These working notes by Russ Tedrake cover nonlinear dynamics and control with a specific focus on mechanical systems. The material explores how to achieve robust, efficient, and graceful robot movement through the integration of mechanical design, passive dynamics, and nonlinear control synthesis. Rather than relying solely on model-free approaches, the text emphasizes using the underlying structure of dynamical equations to develop more data-efficient and robust algorithms via optimization and machine learning.
Main topics include:
* Model systems such as pendulums, acrobots, cart-poles, and quadrotors
* Simple models of walking and running dynamics
* Nonlinear planning and control using trajectory optimization and LQR
* Lyapunov analysis for stability and reachability
* Estimation techniques including Kalman filters and Bayesian methods
* Learning-based approaches such as imitation learning, policy search, and system identification
* Contact-implicit trajectory optimization and hybrid systems
A paraphrasing of Gerald Jay Sussman's explanation for MIT's switch from Scheme to Python in its undergraduate computer science program, focusing on the changing nature of programming and the need to adapt to modern systems and libraries.
A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.
MIT CSAIL researchers have developed three neurosymbolic frameworks - LILO, Ada, and LGA - that use natural language to help large language models (LLMs) build better abstractions for coding, AI planning, and robotics tasks.