Research Scientist @GoogleDeepMind
Moutain View
Joined July 2021
- Tweets102
- Following440
- Followers896
- Likes401
Thank you, Siqi! I really enjoyed working with you this summer too. Wishing you all the best with your PhD at UIUC!
Career update: I recently finished my PhD at @USC and joined @GoogleDeepMind as a research scientist, working on 3D coding for Gemini. Looking forward to the journey ahead!
This year, we won 2 Best Paper Awards at #CVPR2026 workshops:
Humanoid Everyday (humanoideveryday.github.io/) won a Best Paper Award at the Embodied AI Workshop
Ψ0 (psi-lab.ai/Psi0) won a Best Paper Award at the 3D-LLM/VLA Workshop
Congratulations to the team!
@zhenyuzhao123, Hongyi Jing, @SonglWei, @Boqian_Li_ , @yuewang314
Thanks @oier_mees and the team for this excellent survey! Proud to see DreamPlan, led by our students @JiaEmily84473 and Weiduo Yuan, featured among so many inspiring works in this space.
DreamPlan fine-tunes VLM planners entirely inside the "imagination" of a learned video world model, sidestepping costly real-robot RL for deformable manipulation.
Huge congrats to Emily and Weiduo on this recognition!
DreamPlan: arxiv.org/abs/2603.16860
𝐀𝐟𝐭𝐞𝐫 𝐕𝐋𝐀𝐬, 𝐰𝐨𝐫𝐥𝐝 𝐦𝐨𝐝𝐞𝐥𝐬 𝐚𝐫𝐞 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐛𝐢𝐠 𝐭𝐡𝐢𝐧𝐠 𝐢𝐧 𝐫𝐨𝐛𝐨𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 — 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐩𝐚𝐜𝐞 𝐢𝐬 𝐛𝐫𝐞𝐚𝐭𝐡𝐭𝐚𝐤𝐢𝐧𝐠 🚀 𝐒𝐨 𝐰𝐞 𝐰𝐫𝐨𝐭𝐞 𝐚 𝐬𝐮𝐫𝐯𝐞𝐲.
World models, predictive representations of how environments evolve under actions, have become one of the most important building blocks in modern robot learning. They power policy learning, planning, simulation, evaluation and data generation. And with the advent of large-scale generative video models, the field is moving faster than ever.
To help the community keep up, we wrote a comprehensive survey together with @pabbeel, @JitendraMalikCV, @jiajunwu_cs, @du_yilun, @mapo1, @philiptorr, @Jianfei_AI and many others 📖
"World Model for Robot Learning: A Comprehensive Survey"
Paper: arxiv.org/pdf/2605.00080
Project: ntumars.github.io/wm-robot-s…
@UCBerkeley @Stanford @Harvard @ETH @Microsoft @UniofOxford @NTUsg
Back in 2024, I talked to my labmates about the idea of leveraging massive ego-centric human videos for humanoid manipulation. Now we have made it! Happy to share our humanoid foundation model, Psi-0!
Introducing Ψ₀ (psi-lab.ai/Psi0) — an open foundation model for universal humanoid loco-manipulation.
🏆 Outperforms GR00T N1.6 by 40%+ overall success rate
📉 Uses only ~10% of the pre-training data
📦 Fully open-source: model, data, code, and deployment pipeline
1/10
Jiageng Mao retweeted
Introducing the USC Physical Superintelligence (PSI) Lab (psi-lab.ai). We are rebranding to better reflect our current focus. From here on out, we are tackling one thing: solving robotics and physical intelligence with every model, every bug, and every line of code. And yes, we are hiring at all levels, especially PhDs in this cycle and potential PostDocs who are excited about robotics. We hope you can join us in this journey! 1/9
Jiageng Mao retweeted
Full episode dropping soon!
Geeking out with @zhenyuzhao123, Hongyi Jing, Xiawei Liu, @PointsCoder @yuewang314 on Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation humanoideveryday.github.io/
Co-hosted by @micoolcho @chris_j_paxton