Jiaming Tang

You’ve seen Astra draw. This time, it’s an open-source 27B reasoning model we trained! RL entirely in simulation. No demonstrations. No agent harness. Native robot use. I still can‘t believe what we achieved, from an open-source foundation model with almost no native robot use capability to perform well on the real robot. This is really an exciting moment for me as it means a fundamentally different path from existing robot foundation models, directly addressing their biggest limitations. Robot foundation models have largely relied on learning manipulation from robot data, which is painfully scarce. Robot-use agent inherits the LLM’s native world knowledge and reasoning capability, and we just unleash and put them to work on robot. With robot demonstrations so scarce, being able to draw on that existing intelligence matters enormously. The exceptional instruction following and generalization have been striking to watch. Capabilities we’ve come to expect from LLMs suddenly feel remarkable when they show up in a physical robot. In our experiments, more compute and more simulation environments keep translating into stronger capabilities. We also see transfer across tasks, across robots, and from simulation to the real world. That makes the direction feel tangible: scalable simulation compute can translate into transferable robot capabilities. There’s something deeply encouraging about seeing a path where investing more resources keeps moving the needle. We’re sharing our observations and results, and open-sourcing the model, training infrastructure, and deployment stack so everyone can build on them. We’ve also laid out the limitations in detail. Each one points to an open research question, and together they capture much of what we believe are promising directions in the future. Excited to see what others discover—and build. Enjoy! mit-han-lab.github.io/pear-b…