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More insights: π-0.5 is trained to break tasks down into subtasks, before producing actual robot actions. It turns out that adding the subtask prediction data is useful, even if you query the model with the overall task directly.

Scaling data diversity, transfer between data sources, and a good training recipe were the main ingredients to allow robots to generalize to new homes!

We auto-encode point tracks to automatically evaluate motion realism in generative video models. By inherently focusing on motion, our new metric (TRAJAN) correlates much better with human judgments of these models than appearance based metrics.

Check out our new work where we dissect various aspects of chain-of-thought at both training and inference time) for robotics! Awesome work led by Will Chen

Had a blast on the Unsupervised Learning Podcast with Karol Hausman! We covered the past, present, and future of robot learning 🤖 Big thanks to Jacob Effron for being a fantastic host!

TRI's latest Large Behavior Model (LBM) paper landed on arxiv last night! Check out our project website: toyotaresearchinstitute.github.io/lbm1/ One of our main goals for this paper was to put out a very careful and thorough study on the topic to help people understand the state of the