Amazing use case of a @boosterobotics for Halloween.

Today we’re out of stealth, and we’ve raised a $40m seed round co-led by @creandum and @usv, to bring Instant Robot Learning to life. For the past few months, we‘ve been busy building a new company. Machenta. This follows results from a new kind of robot foundation model we’ve built, which I’m so excited to share with you today! Usually, it takes hours/days of data collection and training to teach a robot. We’ve built a model that learns tasks instantly: one demonstration, no further training. A model trained entirely in simulation, without a single pixel of real-world data. A model where teaching robots is so quick and easy, it feels like physical vibe coding. A model for robots that learn like humans do, from someone like you. Welcome to the era of Instant Robot Learning. Thank you @Kathi_Wilhelm, @rebeccakaden, @hkanji, @jonno_evans and all of our angels for your support! @Machenta_ Learn more: machenta.com/blog/instant-ro… See below for what you can do with Instant Robot Learning!

This humanoid has no skin, no face and over 1,000 artificial muscles. 😳 Meet Protoclone by Clone , a biomimetic android designed around a human-like skeleton and synthetic muscles called Myofibers. 🦴 200+ degrees of freedom 💪 1,000+ artificial muscle fibers 🧠 500 sensors, according to the company Instead of conventional electric motors at every joint, its muscle-like actuators contract using fluid pressure to move the skeleton. It is still a prototype, but its design takes humanoid robotics in a very different direction. Does this look like the future of robotics or something straight out of a sci-fi horror movie? 🏢 Company: @clonerobotics ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators. #CloneRobotics #Protoclone #HumanoidRobots #BiomimeticRobotics #ArtificialMuscles #Robotics #CTORoboticsMedia

Bring your own model (Qwen running on Jetson Thor in this case) to your domain, give it the spatial harness, and boom. Any model can leverage Cactus data, spatio-semantic or any other data in your domain, and even robots or other devices.

Physical Intelligence's co-founder put his entire Berkeley course on YouTube: every lecture from this spring, plus the homework code. "CS 185/285: Deep Reinforcement Learning," Sergey Levine (@svlevine), UC Berkeley, Spring 2026. Imitation learning, policy gradients, actor-critic, Q-learning, model-based RL, offline RL, exploration, RL for LLMs and multi-task learning - 25 lectures plus 2 guest talks, about 36 hours, free. Lectures 2 and 3 are learning from human demos, the same idea behind ALOHA and π0. the rest is how a robot keeps getting better after the demos run out. Figure is hiring an RL Engineer for whole-body control right now, at $150K–$350K. this course is what the "RL" in that title means. Lectures: youtube.com/watch?v=DD8APgTE… Homework: github.com/berkeleydeeprlcou… P.S. lecture 14 is RL with sequences and LLMs, the same toolkit used to train today's reasoning models.

JR西日本 新たな鉄道保守ロボットを導入へ news.web.nhk/newsweb/na/nd-2… #nhk_video

Robotics fashion is now an industry

Paris, France: already covered. 🇫🇷 Paris, Illinois: now covered too. 🗼 Two Parises. One network. New stations went live across the U.S. this week, adding more centimeter-level RTK coverage to the world's largest RTK network. 22K+ stations. 175 countries.

By 2035, 900 billion AI agents will run robot cafés, autonomous farms, and the cars that drive you home. But how do billions of independent machines agree on what happens next without a central brain? They speak Tashi.