Autonomous systems need both accurate sensing and reliable connectivity. #FMCW# #LiDAR# delivers precise motion and distance data, while high-frequency #wireless# enables real-time communication and response. See how Sivers Semiconductors is powering next-generation sensing and connectivity: #AutonomousSystems# #Robotics# #EdgeAI#

“How Autonomous Systems Are Reducing Human Intervention” 🤖💡 From Netflix’s self-healing cloud to Amazon’s automated warehouses, autonomous systems are streamlining operations and minimizing manual effort. #AutonomousSystems# #Automation# #IntelligentSystems# #Nimblechapps#

Introducing PLUTO (Plain Language Understanding for Trajectory Optimization), our new research exploring how #agentic# #AI# can make sophisticated #aerospace# #engineering# tools more accessible. PLUTO combines an AI coding agent with established numerical optimization methods, using sequential convex programming as its backbone. The agent translates natural-language requirements into executable constraints, formulates the optimization problem, and helps visualize and verify the resulting trajectory. The key idea: let AI handle problem formulation while relying on rigorous numerical methods to solve it. 📄 Paper: 💻 Code: Coming soon. This is our first step toward accelerating aerospace mission design and operations through agentic AI workflows. Stay tuned for more soon! Work led by Eleanor Brosius, in collaboration with Yuji Takubo, @DanieleGammelli, and Simone D'Amico #AgenticAI# #Aerospace# #TrajectoryOptimization# #AutonomousSystems# @StanfordEng @StanfordAILab @StanfordASL

How reliably can today’s robot policies complete tasks when the world changes mid-execution? In real-world robotics, things rarely go exactly as planned. Objects move, unexpected obstacles appear, and observations change as robots interact with their surroundings. Understanding how well robot policies handle these changes is essential for deploying Physical AI in the real world. In our new paper, LIBERO-MAX: Do Robot Policies Adapt When the World Changes?, we introduce a benchmark designed to study this question. We evaluate 14 state-of-the-art robot policies across 8,000 cases, introducing controlled changes after the robot has already begun acting. The results suggest that adapting to a changing world remains a key challenge for today’s robot policies. Task success drops by 11–26 percentage points, particularly when geometry or observations change, and simply updating policies more frequently does not close the gap. LIBERO-MAX provides a systematic way to measure these challenges, helping identify where current policies fall short and guiding the development of more adaptive and robust Physical AI systems. 📄 Paper: 🌐 Benchmark and project website: 💻 Code: Work led by @YunbeiZhang, in collaboration with Zijian Jin, Yuanzhe Liu, Janet Wang, Xilun Zhang, Yuyou Zhang, Zhenyu Zhang, Daoan Zhang, Shuaicheng Niu, Gen Li, Jianfei Yang, Jihun Hamm, Ismini Lourentzou, Weirui Ye, Bo Liu, and Peter Stone. #Robotics# #PhysicalAI# #RobotLearning# #RobotFoundationModels# #AutonomousSystems#

Ever wondered what it’s like to learn robot autonomy @Stanford? Now you can. Today, we’re opening up the robot autonomy teaching materials that my collaborators and I have been developing over the past several years—for anyone, anywhere, completely free. At the heart of this effort is Principles of Robot Autonomy (PoRA), a textbook inspired by the Stanford robot autonomy course series I’ve been teaching since 2017. Robot autonomy has evolved from a research discipline into a technology deployed in the real world. Building capable autonomous systems requires more than understanding individual algorithms: it requires understanding how perception, state estimation, planning, control, and learning work together in a complete autonomy stack. That’s what we set out to teach with PoRA: bringing classical robotics and modern AI together in one coherent framework, grounded in both theory and practice. Coming soon in print from Cambridge University Press, Principles of Robot Autonomy is also freely available online and comes with a comprehensive collection of open educational resources: 📖 Textbook, — Foundations of robot autonomy, from classical methods to modern AI-based approaches. 🎥 Video lectures, Lectures from my Stanford courses on robot autonomy and on optimal and learning-based control. 💻 Interactive exercises, — Hands-on Python implementations and Jupyter notebooks. 📝 Teaching materials, — Lecture slides and resources for instructors. 🌐 Explore all PoRA resources, — Including Ask the Book, an AI study assistant integrated throughout the textbook to answer questions, explain derivations, and connect ideas across chapters. 📣 Read the Stanford announcement: Our goal is simple: to make high-quality education in robot autonomy accessible to students, researchers, engineers, educators, and anyone curious about how autonomous robotic systems work. I believe that sharing knowledge is fundamental to the mission of a university. Autonomous robotics—and Physical AI more broadly—are among the defining technological frontiers of our time. We want to help more people understand these technologies, build with them, and advance the field. A huge thank you to my co-authors, @DanieleGammelli, Joseph Lorenzetti, @katielulula, and @GioeleZardini, and to everyone who helped bring this project to life. Explore the resources, use them in your own learning or teaching, and please share them with anyone who might find them useful! #Robotics# #PhysicalAI# #ArtificialIntelligence# #AutonomousSystems# #Education# @Stanford @StanfordEng @StanfordAILab @StanfordHAI

How do we make robot policies robust to rare but high-impact failures? Video #World# #Models# (WMs) are rapidly becoming a powerful tool for robotics, enabling policy evaluation and improvement by "imagining" future outcomes. But there's a catch: these imagined futures are typically nominal samples, making it easy to overlook the rare yet safety-critical events that matter most. In our new paper, StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement, we explore a simple but powerful idea: 💡 Instead of passively sampling futures, actively steer world model imaginations toward high-impact yet still plausible scenarios. StressDream optimizes the initial diffusion noise at inference time, allowing us to generate targeted stress-test scenarios without retraining the world model. This enables: - More robust policy evaluation by exposing failure modes that random sampling often misses. - Improved policy optimization by training against challenging but realistic imagined futures. As generative world models become a foundation for #Physical# #AI#, the ability to systematically probe their "long tail" of plausible futures will be increasingly important for building reliable and trustworthy autonomous systems. 📌 𝖯𝗋𝗈𝗃𝖾𝖼𝗍 𝖯𝖺𝗀𝖾: 📄 𝖯𝖺𝗉𝖾𝗋: Work led by Junwon Seo, with a great set of collaborators: Sushant Veer, Thomas Ran Tian, Wenhao Ding, Apoorva Sharma, Karen Leung, Edward Schmerling, Andrea Bajcsy. @NVIDIADRIVE @NVIDIAAI #Robotics# #WorldModels# #PhysicalAISafety# #AISafety# #AutonomousSystems# #RobotLearnin#

"The Age of Autonomous Systems" isn't a forecast. It's the stack being built right now: agents that act, transact, and get things done on your behalf. The premise underneath all of it is a trust layer, which is exactly what Kite is building. We are pleased to share that our Co-Founder & CEO @ChiZhangData will speak in the Tech Track at GTLC 2026 Bay Area Chinese Tech Leaders Summit, hosted by TGO Silicon Valley. She shares the stage with founders and researchers including: ▷ Yuandong Tian (@tydsh), Founder of Recursive Superintelligence, the $4.65B self-improving AI lab. Former Research Director at Meta FAIR, and earlier on Google X's self-driving car team (now Waymo). ▷ Yangqing Jia (@jiayq), creator of Caffe and Co-Founder & CEO of Lepton AI (acquired by NVIDIA). Former VP at NVIDIA, VP at Alibaba Group, and AI Director at Facebook. ▷ Junchen Jiang (@JunchenJiang), CEO of Tensormesh and Professor at UChicago, co-creator of the open-source LMCache. ▷ Bo Li, Co-Founder & CEO of Virtue AI and Professor at UIUC, a leading scholar in AI safety and trustworthy ML (IJCAI Computers and Thought Award, Sloan Fellow). ▷ Ryan Hanrui Wang, SVP Applied AI at Nebius and former Co-Founder & CEO of Eigen AI (acquired by Nebius for $643M), out of MIT's HAN Lab. ▷ Ding Zhao, Professor at CMU and director of the Safe AI Lab, focused on trustworthy and safe AI. ▷ Weiyan Shi (@shi_weiyan), Professor at Northeastern and AI2050 Fellow, who built the negotiation agent behind Meta's CICERO. 📅 July 17, 2026 · Tech Track 2:00 PM 📍 Q Bay Center, San Jose If you're in the Bay Area, come find us. 🪁 🔗

"America must now build autonomous systems at both the quality and quantity required to win. We have the talent advantage. We are losing the production race. The stakes are whether the United States arrives at the next conflict with overwhelming superiority in autonomy, or whether we cede that advantage to adversaries who are already building it." a16z's Daniel Penny, Alex Oliver, and Zach Chen on the moral case for autonomous warfare:

Emerging technologies such as drones and autonomous systems are changing the future of warfare 🛡️ NATO is accelerating their adoption to ensure it has the capabilities needed to protect our one billion people. Tap to learn more ↓

Agentic AI introduces a new model where autonomous systems can coordinate, make decisions, and engage in commerce. That raises an important question: what financial infrastructure best supports those interactions? In this clip, I share how we’re thinking about that shift and why TRON is the answer.