Get live statistics and analysis of Jason Ma's profile on X / Twitter

Introducing Dynamism v1 (DYNA-1) by @DynaRobotics – the first robot foundation model built for round-the-clock, high-throughput dexterous autonomy. Here is a time-lapse video of our model autonomously folding 850+ napkins in a span of 24 hours with • 99.4% success rate — zero human intervention • 60% human throughput speed • 4.3/5 quality ratings (set by the client) A thread on our motivation, insights and results:

Excited to finally share Generative Value Learning (GVL), my @GoogleDeepMind project on extracting universal value functions from long-context VLMs via in-context learning! We discovered a simple method to generate zero-shot and few-shot values for 300+ robot tasks and 50+ datasets using SOTA VLMs like Gemini (Try out the demo on our website on your robot video today!) I worked a lot on leveraging foundation models as guidance for robots in my PhD, and to me, this result forges a new frontier in how we can use foundation models for robot learning, given its broad applicability independent of embodiment and task types. Quite excited about how we can build on this work as a community!

We have raised $120M to accelerate our mission of building and delivering high-performance general-purpose robots to the physical world. Within one year, we have made research breakthroughs, showing that it is possible to achieve real-world reliability with large VLAs, and demonstrated commercial and deployment traction, with our DYNA-1 models running live in sites at SF, LA, and Sacramento. This is just the beginning, and I never felt more optimistic about a future where AI-powered robots can positively impact human productivity. From when I started PhD where robot policies barely worked even in highly controlled settings to now deploying DYNA robots with the confidence of out-of-box model performance, when I think about the trajectory of robotics, it’s astonishing how quickly we’ve gone from if it works in the lab to it works in the world. The next frontier isn’t about proving robots can move—it’s about proving they can reliably help in real-world environments, at scale, across industries. That’s what we’re building at @DynaRobotics The impact of this shift will be massive: - Unlocking productivity across logistics, manufacturing, and beyond. - Expanding what small teams and businesses can achieve. - Freeing humans to focus on higher-level creativity, problem-solving, and connection. The mission is bigger than any single deployment. It’s about ushering in an era where general-purpose robots are as ubiquitous and trusted as computers or smartphones. We’re just getting started, and I couldn’t be more excited for what’s ahead. Join us!🚀🤖

I recently gave a talk at MIT's Embodied Intelligence seminar, covering some of my recent works and perspectives on "Foundation Model Supervision for Robot Learning". Recording is up on Youtube: youtube.com/watch?v=JfZYtp… Hope you like it and find it useful!

Excited to launch @DynaRobotics with a team of incredible researchers, engineers and company builders! At Dyna, our mission is to bring affordable general-purpose AI robots to real production environments.

Sharing some exciting DYNA-1 result: zero-shot environment generalization We put DYNA-1 under test in a completely different environment from our training distribution – with an entirely different background (@DynaRobotics banner) and metal table. The table has a reflective and smooth surface, creating a wildly different visual appearance as well as interaction dynamics. The model is able to proceed as usual, adeptly folding and recovering from its own mistakes. By focusing on task mastery, we achieve robust generalization out of the box

The holy-grail of robotics is a self-improving system that curiously explores and learns from experience. In Tether, we demonstrated that robots can scaffold foundation models' knowledge into high-quality exploratory data and autonomously improve themselves from zero to hero. Some Highlights: 1. autonomously play for 24 hrs with only needing 5 interventions 2. produced 1000+ successful trajectories 3. downstream closed-loop policy learning from 0 to 90% This is my last project from grad school, and I am really excited about the results and what they mean for future agentic robotic systems! Congrats to @willjhliang @sam_wang23 @johnnywang_16 for the herculean effort in pulling this off!

The hard part of robotics *IS* robotics, and the Sunday guys just showed how far a thoughtful data-driven full-stack approach can go! Super impressive, congrats @tonyzzhao @chichengcc !

It's really unfortunate to hear the FAIR layoff news.. Meta friends: if you are interested in working on frontier real-world robotics, @DynaRobotics is hiring! Please DM me!

I'm attending #NeurIPS2025 this week! Excited to check out progress in all areas of AI. Dyna is also recruiting research and engineering roles across AI, robotics, and hardware! DMs are open!

Introducing Dynamism v1 (DYNA-1) by @DynaRobotics – the first robot foundation model built for round-the-clock, high-throughput dexterous autonomy. Here is a time-lapse video of our model autonomously folding 850+ napkins in a span of 24 hours with • 99.4% success rate — zero human intervention • 60% human throughput speed • 4.3/5 quality ratings (set by the client) A thread on our motivation, insights and results:

We have raised $120M to accelerate our mission of building and delivering high-performance general-purpose robots to the physical world. Within one year, we have made research breakthroughs, showing that it is possible to achieve real-world reliability with large VLAs, and demonstrated commercial and deployment traction, with our DYNA-1 models running live in sites at SF, LA, and Sacramento. This is just the beginning, and I never felt more optimistic about a future where AI-powered robots can positively impact human productivity. From when I started PhD where robot policies barely worked even in highly controlled settings to now deploying DYNA robots with the confidence of out-of-box model performance, when I think about the trajectory of robotics, it’s astonishing how quickly we’ve gone from if it works in the lab to it works in the world. The next frontier isn’t about proving robots can move—it’s about proving they can reliably help in real-world environments, at scale, across industries. That’s what we’re building at @DynaRobotics The impact of this shift will be massive: - Unlocking productivity across logistics, manufacturing, and beyond. - Expanding what small teams and businesses can achieve. - Freeing humans to focus on higher-level creativity, problem-solving, and connection. The mission is bigger than any single deployment. It’s about ushering in an era where general-purpose robots are as ubiquitous and trusted as computers or smartphones. We’re just getting started, and I couldn’t be more excited for what’s ahead. Join us!🚀🤖

Excited to launch @DynaRobotics with a team of incredible researchers, engineers and company builders! At Dyna, our mission is to bring affordable general-purpose AI robots to real production environments.

Sharing some exciting DYNA-1 result: zero-shot environment generalization We put DYNA-1 under test in a completely different environment from our training distribution – with an entirely different background (@DynaRobotics banner) and metal table. The table has a reflective and smooth surface, creating a wildly different visual appearance as well as interaction dynamics. The model is able to proceed as usual, adeptly folding and recovering from its own mistakes. By focusing on task mastery, we achieve robust generalization out of the box

Excited to finally share Generative Value Learning (GVL), my @GoogleDeepMind project on extracting universal value functions from long-context VLMs via in-context learning! We discovered a simple method to generate zero-shot and few-shot values for 300+ robot tasks and 50+ datasets using SOTA VLMs like Gemini (Try out the demo on our website on your robot video today!) I worked a lot on leveraging foundation models as guidance for robots in my PhD, and to me, this result forges a new frontier in how we can use foundation models for robot learning, given its broad applicability independent of embodiment and task types. Quite excited about how we can build on this work as a community!

The holy-grail of robotics is a self-improving system that curiously explores and learns from experience. In Tether, we demonstrated that robots can scaffold foundation models' knowledge into high-quality exploratory data and autonomously improve themselves from zero to hero. Some Highlights: 1. autonomously play for 24 hrs with only needing 5 interventions 2. produced 1000+ successful trajectories 3. downstream closed-loop policy learning from 0 to 90% This is my last project from grad school, and I am really excited about the results and what they mean for future agentic robotic systems! Congrats to @willjhliang @sam_wang23 @johnnywang_16 for the herculean effort in pulling this off!

I recently gave a talk at MIT's Embodied Intelligence seminar, covering some of my recent works and perspectives on "Foundation Model Supervision for Robot Learning". Recording is up on Youtube: youtube.com/watch?v=JfZYtp… Hope you like it and find it useful!

The hard part of robotics *IS* robotics, and the Sunday guys just showed how far a thoughtful data-driven full-stack approach can go! Super impressive, congrats @tonyzzhao @chichengcc !

Cool paper from Tony and @edward_s_hu! Tony is also applying to PhD this cycle!

typescript native agentic workflow is really the future for workflow automation, and @bubblelab_ai delivers that with founders who listens to customers and ship crazy fast (gemini3 already supported 😮) I use it daily to supplement my research workflows, check it out!