Behavior trees for robots explained: ticks, control and leaf nodes, reactivity vs FSMs, blackboards and Nav2/BehaviorTree.CPP - the reference architecture for robot task planning and recovery in 2026.
SLAM architecture for autonomous robots explained: the front-end/back-end pipeline, factor-graph optimization, loop closure, visual vs LiDAR-inertial fusion (ORB-SLAM3, LIO-SAM) and failure modes in 2026.
A reference architecture for self-driving labs: the design-make-measure-learn closed loop, orchestration, safety interlocks, and human-in-the-loop control for autonomous experimentation.
Industrial world models explained: how learned simulators and physical AI give robots and digital twins predictive foresight, and what it means for factory autonomy in 2026.
Isaac Sim vs Gazebo vs MuJoCo compared for robotics: physics fidelity, GPU parallelism, photorealistic sensors, ROS 2 integration, sim-to-real, and a decision matrix for 2026.
Why physical AI - foundation models for robots and machines - is reaching the factory floor in 2026: VLA models, humanoids, simulation, and the deployment reality.
Figure 03 and the BotQ factory analyzed: what ~1 robot/hour really means for humanoid mass production, the supply chain, unit economics, and deployment reality.