This past Tuesday, we opened the doors to our new office to host a Smart Fleet Meet & Greet as a part of #SFTechWeek. Thank you to all our attendees for your exciting energy, curiosity, and appreciation for what we’ve been working on. We can’t wait to show you more of what’s to come soon.

Chinese vendors made up nearly all humanoid shipments in the first half of 2026.  @BrookeDiPalma from @YahooFinance had OpenMind founder and CEO Jan Liphardt on to discuss this topic in a live segment earlier this week. @JanLiphardt says there’s “no question” China dominates the supply chain for advanced physical AI, and breaks down how the US can catch up in the global robotics race in the next 2 years. Read the post here: finance.yahoo.com/video/does… – at San Francisco, CA

Phase Action Space for cross-embodiment Representation (PHASOR for short) introduces a universal action representation that maps different humanoid embodiments in a shared motion space, making robot behavior more transferable, interpretable, and scalable across platforms. At OpenMind, our deployments involve mixed fleets that operate on one brain. PHASOR helps our team run shared interpretable actions that are transferable across devices without a need for additional re-training. PHASOR is a collaboration with @AIMSafety_, @LGE_Global, Seoul National University and MAUM AI. Read the research here: openmind.com/resources/phaso…

Our index places China first with a composite score of 82.9 out of 100. Japan follows with a gap at 69.6, while the United States is at third with a score of 69. The scatter plot mapping willingness to deploy and capacity to build suggests something worth noting: countries with strong AI research programs do not necessarily score higher on readiness in a consistent manner. In fact, the two variables actually move somewhat independently of each other.

@forbes interviewed OpenMind's CEO @JanLiphardt about the team's recent research report on Physical AI Readiness Index (PAIRI) to gain a clearer understanding of the global willingness & ability to deploy physical AI at scale. With the FCC's recent ban on foreign robotics hardware imports, we evaluated data from 58 countries and 22 metro clusters on their capacity to build, supply, and power embodied AI systems. Surprisingly, the data found that the observable gaps in readiness stemmed more so from manufacturing & supply capacity over AI research ability. Link to Forbes Article: forbes.com/sites/johnkoetsie… Read more about our findings from the research paper on the thread 🧵below:

OpenMind joined our partner TRMI in celebrating 40 years of innovation and brought along one of our humanoid robots for the occasion. Congratulations to the entire TRMI team on this incredible milestone. We’re proud to celebrate with you and excited for what’s ahead.

One runs on kibble, the other runs on code. 📸: @KraneShares

Robots should not care about your lighting. Our paper on Domain-Invariant Latent Lookahead (DILL) was selected for a presentation at RSS 2026. VLA policies quietly learn visual shortcuts: change the camera angle, the background, or the light, and they execute the wrong task. DILL teaches the policy to predict a domain-invariant future, not the pixels. This is the work behind the promise: OpenMind builds robot experiences that stay reliable in the real world, where the lights, the layout, and the cameras never match the lab. Huge thanks to our partners at @SeoulNatlUni, @Hyundai, Ajou University, and Tommoro Robotics. Stay tuned for more from OpenMind Research.