Tyler Han

Animals can’t learn by being tele-operated. But, they do learn by observing and interacting with the world around them. So, why don’t robots learn this way? Excited to release, “Planning from Observation and Interaction”, for real-world observational learning on robots! 🧵(1/12)

We’re releasing OmniReset, a framework for training robot policies using large-scale RL and diverse resets for contact-rich, dexterous manipulation. OmniReset pushes the frontier of robustness and dexterity, without any reward engineering or demonstrations. Try the policies yourself in our interactive simulator! weirdlabuw.github.io/omnireset/ (1/N 🧵)

This work would not be possible without: Siyang Shen, @RohanBaijal @harine_ravi, @bxtbold, Kevin Huang, Sanghun Jung, and Byron Boots. Find more videos on the project website. 🌐Project: uwrobotlearning.github.io/mpail2/ 📜Paper: arxiv.org/abs/2602.24121 🧑‍💻Code: github.com/UWRobotLearnin…

Scaling up MPAIL, this work is a second step in a vision towards enabling any embodiment (not only those with thousands of hours of action-labeled and prior data) to learn directly in the real world, advancing robots ever so slightly towards animal-like learning and adaptation.

Animals can’t learn by being tele-operated. But, they do learn by observing and interacting with the world around them. So, why don’t robots learn this way? Excited to release, “Planning from Observation and Interaction”, for real-world observational learning on robots! 🧵(1/12)

A reward model that works, zero-shot, across robots, tasks, and scenes? Introducing Robometer: Scaling general-purpose robotic reward models with 1M+ trajectories. Enables zero-shot: online/offline/model-based RL, data retrieval + IL, automatic failure detection, and more! 🧵 (1/12)

How can we create a single navigation policy that works for different robots in diverse environments AND can reach navigation goals with high precision? Happy to share our new paper, "VAMOS: A Hierarchical Vision-Language-Action Model for Capability-Modulated and Steerable Navigation"! 📜 Paper: arxiv.org/abs/2510.20818 🌐 Website: vamos-vla.github.io

Long Range Navigator (LRN) 🧭— an approach to extend planning horizons for off-road navigation given no prior maps. Using vision LRN makes longer-range decisions by spotting navigation frontiers far beyond the range of metric maps. personalrobotics.github.io/lrn/

Celebrating the #robotics community during #NationalRoboticsWeek. 👏 @UW's Wheeled Lab project is bringing sim-2-real #robotics to low-cost, open-source platforms using NVIDIA Isaac Lab. Learn more about their work ➡️nvda.ws/4lpr6hO