Optimizing Robotic Systems at All Scales
Event details
| Date | 02.12.2025 |
| Hour | 14:00 › 15:00 |
| Speaker | Assistant Professor Brian K. Plancher, Computer Science at Dartmouth College, USA. |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
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 my lab is 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, our 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 Dartmouth College where he leads the Accessible and Accelerated Robotics Lab (A²R Lab). He was previously an Assistant Professor of Computer Science at Barnard College where he also held affiliate positions in Computer Science and Electrical Engineering at Columbia University. He received his A.B., M.Eng., and Ph.D. from Harvard University. Brian 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.
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 my lab is 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, our 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 Dartmouth College where he leads the Accessible and Accelerated Robotics Lab (A²R Lab). He was previously an Assistant Professor of Computer Science at Barnard College where he also held affiliate positions in Computer Science and Electrical Engineering at Columbia University. He received his A.B., M.Eng., and Ph.D. from Harvard University. Brian 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.
Practical information
- General public
- Free
Organizer
- Prof. Colin Jones