It’s notoriously difficult to model the mechanics of compliant robot jaw tips during grasping! We found that a new tool from computer graphics can help. IPC-GraspSim, from AUTOLab UC Berkeley. Paper, data, video: sites.google.com/berkeley.edu/i… (1/9)

Wouldn’t it be nice if ChatGPT could find your missing keys for you? Our latest research from Berkeley AI Research + Google AI suggests that robots can use large language models (LLMs) to find hidden objects faster. 🧵👇

Can vision and language models be extended to include touch? Yes! We will present a new touch-vision-language dataset collected in the wild and Touch-Vision-Language Models (TVLMs) trained on this dataset at #ICML2024. 🙌 1/6 tactile-vlm.github.io

Vision-language models perform diverse tasks via in-context learning. Time for robots to do the same! Introducing In-Context Robot Transformer (ICRT): a robot policy that learns new tasks by prompting with robot trajectories, without any fine-tuning. icrt.dev [1/N]

1/N Most Vision-Language-Action models need tons of data for finetuning, and still fail for new objects and instructions. Introducing OTTER, a lightweight, easy-to-train model that uses text-aware visual features to nail unseen tasks out of the box! Here's how it works 👇

Can we scale up robot data collection without a robot? We propose a pipeline to scale robot dataset from one human demonstration. Through a real2render2real pipeline, policies trained with the generated data can be deployed directly on a real robot.

Can we track object part motions from a monocular video? Check out POD! With an object scan and a monocular video, we can learn an object configuration model. This could be useful for reconstructing articulated objects for robot learning.

(1/N) How close are we to enabling robots to solve the long-horizon, complex tasks that matter in everyday life? 🚨 We are thrilled to invite you to join the 1st BEHAVIOR Challenge @NeurIPS 2025, submission deadline: 11/15. 🏆 Prizes: 🥇 $1,000 🥈 $500 🥉 $300