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Check out Yunchu's new work on automatically selecting keypoints as a representation for super robust policy learning!

In robotics benchmarks are rarely shared. New eval setups are created for each new project, a stark difference from evals in broader ML. But generalist policies share a problem statement: do any task in any environment. Can generalist capabilities make robot evaluation easier?

Check out some of our new work on distributed robot evaluation led by Karl Pertsch, Pranav Atreya and Tony Lee! Hopefully folks can contribute, and help us take a step towards systematic and standardized empiricism in robot learning! :) Also check out some of the fun sim eval

I'm sadly unable to be at #RSS2025 this year, but my students Arhan Jain, Chuning Zhu and Marcel Torné will be! Find them presenting some exciting work today, 6/21: 1) Chuning Zhu will present Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large

In our latest paper, we discovered a surprising result: training LLMs with self-play reinforcement learning on zero-sum games (like poker) significantly improves performance on math and reasoning benchmarks, zero-shot. Whaaat? How does this work? We analyze the results and find

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