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How can we move beyond static-arm lab setups and learn robot policies in our messy homes? We introduce HoMeR, an imitation learning agent for in-the-wild mobile manipulation. 🧵1/8

Excited to share our new work ReLIC, a framework for versatile loco-manipulation through flexible interlimb coordination. We combine reinforcement learning and model-based control to let robots dynamically assign limbs 🦾🦿 for manipulation or locomotion based on task demands.

Manipulation is not just about the hand; it is a whole-body activity 🐕

always enjoy reading Seohong's paper, they are incredibly clear and thought-provoking -- Seohong needs to write more :)

just realized twitter cropped the images :( here's the full list:

We've been heads-down building.  The robots have gotten pretty good.  We'll be sharing a brief update soon.

Today we're excited to share a glimpse of what we're building at Generalist. As a first step towards our mission of making general-purpose robots a reality, we're pushing the frontiers of what end-to-end AI models can achieve in the real world. Here's a preview of our early

haha 😂😂 Andy Zeng

There’s something satisfying to see the robot slotting in the box flaps so nicely in the end ... 😌

Let’s say we do have a powerful robot model that learns rich behaviors from large-scale robot data. What now? To make them actually useful, we need to be able to steer and control their behavior so that they match user intent, preferences, or deployment needs. How can we do

Teleoperating a robot feels unnatural — not just because of limited arm or hand DoFs, but also because of the lack of perceptual freedom! Humans naturally move their head and torso to search, track, and focus — far beyond a simple 2-DoF camera. How to get there? Check out

I'll present RoboPanoptes at #RSS2025 tomorrow 6/22 🐍 Spotlight talk: 9:00-10:30am (Bovard Auditorium) Poster: 12:30-2:00pm, poster #31 (Associates Park)

We build Cosmos-Predict2 as a world foundation model for Physical AI builders — fully open and adaptable. Post-train it for specialized tasks or different output types. Available in multiple sizes, resolutions, and frame rates. 📷 Watch the repo walkthrough

Robot learning has largely focused on standard platforms—but can it embrace robots of all shapes and sizes? In Xiaomeng Xu's latest blog post, we show how data-driven methods bring unconventional robots to life, enabling capabilities that traditional designs and control can't

Point maps have become a powerful representation for image-based 3D reconstruction. What if we could push point maps even further tackle 3D registration and assembly? Introducing Rectified Point Flow (RPF), a generic formulation for point cloud pose estimation.

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

have been waiting for this release! Robotics needs rigorous and careful evaluation now more than ever 🦾

Tactile interaction in the wild can unlock fine-grained manipulation! 🌿🤖✋ We built a portable handheld tactile gripper that enables large-scale visuo-tactile data collection in real-world settings. By pretraining on this data, we bridge vision and touch—allowing robots to:

Really cool invited talk by Shuran Song - “Making Video Model Useful for Robots”

Thank you Kosta Derpanis!!