The Business of Robotics and Physical AI. Research & Podcast. https://xitter.cf/t.co/aanMm8guOj

Joined June 2026
Core Matter retweeted
Could a robot run for a flight without spilling its coffee? I started thinking about this during my own airport sprint, coffee in hand. For a humanoid, 6 senses are working together at all times: - Sight: What can it see? - Depth: How far away is the obstacle? - Balance: Where is its body, and is it balanced? - Force: How much force is it applying? - Tactile: Is something slipping at the contact surface? - Audio: What can it hear? New Core Matter @CoreMatter_ field guide walks through sight, depth, balance and position, force, touch, and hearing. Including each supply chain and deployment failure modes. The guide also looks at @Waymo's camera, lidar and radar approach and @Tesla's camera-based Tesla Vision.
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Core Matter retweeted
Inside tactile robotics w/ Professor Shan Luo, King's College London Robot skin changes. Models must adapt. “But hardware still matters a lot.” Luo leads the Robot Perception Lab. We discuss Unilever's material-assessment questions, Ocado's delicate-object handling, and the data behind a robot's sense of touch. GenForce studies transferring force-prediction knowledge across tactile sensors. His ViTacGen example concerns object pushing: learning uses physical touch, while deployment in that task can use vision. We cover: › Material assessment and contact force › Cross-sensor learning with GenForce › Vision-to-touch generation for object pushing › What changes when robot skin is replaced? › Task-specific finger and sensor design › Unilever, Ocado and open tactile datasets Chapters 00:00 Cold open: what happens when robot skin changes? 00:29 Why Shan Luo chose tactile sensing 02:04 Scaling tactile data and simulation 03:40 The interface between different sensors 05:04 Material assessment, cleaning and insertion 09:18 Vision, contact and the last mile 11:54 Manipulating transparent objects 14:29 GenForce: transferring force-prediction knowledge 17:59 Hardware, shared datasets and standards 21:43 ViTacGen: learning from touch, deploying with vision 25:42 How tactile sensor skin wears 28:43 Replacement skin and model calibration 30:49 Manufacturing cost and consistency 32:38 TacDiff: designing the finger and policy together 36:47 Industrial research with Unilever and Ocado 40:01 ViTac workshop and open tactile data 45:11 A startup question and a surprise announcement 49:43 What warehouse robots could feel in 2031
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Core Matter retweeted
I’ll be at IROS this week and look forward to connecting with researchers and operators building dexterous hands, autonomous factories, world models. Core Matter @corematter_ is co-hosting Robotics Research Night with Saturday Robotics @saturdayrobotic on Monday 9/28. Come join us for an evening of research discussions. Big thanks to our sponsors @FAIRplus_sz and Daimon Robotics. Come join us! Sign up here: luma.com/tzbw7n61 == @junfanzhu98 @aurorafeng_01
🛫 I’m heading to Pittsburgh for IROS 2026 🤖 9/27–10/2. Introducing 🥂 @saturdayrobotic × #IROS2026: Saturday Robotics Research Night 📍 Pittsburgh | Sept 28, 5:30–9:30 PM 🎟️ RSVP: luma.com/tzbw7n61 Lightning talks from researchers at @Caltech, @RhodaAI , @XSquareRobot, @CAS__Science, @UCBerkeley, and @HarryXu12 (@PokeBot_Global), covering: • humanoid safety + RL • in-context learning for robot data • scalable sim evaluation • 3D VLA + memory + inference-time learning • hindsight reward editing • GPT-6 Astra & the future of robotics The interesting part isn’t just the demos. It’s figuring out what actually scales—and what doesn’t. A few questions to discuss at Saturday Robotics IROS research night: → Is robot scaling really about more data—or better experience per sample? → Can in-context learning turn robot data from static demonstrations into reusable experience? → Will world models become the data engine for robotics? → What should robots remember over long horizons? → And what does scaling not fix—representation, architecture, reward, evaluation? If you’re working on robot foundation models, world models, in-context learning, robot data, sim2real / Real2Sim2Real—or just trying to answer “how do we actually scale robot learning?”—let’s grab coffee ☕️. See you in Pittsburgh. 👋 @aurorafeng_01 @michellelsun
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