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

A full MIT course on visual autonomous navigation. If you work on robotics, drones, or self-driving systems, this one is worth bookmarking‼️ MIT’s Visual Navigation for Autonomous Vehicles course covers the full perception-to-control stack, not just isolated algorithms. What it focuses on: • 2D and 3D vision for navigation • Visual and visual-inertial odometry for state estimation • Place recognition and SLAM for localization and mapping • Trajectory optimization for motion planning • Learning-based perception in geometric settings All material is available publicly, including slides and notes. 📍 If you know other solid resources on vision-based autonomy, feel free to share them. —- Weekly robotics and AI insights. Subscribe free:

It's good to see robotics finally getting the recognition it deserves these days. The venture capital market says so, the steady flow of talent says so, and of course so does the progress in AI, which promises that the physical world can be automated, despite being so unpredictable and messy for machines. That's exactly why I wanted to shine a light on the people building in the robotics space. They're a bit like Renaissance men. Their skills go way beyond writing code. They have to bridge software with the real world, and despite how it might sound, that's an incredibly hard challenge. Atoms are harder to control than bits. Here are just some of the fields a roboticist must be aware of: → Software engineering & robotics frameworks: Fluency in Python/C++ and tools like ROS 2 and simulators. Modern robots are software-defined, so code quality is critical. → AI & ML for perception and control + data: Computer vision, sensor fusion, and deep/reinforcement learning. Knowing when to use classical vs. learned methods is key. → Systems integration & mechatronics: Understanding how sensors, actuators, electronics, and embedded systems fit together. Debugging across the hardware/software boundary sets strong engineers apart. → Control theory & motion planning: From PID and MPC to trajectory optimization and path planning. This is what makes robots move reliably in the real world. → Collaboration & safety-aware design: Communicating across disciplines and designing for reliability and safety standards. Deployment realities and failure modes matter as much as features. Hats off to all these great people working in robotics space, and focusing on really hard problems :) Image credits: @jbhuang0604 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →