Jean Oh

🤖What if a robot could understand hair dynamics well enough to style your hair, just like your favorite barber💈? 🔥Excited to announce DYMO-Hair, a model-based robot hair styling system powered by a generalizable 3D hair dynamics model. 🚀A new step toward robots that can understand and manipulate more complex deformable materials. New dynamics learning paradigm. Fully synthetic data from a novel lightweight simulator. Zero-shot sim2real transfer. 👉Check it out at: chengyzhao.github.io/DYMOHair-web/ More details below: [1/N]

Due to a number of requests, the submission deadline for the IEEE RA Magazine Special Issue on the Arts and Robotics has been extended to August 15th. The formatting instructions for both technical and non-technical papers can be found on the website: lnkd.in/eBMQNaFC

We are organizing the first "Robot Fashion Show" at CoRL/Humanoids25 and invite artists, designers, and researchers to propose creative ideas for “dressing up” the robots for safety and aesthetics. Deadline: July 15, 2025 corl.org/contributions/… #robotFashionShow #designer

We are launching a new platform to build a diverse and friendly multicultural community. Please sign up to join our waitlist or try the Beta version to earn points by posting your photos or explore other cultures in the world! worldccub.netlify.app #WorldPeace #WorldCCUB

IEEE RAMagazine welcomes articles for the Special Issue on the Arts and Robotics: ieee-ras.org/publications/r…

@vernon_luk @jeanoh Check out our website, paper, or code to learn more: website: rewarped.github.io paper: arxiv.org/abs/2412.12089 code: github.com/rewarped/rewar… All of our code, simulation environments & algorithms, is open source and available now: 𝐩𝐢𝐩 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐫𝐞𝐰𝐚𝐫𝐩𝐞𝐝 7/7

This was a wonderful collaboration over last year with @vernon_luk, @jeanoh. And it would not have been possible without the amazing open source effort by the NVIDIA Warp team. 6/n

We show that SAPO can learn a range of challenging manipulation and locomotion tasks, re-implemented in Rewarped, that involve interaction between rigid bodies, articulations, and deformables. SAPO beats model-free RL (PPO, SAC) as well as prior first-order RL (APG, SHAC). 5/n

SAPO (Soft Analytic Policy Optimization): maximum entropy first-order RL algorithm that uses analytic gradients from diff. sim to train a stochastic actor to maximize expected return and entropy. Why MaxEnt RL? Smoother optimization landscapes stabilize first-order RL. 4/n

Rewarped: parallel differentiable multiphysics simulation platform that supports simulating various materials beyond just rigid bodies. Another simulator? Re-write tasks in Rewarped and scale first-order RL on tasks involving rigid and soft body interaction. 3/n

Model-free RL is too sample inefficient to scale on slow soft body simulation. Prior first-order RL algorithms work on rigid body locomotion, but fail to solve soft body manipulation. No existing simulator satisfies all: (1) parallel envs (2) differentiable (3) multiphysics. 2/n

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

My student, Mehul Agarwal's work was featured in CMU RI News. Together with Mehul's sister, Gauri at MIT Media Lab, we developed a technology to check your identity before using your photograph for genAI. CMU and MIT Join Forces to Combat Deepfake Threats ri.cmu.edu/cmu-and-mit-jo…

Happy Valentine's Day from BIG

Call for Proposals: Arts in Robotics at ICRA’25 (Deadline: Dec 15th) 2025.ieee-icra.org/announcements/…

RoPotter, our pottery-making robot paper won the Best Oral Presentation Finalist Award at Humanoids 2024 🎉 Uksang Yoo, Adam Hung, Jonathan Francis, Jean Oh, Jeffrey Ichnowski @UksangYoo @jeff_ichnowski arxiv.org/abs/2408.02184

🙏 This was a super exciting collaboration! Huge thanks to Uksang Yoo (@UksangYoo), Jean Oh (@JeanOhCmuBIG), Christopher Atkeson (@ChrisAtkeson) and Jeffrey Ichnowski (@jeff_ichnowski) for their invaluable guidance and support. soft-spin.github.io. 5/5🧵.

🦾 Using CMA-ES (Covariance Matrix Adaptation Evolution Strategy), the robot could quickly identify effective movements using only the real robot (no simulation). With as few as 10 generations, the soft robot learns how to spin various objects.🤲💡 4/5🧵.

🔄 The robot practice consistently using a soft hand attached to a manipulator arm. To make the initial state repeatable, we place dropped pens into a fixture, and the robot picks it up and tries again. ⏪🔧 3/5🧵.

🎯 Surprisingly, we do not need the robot to plan motion every step. By representing the entire pen spinning motion with just 8 variables, we reduced the action space, making it easier for the robot to optimize spin actions in the real world. ⚡️🖊️ 2/5🧵.