Dartmouth Events

Abstract: Intelligent field robots are a promising solution to many societal challenges from combating epidemics, to scaling global supply chains, to providing home health care to the elderly. However, today, robots are mostly limited to laboratory settings as the computational intensity of many robotics algorithms prevents their real-time use on edge robotic hardware. In this talk, I will discuss how I am addressing these challenges through algorithm-hardware-software co-design, generating new algorithms and implementations that can run at real-time rates on the edge. Specifically, I will show how the performance of nonlinear model predictive control (MPC) algorithms can be significantly enhanced through a combination of parallelism, approximation, numerical conditioning, and structure exploitation. Through the resulting theoretical and computational advancements, my work has enabled GPU-accelerated whole-body nonlinear MPC for manipulators at kHz rates over long-horizon trajectories, as well as real-time dynamic obstacle avoidance for microcontroller-powered tiny quadrotors. This work sets the stage for a future filled with dynamic, adaptable, and useful robotic systems.

Bio: Brian Plancher is an Assistant Professor of Computer Science at Barnard College, Columbia University where he leads the Accessible and Accelerated Robotics Lab. He holds affiliate positions in the Department of Computer Science and Electrical Engineering at the Fu Foundation School of Engineering and Applied Science, Columbia University. He is also a co-chair for the Tiny Machine Learning Open Education Initiative (TinyMLedu) and an associate co-chair for the IEEE-RAS TC on Model Based Optimization for Robotics. His research is focused on optimizing robotic systems at all scales by developing, optimizing, implementing, and evaluating next-generation algorithms and edge computational systems through algorithm-hardware-software co-design. As such, his research is at the intersection of Robotics and Computer Architecture, Embedded Systems, Numerical Optimization, and Machine Learning. He also wants to promote a responsible, sustainable, and accessible future for robotics and edge computing through his research, teaching, and service, including the development of new interdisciplinary, project-based, open-access courses that lower the barriers to entry for cutting-edge topics like robotics, parallel programming, and embedded machine learning.