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Stanford ASL

@StanfordASL

The Autonomous Systems Lab (ASL) develops methodologies for the analysis, design, and control of autonomous systems. @Stanford
asl.stanford.edu Joined April 2018
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Stanford ASL @StanfordASL
Ever wondered what it’s like to learn robot autonomy at Stanford? Now you can, for free!

Principles of Robot Autonomy fuses classical robotics and modern AI: textbook, video lectures, Python notebooks, and an AI study assistant. Nearly a decade of Stanford teaching, open to all.
Marco Pavone Verified account @drmapavone Oct 22
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/2026/1…

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
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Stanford ASL @StanfordASL
💡Can open-source models verify their own outputs and beat frontier models doing so?

Check out @jackyk02's work on verification scaling: sampling just 5 solutions with DeepSeek V4 Flash + LLM-as-a-Verifier outperforms Claude Fable 5 on Terminal-Bench at 11x lower cost.👇
Jacky Kwok Verified account @jackyk02 Aug 17
Scaling self-verification with DeepSeek V4 Flash beats Claude Fable 5 on Terminal-Bench 2.1, while being 11x cheaper 💰

As open-source models become more capable, they can now generate large numbers of high-quality candidate solutions and verify their own outputs at very low cost.

For example, we find that sampling just 5 solutions with DeepSeek V4 Flash and ranking them using the same model with LLM-as-a-Verifier can lead to a significant boost in accuracy (79% → 88%), outperforming closed frontier models on Terminal-Bench.

Try it out today: github.com/llm-as-a-verif…

More on verification scaling in my previous post.
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Stanford ASL @StanfordASL
🔔Scalable and safe deployment of generative robot policies in the real world requires that we actively monitor their behavior and issue warnings when they are failing.

Check out @agiachris and @RohanSinhaSU latest work on runtime monitoring for generative robot policies.
In real-world deployment settings, generative robot policies are bound to encounter out-of-distribution scenarios that cause them to fail in unexpected ways.

We present Sentinel, a runtime monitor that detects unknown failures of generative policies at deployment time!💂(1/6)
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Stanford ASL @StanfordASL
💡For human-robot interaction, human preferences need to be captured at all levels of the robot planning stack: task, motion, and control!

Check out Text2Interaction from Jakob and @agiachris
How can robots incorporate human preferences into their plans?

Introducing Text2Interaction: a long-horizon, skill-based planner that meets human preferences at the task, motion, and control levels zero-shot using code writing LLMs.

Project site: sites.google.com/view/text2inte…
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