Catching skin cancer early is a home robotics problem. Melanoma is highly treatable when detected early, yet today’s screening process depends heavily on patients noticing tiny changes across their entire skin surface. This requires patients to solve a near-impossible visual-memory and registration problem. I built OpenDerm, an open-source 4-DOF robot that captures high-resolution images of the skin and uses them to reconstruct and track the skin surface in 3D over time. The best way to make skin screening truly routine is to bring it into the home. OpenDerm shows that inexpensive robotic skin imaging is possible, but the path to scale is not a dedicated screening robot in every household—it is to make skin screening one of the many useful things a general-purpose home robot can do. Read more about why I built OpenDerm and how it works here: Blog: Project:

Many tasks cannot be completed by one robot alone. But coordinating multiple robots performing complex manipulation tasks is very difficult. Many solutions rely on complicated centralized control, which tends towards bespoke methods that do not scale well with team size. Enter CHORUS by @riadoshi21, @leto__jean, and team. They train a single VLA to control multiple, diverse robots, given only local observations and identifying information. This allows robots to collaborate on tasks like using a tape measure, handing over a book, or lifting a laundry basket. To learn more, watch Episode #93# of Robopapers now, with @micoolcho, @DJiafei, and @chris_j_paxton!

🤖 Can a single robot policy manipulate diverse tools without ever seeing them before? Introducing SimToolReal 🔨 : a generalist dexterous manipulation policy that transfers zero-shot sim→real to unseen tools + unseen tasks All videos are 1x speed (60 Hz control) 🧵👇

Long horizon bimanual mobile manipulation requires reasoning in many coordinate frames: base, L/R hands, etc. In which frame would policy work best? It really depends, so don’t pick. Mixture of Frames Policy: denoise in multiple frame in parallel. 🌐 (1/9)

🤖 How can we teach dexterous robots to perform precise, contact-rich assembly? Introducing Play2Perfect: first learn to play with objects, then perfect the policy for tight insertion, multi-part assembly, and screwing. Sound on! 🔊 🧵👇

🤔 Can we train one VLA policy to control multi-robot teams without any explicit communication? ✨ Introducing CHORUS: a single policy for decentralized, multi-embodiment collaboration 🧵⬇️

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