MTS @ Amazon Frontier AI & Robotics (FAR) PhD from @UCSanDiego Robot Learning / Embodied AI
San Diego, CA
Joined July 2017
- Tweets201
- Following1K
- Followers2.5K
- Likes778
Ruihan Yang retweeted
How do we make dexterous hands handle both power and precision tasks with ease? 🫳👌🫰
We introduce Power to Precision (💪➡️🎯), our new paper that optimizes both control and fingertip geometry to unlock robust manipulation from power grasp to fine-grained manipulations.
With simplified finger motions and augmented fingertips, the hand can perform diverse motions from pinching a nut🔩 to handling a pan🍳. Check the demos below🎥.
Ruihan Yang retweeted
Unified multimodal models can generate text and images, but can they truly reason across modalities? 🎨
Introducing ROVER, the first benchmark that evaluates reciprocal cross-modal reasoning in unified models, the next frontier of omnimodal intelligence.
🌐 Project: roverbench.github.io
📄 Paper: arxiv.org/abs/2511.01163
📂 Benchmark: huggingface.co/datasets/cher…
Ruihan Yang retweeted
We're hiring interns (and full-times) all year long! Please email me if interested.
Ruihan Yang retweeted
For years, I’ve been tuning parameters for robot designs and controllers on specific tasks. Now we can automate this on dataset-scale.
Introducing Co-Design of Soft Gripper with Neural Physics - a soft gripper trained in simulation to deform while handling load.
Ruihan Yang retweeted
Our robotics team will be at ICRA next week in Atlanta! Having started a new research team at Amazon building robot foundation models, we're hiring across all levels, full-time or intern, and across both SW and Research roles. Ping me at [email protected] and let's have a chat!
Ruihan Yang retweeted
AMO, a new humanoid controller, makes the humanoid not just move around with fancy motions, but actually do something useful. We will present this #RSS2025
Meet 𝐀𝐌𝐎 — our universal whole‑body controller that unleashes the 𝐟𝐮𝐥𝐥 kinematic workspace of humanoid robots to the physical world.
AMO is a single policy trained with RL + Hybrid Mocap & Trajectory‑Opt.
Accepted to #RSS2025.
Try our open models & more 👉 amo-humanoid.github.io
Ruihan Yang retweeted
"Gr00t" vs "Pi0" vs "Pi0 Fast".
I compared top open-source robotic models, and here's a detailed overview based on our own experience:
Ruihan Yang retweeted
Robot Learning has focused a lot of the policy. But how about the hardware? Can hardware be learned as well? We will discuss this in the new workshop at RSS.
Excited to announce the 1st Workshop on Robot Hardware-Aware Intelligence @ #RSS2025 in LA! We’re bringing together interdisciplinary researchers exploring how to unify hardware design and intelligent algorithms in robotics! Full info: rss-hardware-intelligence.gi…
@RoboticsSciSys
Ruihan Yang retweeted
It's All in the Hips
Can we learn anything from the evolution of hip kinematics in humanoid bots?
Is there a growing consensus of the optimal design?
Yes. And there is data to prove it.
First, a primer on Hip Kinematics
🧵
Ruihan Yang retweeted
Teleoperation is so tedious. Can we find a better way to scale real-world data? Answer: Human videos.
Inspiration: When we were doing teleoperation, we observed the motion performed by the human is almost the same as the robot, it is really just a 3D transformation away. Besides robot data, can we record humans themselves doing tasks using VisionPro as training data?
Approach: Transform the robot data into human data space: align everything into 3D hand pose representations. Train a Human Policy with both human and robot data. The Human Policy can control the robot by applying IK and retargeting on its outputs.
What enables it and not back then: Apple VisionPro provides very accurate hand pose estimation. We collect video data on humans doing tasks by wearing the VisionPro. Just with a few weeks of time, we managed to obtain 27k demonstrations with accurate hand poses.
Results: Training with both human and robot data allows generalization across objects, scenes, and robots outside the robot training data.
Ruihan Yang retweeted
Reinforcement learning is used to speed the production of behavior for the @BostonDynamics Atlas humanoid robot. At the heart of the learning process is a physics-based simulator that generates training data for a variety of maneuvers.
Ruihan Yang retweeted
During our research on generalist foundation models, we believe more AI agents will master tasks across virtual and physical worlds. @RchalYang and I created github.com/cheryyunl/awesome… to track generalist agents covering embodied & web agents. Contributions welcome!🤗
Ruihan Yang retweeted
Without any maps and prior knowledge of the scene, our humanoid and quadruped can now navigate with human language instructions to anywhere outdoors and in any house we go!🔥🔥🔥
Introducing NaVILA, a 2-level navigation foundation model (mid-level action VLA + locomotion skills) leveraging massive offline datasets, e.g., not only sim envs, but also human touring videos. A thread 1/N
Ruihan Yang retweeted
A proud advisor moment! Congratulations to @RchalYang for winning the IROS Best Paper Award on Mobile Manipulation!
rchalyang.github.io/Harmonic…
Ruihan Yang retweeted
@ZCCZHANG, Jordi Salvador, and @rosemhendrix accepting the Outstanding Paper award at CoRL! PoliFormer was so much fun to work on with all of you, I'm so happy to see it getting recognized! Wish we all could have been there together (Alvaro, @ehsanik, @anikembhavi , @inkynumbers)
Ruihan Yang retweeted
Atlas is autonomously moving engine covers between supplier containers and a mobile sequencing dolly, using ML to detect and localize the environment fixtures and individual bin. There are no prescribed or teleoperated movements. youtu.be/F_7IPm7f1vI
Ruihan Yang retweeted
Robot Utility Models (RUMs) enable basic tasks – door opening, drawer opening, object reorientation, etc. – at ~90% accuracy without ANY finetuning (i.e. zero-shot) in unseen new environments.
Fully open source!!! models, data, code & hw.
We think this is super exciting, why?👇
1. Unlocks many practical home utility tasks that often involve these basic tasks as part of an action chain. “Go get me a fork” involves opening the kitchen door and then opening the cutlery drawer.
2. This works well **zero-shot in unseen and new** environments, which is practically a huge deal. Turn the robot on, and get going.
3. The recipe for building a new model is fairly generic, and we think with a bit more refinement this can be a general recipe to build many more Utility models.
More details and access 👇
Ruihan Yang retweeted
OpenAI: pip install openai and set OPENAI_API_KEY
Anthropic: yea same but s/openai/anthropic/g
Google: oh boy. ok so you have a GCP account? no? ok go set that up. and a payment method. now make a "project". SURVEY POPUP! k now gcloud auth. wait you have the gcloud CLI right–
Ruihan Yang retweeted
Seeking robotics wizards to join our quest! 🧙♂️🤖
Join our cutting-edge team and shape the future of dexterous robots. We're seeking brilliant minds to push the boundaries of what's possible in robot manipulation.
Link: linkedin.com/jobs/view/39745…
#Robotics #AI #RobotLearning