Go2 Autonomous Navigation — Learned Nonlinear MPC

January 1, 2026 · 1 min read

A data-efficient framework that learns the cost weights of a nonlinear MPC navigation stack using Bayesian Optimization, treating closed-loop performance as a black-box objective and deploying the result on a Unitree Go2 quadruped.

Add a demo clip at static/videos/go2-navigation.mp4 to play it in the homepage showcase.

Model Predictive Control Bayesian Optimization Legged Robotics ROS 2
Lorenzo Ortolani
Authors
Lorenzo Ortolani (he/him)
Roboticist · Physical AI & VLA for Humanoids
I build Physical AI — robots that perceive, reason, and act in the real world. My focus is advancing Vision-Language-Action (VLA) policies so that a single humanoid can learn a diverse library of skills and choose the right one for whatever industrial task it faces, generalizing far beyond a single demonstration. I co-founded TalOS Robotics AI to put this in operators’ hands, and I work across the full stack: locomotion and navigation, learned manipulation, sim-to-real, and the agent tooling that wraps it all together.