Ying Yuan

It is great to know that my student Haozhi Qi @HaozhiQ , jointly supervised with Professor Jitendra Malik @JitendraMalikCV, is the recipient of the Lofti A. Zadeh Prize for 2024-25 given by the EECS Department of UC Berkeley for graduating PhD students. Congratulations!

🤖 Want to train a humanoid to stand up safely and smoothly? Try HumanUP: Sim-to-Real Humanoid Getting-Up Policy Learning! 🚀 ✨ HumanUP is a two-stage RL framework that enables humanoid robots to stand up from any pose(facing up and down) with stability and safety. Check out our new project: Learning Getting-up Policies for Real-world Humanoid Robots! A huge thanks to our team for making this possible! 💪🎉@RunpeiDong @C___eric417 @_saurabhg Project page: humanoid-getup.github.io Paper: arxiv.org/abs/2502.12152

Low-cost teleop systems have democratized robot data collection, but they lack any force feedback, making it challenging to teleoperate contact-rich tasks. Many robot arms provide force information — a critical yet underutilized modality in robot learning. We introduce: 1. 🦾A low-cost, force-feedback-enabled teleop system. 2. 🥊Force-Attending Curriculum Training (FACTR) uses force to improve generalization in complex, contact-rich tasks. jasonjzliu.com/factr/ 🧵(1/N)

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: genesis-world.readthedocs.io/en/latest/user…). The Genesis physics engine and simulation platform is fully open source at github.com/Genesis-Embodi…. We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: github.com/Genesis-Embodi… Project webpage: genesis-embodied-ai.github.io Documentation: genesis-world.readthedocs.io 1/n