Teaching robots to learn only from RGB human videos is hard! In Feel The Force (FTF), we teach robots to mimic the tactile feedback humans experience when handling objects. This allows for delicate, touch-sensitive tasks—like picking up a raw egg without breaking it. 🧵👇

🎉 Excited to share our latest work: Feel the Force (FTF)! Can robots learn to feel—not just see? FTF enables precise, force-sensitive manipulation by learning from human tactile behavior—no robot data or teleoperation needed. 👉 feel-the-force-ftf.github.io

We just open-sourced EgoZero! It includes the full preprocessing to turn long-form recordings into individual demonstrations as 3D states + actions. We engineered this for scalability to big datasets (streaming, parallel workers, CPU/GPU utilization)

FeelTheForce is now open-source! 🤖🖐️ We’ve released the full codebase from our paper: 📡 Streaming infrastructure with docs 🧹 Preprocessing for multi-modal data 🎓 Training pipelines & commands 🧠 Inference code for force-sensitive policies 🛠️Code: github.com/feel-the-force…

We have developed a new tactile sensor, called e-Flesh, with a simple working principle: measure deformations in 3D printable microstructures. Now all you need to make tactile sensors is a 3D printer, magnets, and magnetometers! 🧵

🎙️Ep 75: In this episode, I talk with Lerrel Pinto, Assistant Professor at NYU and one of the most cited researchers in robotics today: His work spans everything from self-supervised learning to robot dexterity, and he's on a mission to make robots generalize the way humans do.

I gave a Early Career talk at CoRL 2025 in Seoul last week, where I talked about my observations from the past decade in robot learning along with where the field is headed for the next decade. In summary, the future of robot learning needs: (1) Data beyond teleop: We are never