Notes from the lab on sensing, actuation, Physical AI, and robot learning for the extreme environments where machines must perceive and act.
Welcome to the first installment of our "Robotics Zero to Hero" series. Before a robot can interact with the world, it must answer a deceptively simple question: *where am I, and which way am I facing?* This is the **Geometry of Motion**, and getting it right is the foundation everything else is built on.
Welcome to Part 2 of "Robotics Zero to Hero." In Part 1 we learned to describe a single rigid body with $SE(3)$. Now we chain those transforms together to form a robot's "skeleton." This is **Kinematics** — the geometry of motion *without* worrying about the forces that cause it.
Welcome to Part 3 of "Robotics Zero to Hero." Forward Kinematics (Part 2) maps joint values to a tip pose. But in the real world a robot is handed a *goal*: *"Grab the cup at $(x,y,z)$."* It must compute the joint values that achieve that pose. This is **Inverse Kinematics (IK)** — and it is where low-dimensional and high-dimensional robotics part ways most dramatically.
Welcome to Part 4 of "Robotics Zero to Hero." Kinematics (Parts 1–3) described motion without asking *what causes it*. But robots have mass, accelerate hard, and fight gravity. To control them accurately we need **Dynamics** — the relationship between forces/torques and motion.
Welcome to Part 5 of "Robotics Zero to Hero." We've covered the clean mathematics of kinematics and dynamics. But moving from a perfect Python simulation to physical hardware introduces an entirely new adversary: **reality**.
Welcome to Part 6 of "Robotics Zero to Hero." As we saw in Part 5, real robots — especially our flexible, cable-driven octopus — exhibit dynamics on *wildly different time scales* at once. How do you control a system that moves slowly and massively while also vibrating fast and jittery? You decompose it with **Singular Perturbation Theory**.
Welcome to Part 7 of "Robotics Zero to Hero." We can model geometry, compute kinematics, and stabilize dynamics. The next step is autonomy: getting from Point A to Point B without crashing. This is **Motion Planning** — and it is the clearest example in all of robotics of how high dimensionality changes the *kind* of algorithm you must use.
Welcome to Part 8 of "Robotics Zero to Hero." We've built an advanced software stack: kinematics, singularly-perturbed dynamics, and diffusion-based planning. But software is useless without hardware to run it — *fast, locally, and within a power budget.*
Welcome to the grand finale of "Robotics Zero to Hero." We have traversed the foundational math, the physics of reality, modern AI planners, and specialized edge hardware. How do we fuse these disparate systems into a single coherent, intelligent agent? With **Multi-Modal Fusion** and high-level **LLM controllers**.
Welcome to the finale of "Robotics Zero to Hero." Parts 1–9 built a robot that can *think*: it knows its geometry (Part 1), its kinematics and dynamics (Parts 2–4), it can plan (Part 7) and reason with learned models (Parts 8–9). Part 5 confronted the **sim-to-real gap** — the moment theory meets a noisy, frictional world.