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Deep Learning for Robot Locomotion

We explore ways in which deep reinforcement learning can be used to help underactuated and unintuitive robots learn locomotion tasks, ranging from gait discovery to path planning.

Geometric Mechanics of Robot Locomotion

We explore ways in which ideas and techniques from geometric mechanics can be applied to underactuated and dynamic robot systems with an eye toward locomotion.

teaching

COMS 4701, Artificial Intelligence

Fall 2019, Fall 2018

Artificial Intelligence (AI) is a broad and fast-growing subfield of Computer Science concerned with the construction and deployment of intelligent agents. This course provides an overview of methods, history, and impact of AI. It covers heuristic search, game playing, reasoning under uncertainty, reinforcement learning, Bayesian networks, Markov models, machine learning, and applications (natural language processing, vision, robotics, as time permits). Students will solve a variety of AI problems using Python.

COMS 4733, Computational Aspects of Robotics

Spring 2019

This course is an introduction to robotics from a computer scientist’s perspective. While robotics is inherently broad and interdisciplinary, we will primarily focus on ideas with roots in computer science, as well as the roles that a computer scientist would play in a robotics research or engineering task. At the same time, students will learn the necessary tools for communication and cooperation with other engineers in order to develop a working robot system, including the ability to read and implement ideas from robotics research papers.

COMS 3203, Discrete Mathematics

Spring 2019

The study of discrete mathematics provides an important foundation for basic theoretical principles in computer science. This course starts by building a strong background in logic and formal proofs, particularly those constructed by induction. After developing the ability to write coherent, rigorous proofs, we will study topics relating to functions, number theory, group theory, counting, and graph theory. Applications in cryptography, error correcting codes, and other areas of computer science will be considered as time permits.

16-384, Robot Kinematics and Dynamics

Spring 2013 - Spring 2018 (Carnegie Mellon)

Although I was never a formal instructor for this course, I initially was a graduate student TA and then worked closely with my advisor in subsequent semesters to implement an online curriculum for present offerings of the course.