet ACE-F — a next-gen teleop system merging human and robot precision. Foldable, portable, cross-platform — it enables 6-DoF haptic control for force-aware manipulation. 🦾 See our demo & talk at the Robot Hardware-Aware Intelligence workshop this Wed Robotics: Science and Systems!

What would a World Model look like if we start from a real embodied agent acting in the real world? It has to have: 1) A real, physically grounded and complex action space—not just abstract control signals. 2) Diverse, real-life scenarios and activities. Or in short: It has to