Ruihan Yang
199 posts
Ruihan Yang
@RchalYang
Applied Scientist @ Amazon Frontier AI & Robotics (FAR) PhD from @UCSanDiego Robot Learning / Embodied AI
In our new blog (written w/ @RchalYang), we discuss where, when, and how to make vibe agents come alive in the real world. cheryyunl.github.io/blog/vibe-agen… We look at two interfaces for bringing AI agents into the physical world: code (composable, transparent, but whose reliability hinges on API design and feedback) and action (fluid, contact-rich, but compounds errors and forgets). The hybrid of both is emerging, but the harness that closes the loop does not yet exist. Every roboticist in 2026 will need to think about which problems or tasks are fundamentally solvable by vibe agents, where that boundary lies, and what scaffolding and harness robots need.
🤔🤖Robots can throw, but can they play ring tossing like humans? 🦾🎯Introducing 𝐃𝐀-𝐌𝐌𝐏 — a dynamics-aware motion manifold primitives framework for learning coordinated and accurate throwing.
Incredibly lucky name
Unitree Spring Festival Gala Robots —a Full Release of Additional Details 🥳 Dozens of G1 robots achieved the world’s first fully autonomous humanoid robot cluster Kung Fu performance (with quick movement), pushing motion limits and setting multiple world firsts! H2 made striking appearances at both the Beijing main venue and the Yiwu sub-venue, clad in the Monkey King’s heavy armor and riding a “somersault cloud” played by B2W quadruped robot dogs, delivering New Year blessings from the clouds.
每個人都應該安全咁分享道路。了解下Waymo點樣努力令我哋嘅道路更安全。
Humanoid motion tracking performance is greatly determined by retargeting quality! Introducing 𝗢𝗺𝗻𝗶𝗥𝗲𝘁𝗮𝗿𝗴𝗲𝘁🎯, generating high-quality interaction-preserving data from human motions for learning complex humanoid skills with 𝗺𝗶𝗻𝗶𝗺𝗮𝗹 RL: - 5 rewards, - 4 DR terms, - Proprio. ONLY, - NO history/curriculum. Ready for agile, human-like 🤖? (Best with 🎧) 🔗 omniretarget.github.io 🎥 1/9
How can we enable finetuning of humanoid manipulation policies, directly in the real world? In our new paper, Residual Off-Policy RL for Finetuning BC Policies, we demonstrate real-world RL on a bimanual humanoid with 5-fingered hands (29 DoF) and improve pre-trained policies with ~15-75 minutes of robot interaction. By learning residual corrections on frozen BC policies using sample-efficient off-policy RL, we achieve significant improvements in sample efficiency, enabling policy finetuning directly on the hardware — to our knowledge, one of the first examples of this on a humanoid with bimanual dexterous hands. (If you know of other examples, let me know!)
People who are really serious about robot learning should make their own robot hardware.
