Marco Pavone - Stanford University
Marco Pavone. Associate Professor. Stanford University. Department of Aeronautics and Astronautics. Departments of Electrical Engineering (by courtesy) and of Computer Science (by courtesy) Director, Autonomous Systems Laboratory.
Brief Bio - Stanford University
Dec 11, 2020 · Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he is the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford.
Principles of Robot Autonomy II - Stanford University
This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans.
In this paper we introduce Monte Carlo Motion Planning, a novel approach to planning under uncertainty that is accurate, fast, and general. Related work: Conceptually, to enable a robot to plan its motion under uncer-tainty, one needs to design a strategy for a decision maker.
Course structure. Three modules, roughly of equal length. learning-based control and perception. interaction with the physical environment. interaction with humans. Requirements. AA 174A / AA 274A / CS 237A / EE 260A.
Marco Pavone Submitted to the Department of Aeronautics and Astronautics on May 26, 2010, in partial ful llment of the requirements for the degree of Doctor of Philosophy Abstract Recent years have witnessed great advancements in the sciences and technology of autonomy, robotics and networking. This dissertation develops concepts and algorithms ...
Marco Pavone 19.1Introduction 19.1.1Overview This chapter discusses the operational and economic aspects of autonomous mobility-on-demand (AMoD) systems, a transformative and rapidly developing mode of transportation wherein robotic, self-driving vehicles transport passengers in a given environment.
Robust Adaptive Model Predictive Control with Matched and Unmatched Uncertainty. Rohan Sinha, James Harrison, Spencer M. Richards, Marco Pavone. Under review at Conference on Decision and Control (CDC), 2021. arXiv: 2104.08261 Extended version under review at Transactions on Automatic Control (TAC). 14.
Students will learn the theoretical foundations for these concepts. Course Goals: With this course, students will obtain a fundamental understanding of advanced principles of robot autonomy, including robot learning, physical interaction with the environment, and interaction with humans.
Lectures: Monday and Wednesday, 1:30pm - 2:50pm. Information about office hours available in the Syllabus: http://web.stanford.edu/class/cs237b/pdfs/syllabus.pdf. Course websites: https://cs237b.stanford.edu (course content and announcements)