Advanced Robotics
40 MIN READ
Module 01: The "Why" and The Architecture
Why L5 autonomy is harder than a moon landing. Understanding ODD, latency loops, compute constraints, and the modern Hybrid Architecture (Modular vs. End-to-End).
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Autonomous & Real-World AI
Your Unique Edge
How self-driving cars actually work—prediction, calibration, sensing, and closed-loop reasoning.
Learning Path
- → Start with **Behavior Prediction** for closed-loop autonomous driving
- → Explore **Camera Calibration** (AutoCalib) for perception systems
- → Learn about **Physics-Aware Models** for simulation and robotics
All Posts in This Track
12 articles covering robotics
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- Advanced Robotics45 MIN READ
Module 02: The Senses of an Autonomous Vehicle
The raw senses of an autonomous vehicle: What data does each sensor provide? Covers cameras, radar, LiDAR, ultrasonics, and microphones—their physics, strengths, weaknesses, and why fusion is necessary.
- Advanced Robotics30 MIN READ
Module 03: The Bedrock (Calibration & Transforms)
If you don't know where your eyes are relative to your feet, you trip. Covers intrinsics, extrinsics, SE(3) transforms, online vs. offline calibration, and time synchronization.
- Advanced Robotics35 MIN READ
Module 04: Localization — The Art of Not Getting Lost
From GPS to centimeter accuracy: How autonomous vehicles know their exact position. Covers GNSS, IMU, wheel odometry, scan matching, and Factor Graphs.
- Advanced Robotics40 MIN READ
Module 05: Mapping — The Memory of the Road
How autonomous vehicles remember the world. Covers HD maps, lane graphs, offline vs. online mapping, MapTR, and the map-heavy vs. map-light debate.
- Advanced Robotics65 MIN READ
Module 06: Perception — Seeing the World
From pixels to 4D realities: How AVs understand their environment. Deep dive into BEV Transformers, Panoptic Occupancy, Scene Flow, and Foundation Models for open-world perception.
- Advanced Robotics60 MIN READ
Module 07: The Fortune Teller — The Evolution of Prediction
The hardest problem in AV: predicting human irrationality. From physics-based Kalman Filters to Joint Autoregressive Distributions, Generative Motion Diffusion, and World State Propagations.
- Advanced Robotics45 MIN READ
Module 08: The Chess Master — The Art of Planning
From perception to action: How autonomous vehicles make decisions. Covers cost functions, game-theoretic planning, MPC, and the "End-to-End" debate.
- Advanced Robotics55 MIN READ
Module 09: The Unified Brain — Foundation Models in Autonomous Driving
From modular stacks to unified intelligence: How foundation models are reshaping AV and generalist robotics. Covers VLA models (GR00T, Pi0), Physical AI, and the 2026 embodied revolution.
- Advanced Robotics40 MIN READ
Diffusion for Action: Trajectories and Policy
How diffusion models predict action sequences instead of pixels. Covers Diffusion Policy, world models for robotics, and connecting diffusion to reinforcement learning for autonomous systems.
- Advanced Robotics20 MIN READ
The Role of Predictions in Closed-Loop Autonomous Driving
Reflections on building production-grade behavior prediction systems for autonomous vehicles — and why closed-loop reasoning is the bridge between perception and planning.
- Advanced Robotics15 MIN READ
AutoCalib – Automatic Traffic Camera Calibration at Scale
How we used deep learning to automatically calibrate traffic cameras by observing vehicle motion—work that won Best Paper Award at ACM BuildSys 2017.