Toyota Research Institute

The results are in: our first Large Behavior Models (LBMs) are here! TRI researchers have been developing a single neural network, which we call Large Behavior Models, that can empower robots to learn and perform a wide variety of tasks. Today, we’re sharing the results from our first major milestone: LBM 1.0. These findings represent a foundational step towards building general-purpose robots that amplify people at home and at work. Watch the results and full demonstrations → https://xmrrwallet.com/cmx.plnkd.in/gCXsqcWP Read more → https://xmrrwallet.com/cmx.plnkd.in/gngyUURu Want to join our team? Find open roles at tri.global/careers #robotics #AI #TRI

What's the difference between robots trained on TRI's Large Behavior Models (LBMs) and those trained on single tasks without LBMs? 🥣 See the results as these robots serve up breakfast autonomously. Learn more about our work on LBMs here: https://xmrrwallet.com/cmx.plnkd.in/gngyUURu

Happy National Intern Day! 🎉 Today, we take a moment to recognize and celebrate all of the incredible interns at TRI this year. TRI’s internship program offers a unique opportunity to work closely with our researchers on technologies to improve the quality of life for individuals and society. Here’s a glimpse into that experience from some of our 2025 interns! Think you're up for the challenge (and the snacks)? 🧋👀 Keep an eye out for internship opportunities: https://xmrrwallet.com/cmx.plnkd.in/d5Mtz5_R

📖 In our latest blog post, we explore how Storybook, a narrative-building tool created by TRI's Human-Centered AI division, is helping people explore and navigate their career paths. We tested Storybook with college students preparing for grad school, Toyota team members, and everyday users who used the tool to create illustrated, interactive stories for sharing experiences and advice. Results were strong: • 80% said they’d recommend Storybook • Users found it engaging, creative, and empowering • Professionals saw new ways to share stories they hadn’t told before Our work on Storybook reveals the power of shared experiences in navigating complex career landscapes. We plan on launching other deployments soon, so please check back for more updates! 📖 Read the full post: https://xmrrwallet.com/cmx.plnkd.in/gu2sVhn8

🍎 Watch TRI’s Large Behavior Models (LBMs) in action as these robots learned to core and cut an apple into multiple slices autonomously. In our latest update, we trained our LBMs on a mixture of:  • 468 hours of internally collected bimanual robot teleoperation data  • 45 hours of simulation-collected teleoperation data  • 32 hours of Universal Manipulation Interface (UMI) data  • 1,150 hours of internet data curated from the Open X-Embodiment dataset See the results: https://xmrrwallet.com/cmx.plnkd.in/gngyUURu

Thank you to everyone who joined us at CVPR (Computer Vision and Pattern Recognition) and RSS (Robotics: Science and Systems) conferences last month! Some highlights: 🎉 We hosted our first-ever Women & Allies breakfast event at RSS! 🎤 Russ Tedrake, SVP of TRI’s Large Behavior Models division, spoke on a dynamic panel titled “The Past, Present, and Future of Robotics Research.” Watch it here: https://xmrrwallet.com/cmx.plnkd.in/g9qBNEme 🔬 Multimodal Learning for Materials Science workshop: https://xmrrwallet.com/cmx.plnkd.in/gzRqJxUg 🤖 Robot Evaluation workshop: https://xmrrwallet.com/cmx.plnkd.in/gf9vdSzS 📄 Papers presented: Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion https://xmrrwallet.com/cmx.pmvgd.github.io/ Learning Temporally Consistent Video Depth from Video Diffusion Priors https://xmrrwallet.com/cmx.plnkd.in/g9Mjze8A ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping https://xmrrwallet.com/cmx.psh8.io/#/zerograsp Safety with Agency: Human-Centered Safety Filter with Application to AI-Assisted Motorsports https://xmrrwallet.com/cmx.plnkd.in/g9vjMm_f Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies https://xmrrwallet.com/cmx.plnkd.in/geGg-YRW Is your imitation learning policy better than mine? Policy comparison with near-optimal stopping https://xmrrwallet.com/cmx.plnkd.in/gR4WZNDM CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity https://xmrrwallet.com/cmx.plnkd.in/get-MTrs Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets https://xmrrwallet.com/cmx.plnkd.in/gMMXHx-p 🙋 Interested in working at TRI? See open roles: https://xmrrwallet.com/cmx.plnkd.in/d5Mtz5_R

📣 New call for proposals! TRI’s Human-Centered AI (HCAI) division is excited to announce a new collaborative research opportunity called the Human-AI Kaizen Initiative! We are looking for potential university partners to work with us on uncovering a deeper understanding of how to design and evaluate systems in which humans and AI can grow, trust, and adapt to each other over time. 📅 Deadline for Proposals: Sep. 18, 2025 🔗 Learn more and submit your proposals: https://xmrrwallet.com/cmx.plnkd.in/gWZJND3F Let’s shape the future of human-centered #AI systems together.

TRI’s Large Behavior Models (LBMs) enable robots to autonomously assemble a bike wheel and even recover from making a mistake. We trained a series of diffusion-based LBMs on almost 1,700 hours of robot data, conducted 1,800 real-world evaluation rollouts, and over 47,000 simulation rollouts to rigorously study their capabilities. See the results: https://xmrrwallet.com/cmx.plnkd.in/gngyUURu

Learn more about our latest work on LBM (Large Behavior Models) 1.0 from Russ Tedrake, SVP of the LBM division at TRI! 🎥 Watch our research in action: https://xmrrwallet.com/cmx.plnkd.in/gCXsqcWP

TRI's "LBM 1.0" paper appeared on arxiv last night! Large Behavior Models (LBMs) are foundation models for robots that map robot sensors (notably camera inputs) and natural language commands into robot actions. The robots are programmed just through demonstration; we can develop incredible new skills like the video below without writing a single line of new code. There is a lot of excitement in the field right now because of the incredible potential for this type of technology. Inevitably, there is also a lot of hype. 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 technology, and to share a lot of details for how we're achieving it. The short version is: LBMs work! We see consistent and statistically significant improvements as we increase the amount of pretraining data. But doing the science is still hard; as a field we have more work to do to improve the statistical power of our experiments. Please check out our project website for the paper and more details: https://xmrrwallet.com/cmx.plnkd.in/eDn_sqGh. https://xmrrwallet.com/cmx.plnkd.in/epSksw5E