🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

our new system trains humanoid robots using data from cell phone videos, enabling skills such as climbing stairs and sitting on chairs in a single policy (w/ Hongsuk Benjamin Choi Junyi Zhang David McAllister)

Multimodal model support is here in 0.7! Ollama now supports multimodal models via its new engine. Cool vision models to try👇 - Llama 4 Scout & Maverick - Gemma 3 - Qwen 2.5 VL - Mistral Small 3.1 and more 😍 Blog post 🧵👇

As we all know, collecting data for robotics is very costly. This is why I’m very impressed by this work: it generates a huge amount of data for different robots without any teleoperation.

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.

Ppl are collecting large-scale teleoperation datasets, which are often just kinematics-level trajectories. Real2Render2Real is a new framework that can generate these data w.o. teleoperation or tricky sim+rl. High data quality for BC + nice scaling effect, plz dive in for more!

Large language models can do new tasks from a few text prompts. What if robots could do the same—with trajectories? 🤖 ICRT enables zero-shot imitation: prompt with a few teleop demos, and it acts—no fine-tuning. Happy to chat more at ICRA! 📍 ICRA | Wed 21 May | 08:35 - 08:40

Interested in collecting robot training data without robots in the loop? 🦾 Check out this cool new approach that uses a single mobile device scan and a human demo video to generate diverse data for training diffusion and VLA manipulation policies. 🚀 Great work by Max Fu

🚀 Struggling with the lack of high-quality data for AI-driven human-object interaction research? We've got you covered! Introducing HUMOTO, a groundbreaking 4D dataset for human-object interaction, developed with a combination of wearable motion capture, SOTA 6D pose

Learning 🤝 Model-Based Method See you tomorrow at ICRA! GWCC Building A, Room 412 1:30 PM - 6:00 PM

We hope everyone had a great time at the ICRA 2025 Workshop on Learning Meets Model-Based Methods for Contact-Rich Manipulation (contact-rich.github.io)! Big thanks to our incredible speakers, panelists, and generous sponsors — and most of all, to our amazing co-organizers

🤯 We cracked RLVR with... Random Rewards?! Training Qwen2.5-Math-7B with our Spurious Rewards improved MATH-500 by: - Random rewards: +21% - Incorrect rewards: +25% - (FYI) Ground-truth rewards: + 28.8% How could this even work⁉️ Here's why: 🧵 Blogpost: tinyurl.com/spurious-rewar…

A legged mobile manipulator trained to play badminton with humans coordinates whole-body maneuvers and onboard perception. Paper: science.org/doi/10.1126/sc……Video: youtu.be/zYuxOVQXVt8 Yuntao Ma, Andrei Cramariuc, Farbod Farshidian, Marco Hutter

Waymo in a new blog post: "We conducted a comprehensive study using Waymo’s internal dataset. Spanning 500,000 hours of driving, it is significantly larger than any dataset used in previous scaling studies in the AV domain. Our study uncovered the following: • Similar to LLMs,