Daniel Morton

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, arxiv.org/abs/2608.03496 — Foundations of robot autonomy, from classical methods to modern AI-based approaches. 🎥 Video lectures, porabook.com/lectures/ Lectures from my Stanford courses on robot autonomy and on optimal and learning-based control. 💻 Interactive exercises, porabook.com/exercises/ — Hands-on Python implementations and Jupyter notebooks. 📝 Teaching materials, porabook.com/instructors/ — Lecture slides and resources for instructors. 🌐 Explore all PoRA resources, porabook.com/ — 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: news.stanford.edu/stories/20… 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

Introducing ConstrainedMimic (arxiv.org/pdf/2606.00374) — a control framework for #humanoid robot #safety that enables real-time constraint enforcement within #RL-based motion tracking policies by leveraging whole-body kinematics and dynamics. Recent advances in reinforcement learning have unlocked remarkable whole-body agility for humanoid robots. However, ensuring safety and satisfying constraints—especially those introduced after training—remains a significant challenge for deploying safe and reliable systems. ConstrainedMimic addresses this challenge by combining ideas from operational space control and control barrier functions (CBFs). The framework enables enforcement of arbitrary runtime constraints while preserving the ability of the policy to track complex motions. Importantly, constraints can be imposed on both the kinematic reference motion and the underlying robot dynamics, providing a principled approach to safer, more robust, and more controllable humanoid behavior. As #PhysicalAI, #humanoid #robotics, and #embodied #AI systems move from research environments into the real world, the ability to guarantee safety and respect operational constraints will become increasingly important — ConstrainedMimic is a step in this direction. 📄 Paper: arxiv.org/pdf/2606.00374 💻 Code: Coming soon Great work led by @danielpmorton . #PhysicalAI #AISafety #HumanoidRobotics #EmbodiedAI #ReinforcementLearning #Robotics @StanfordAILab @StanfordEng

Introducing FRAX: Fast Robot Kinematics and Dynamics in #JAX — to be presented at the 2026 IEEE International Conference on Robotics and Automation (ICRA) Frontiers of Optimization for Robotics (FOR) Workshop. FRAX delivers extremely fast (low-microsecond) execution for common inverse-kinematic and inverse-dynamic control workloads, with a pure Python codebase that can achieve up to 5× faster performance than MuJoCo or Pinocchio Python bindings in several settings. At the same time, FRAX is fully differentiable and seamlessly compatible with CPU, GPU, and TPU execution through #JAX — enabling scalable workflows spanning robotics, control, planning, and machine learning. Our broader goal is to help bridge the gap between modern AI tooling and robotics computation, making it easier to develop scalable #Physical #AI systems. This also makes FRAX a great complement to CBFPY (github.com/StanfordASL/cbfpy), our package for robot safety and control barrier functions. Kudos to @danielpmorton for leading this effort. If you’ll be at ICRA, reach out! The FOR Workshop is on Monday, June 1, and we’ll have a poster there. 💻 GitHub: github.com/StanfordASL/frax 📄 Paper: arxiv.org/pdf/2604.04310 #Robotics #PhysicalAI #JAX #DifferentiablePhysics #MachineLearning #AutonomousSystems #GPU #Simulation #ICRA

Check out our #IROS2025 work on robot safety! We show low-latency teleop up to the limits of the hardware's capabilities, all while enforcing hundreds of CBF constraints at kilohertz control rates. 📄 arxiv.org/pdf/2503.06736 🌐 stanfordasl.github.io/oscbf/ With @danielpmorton