Super excited about these results. We need more rigorous experimental studies like this in robotics!

How to learn dexterous manipulation for any robot hand from a single human demonstration? Check out DexMachina, our new RL algorithm that learns long-horizon, bimanual dexterous policies for a variety of dexterous hands, articulated objects, and complex motions.

If you’re working on robotics and AI, the recent Stanford talk from Russ Tedrake on scaling multitask robot manipulation is a mandatory watch, full stop. No marketing, no hype. Just solid hypothesis driven science, evidence backed claims. A gold mine in today’s landscape!

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

🚨Past work shows: dropping just 0.1% of the data can change the conclusions of important studies. We show: Many approximations can fail to catch this. 📢Check out our new TMLR paper (w/ David Burt, Yunyi Shen/申云逸 🐺 , Tin Nguyen, and Tamara Broderick ) 👇 openreview.net/forum?id=m6EQ6…

🔥 Join our Challenge on Multimodal Robot Learning in InternUtopia and Real World! 🎮 Tasks: Manipulation & Navigation 🗺️ Each track includes an online qualifier and on-site finals 🧰 Starter kits open now 🥇 Winner prize: $10K 🔗 internrobotics.shlab.org.cn/challenge/2025/ #IROS2025 #Robotics

Excited to give a talk at the Workshop on Multimodal Robot Learning in Physical Worlds at #IROS2025 this October in Hangzhou, China! 🤖

Happy to announce that we’ll be presenting our work on sim-and-real cotraining at IROS 2025! Check out our latest arXiv version - we’ve added new experiments for 2 alternative co-training formulations and addressed some FAQs 📄 arxiv.org/abs/2503.22634 🌐 sim-and-real-cotraining.github.io