Do Vision-Language-Action models mean what they say? Embodied chain-of-thought offers a promising way to leverage additional inference-time computation for robot policies, with empirical evidence that it can improve downstream task performance. However, in practice the extent to which this chain-of-thought actually influences a robot policy’s chosen actions is not fully understood yet. In our new paper on #embodied #reasoning, we formally define the notion of faithfulness i.e., whether the chain-of-thought truthfully reflects the policy's decision for #Physical #AI and argue that the faithfulness of robot policies should play an explicit role in algorithm design. We train a critic of embodied faithfulness, identifying the signatures of inconsistent and incoherent reasoning, and use it as a reward signal during reinforcement learning (RL) post-training of a robot policy. We call our critic Pinocchio 🤥: a model that learns to tell when a robot policy is 'lying' about the justifications for a decision. Pinocchio scores two complementary failure modes: whether the reasoning is grounded in what the robot actually sees, and whether it is coherent step-to-step through the chain-of-thought. We find that post-training for faithfulness yields a +18% improvement in reasoning consistency over standard baselines, and critically, makes the policy 1.6× more responsive to rare, out-of-distribution scenarios that confuse SoTA models. These findings suggest that faithful embodied reasoning is not merely a post-hoc interpretability property, but an important ingredient for building more robust and trustworthy autonomous systems. 📄 Paper: arxiv.org/pdf/2607.04681 🌐 Project page: mjf-su.github.io/pinocchio/ 💻 Code: Coming soon! Great work led by Matthew Foutter, in collaboration with Matteo Cercola Lena Wild Yunshan Wang Michelle Li and @DanieleGammelli @StanfordEng @StanfordAILab @StanfordHAI

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