OpenDriveLab

📣 #Recognition #ResearchAward As we usher in the Year of Horse, our team recognizes outstanding members from the past year in the exceptional contribution of areas. Congratulations!!! Let’s rock ‘n’ roll in 2026 🕶️🍾🎆🥂#opendrivelab

Proud to announce the strategic partnerships with 3 Leading Embodied AI companies. Together with Unitree, Noitom Robotics, and BrainCo, HKU’s Embodied Intelligence Joint Lab is live at our Zhangjiang base. We’re in this for the long game: turning embodied intelligence into a durable, shared foundation for what comes next. Builders: let’s collaborate. Press Release: hku.hk/press/news_det… @HKUniversity @hkudatascience @HKU_CDS @YiMaTweets @francislee2020 @UnitreeRobotics @noitomrobotics @BrainCo_Tech #EmbodiedAI #Robotics

Just saw Sonic — it’s incredibly solid. By far the best whole-body control model I’ve seen so far. It opens up an invaluable path to acquiring full-body coordination data for humanoid robots. Huge congrats to the team and big thanks for open-sourcing it! 🙌🔓

At the RI seminar at CMU yesterday, I presented a 3 level analysis of robot skills & discussed the pros and cons of teleoperation, simulation, and learning from videos, before presenting our research. Enjoy! youtube.com/watch?v=ry8iti…

📣 #Recognition #ResearchAward As we usher in the Year of Horse, our team recognizes outstanding members from the past year in the exceptional contribution of areas. Congratulations!!! Let’s rock ‘n’ roll in 2026 🕶️🍾🎆🥂#opendrivelab

🧐Applying world models to improve real-world policy on challenging manipulation tasks used to be considered out of reach. 😌After sustained effort, we’re now seeing encouraging progress. 🚀Thrilled to introduce RISE: Self-Improving Robot Policy with Compositional World Model opendrivelab.com/kai0-rl/ arxiv.org/abs/2602.11075 RISE is, to our knowledge, the first work to use a world model as an effective learning environment for challenging real-world manipulation, enabling policy improvement on tasks that demand high dynamics, dexterity, and precision. Incredible teamwork with @lin_kunyang111 @francislee2020 @YueXiangyu @HaoZhao_AIRSUN @smch_1127

🧥 Live-stream robotic teamwork that folds clothes. 6 clothes in 3 minutes straight. χ₀ = 20hrs data + 8 A100s + 3 key insights: - Mode Consistency: align your distributions - Model Arithmetic: merge, don't retrain - Stage Advantage: pivot wisely 🔗 mmlab.hk/research/kai0

Humanoid robots have been prisoners of the lab. We set them free — with human data. We present EgoHumanoid: The first endorsement of human-to-humanoid transfer for whole-body loco-manipulation. 🔗 Home: opendrivelab.com/EgoHumanoid 📑 Arxiv: arxiv.org/abs/2602.10106 🧵👇

⁉️ Humans perform loco-manipulation everywhere, every day.
What if robots could learn from that? 📣 Our latest work EgoHumanoid introduces the first human–humanoid co-training framework, transferring in-the-wild #Egocentric data to real humanoids. 🎯 The future of robotics is human-driven data at scale.
Let’s build the #DataInfra together.

【5/5】Big shout to our amazing collaborators Siqi Liang, @ilnehc , Yuxian Li, Yukuan Xu, Yichao Zhong, Fu Zhang, @francislee2020 🎆🎆

【4/5】The ultimate test? Tai Ping Shan. 🏔️ Steep slopes, long horizons, pitch-black night - all in one. SparseVideoNav handles it like a champ. 🔥

【1/5】❓Why do Vision-Language Navigation (VLN) agents need "step-by-step" language instructions? No.❌ We only want to give a intent. 📍 Meet SparseVideoNav: a new paradigm that uses video generation to navigate like a human. Tell the destination. Imagine the path. Reach the goal. 🌄🤖 Paper: arxiv.org/abs/2602.05827 Page：opendrivelab.com/SparseVideoNav/ Github：github.com/OpenDriveLab/S…

Glad to share me and my folks work of the last research cycle, good luck and have fun. Upvote on HF is appreciated! χ0: huggingface.co/papers/2602.09… χ0, a resource-efficient robotic manipulation framework addresses distributional shifts through model arithmetic, stage-aware advantage estimation, and train-deploy alignment to achieve long-horizon task reliability. RISE: huggingface.co/papers/2602.11… RISE, the first work to use a world model as an effective learning environment for challenging real-world manipulation, enabling policy improvement on tasks that demand high dynamics, dexterity, and precision. EgoHumanoid: huggingface.co/papers/2602.10… EgoHumanoid, the first endorsement of human-to-humanoid transfer for whole-body loco-manipulation. SparseVideoNav: huggingface.co/papers/2602.05… Vision-language navigation systems traditionally require detailed instructions but can be improved by incorporating video generation models with sparse future planning for faster, more efficient real-world deployment.

Key Technical Highlights: - 21 fully actuated degree-of-freedom - 350 gram hand weight without remote motors - Never overheating with remote actuation - 15 tactile sensors with 1mm spatial and 0.1N force resolution - 2 palm cameras with 140° FoV and controllable LED - 21 finger joint angle sensors - 21 tension sensing & self-tighting devices - Tendon connectors for rapid finger / motor replacement - SPI/I2C interfaces for in-hand sensors - TTL/CAN interfaces for standard motors - 3D-printed structure for easy mechanical modifications - Arduino programming for easy software modifications - Fully open-source including CAD, assembly guide, electronics, and software - 1400 USD material Cost

We're inviting a small group of hands-on researchers and makers to co-iterate MM-Hand 1.0, an open-source low-cost dexterous hand. Selected applicants will receive beta-version hardware in early 2026 at cost price (~USD $1,400 but tax not included) and direct engineering help.

🦾 【MM-Hand 1.0】Open-Source, Lightweight, High-DoF, Multimodal, Modular Design for Easy Disassembly and Modification 🔗 Details: mmlab.hk/research/MM-Ha… 🎁 Beta Program: forms.gle/QhaHGCigY6buuS… 📮 Inquiries: research@mmlab.hk #MMHand #DexterousHand #Robotics #OpenSource #EmbodiedAI #TactileSensing

We’re releasing our paper and code, check: arXiv: arxiv.org/abs/2602.09021 GitHub: github.com/OpenDriveLab/k… Community: discord.gg/weUGdEPpsF Challenge: opendrivelab.com/challenge2026