CS Events

Qualifying Exam

Improving the Efficiency of Kinodynamic Planning with Machine Learning


Download as iCal file

Monday, October 18, 2021, 12:30pm - 02:00pm


Speaker: Aravind Sivaramakrishnan

Location : 1 Spring Street, Room SPR-319 & via Zoom


Prof. Kostas Bekris (Chair)

Prof. Abdeslam Boularias

Prof. Jingjin Yu

Prof. Amélie Marian

Event Type: Qualifying Exam

Abstract: Kinodynamic motion planning is characterized by the lack of a steering function (i.e., a local planner) for the underlying robotic system. This talk will first survey efforts to improve the efficiency of state-of-the-art, sampling-based kinodynamic planners through data-driven methods. It will then present a proposed direction for improving the path quality and computational efficiency of such planners when applied to vehicular navigation. Given a black-box dynamics model for the vehicle, a reinforcement learning process is trained offline to return a low-cost control that reaches a local goal state (i.e., a waypoint) in the absence of obstacles. By focusing on the system's dynamics and not knowing the environment, this process is data-efficient and takes place once for a robotic system. In this way, it can be reused in different environments. Then, the proposed sampling-based planner generates online local goal states for the learned controller in an informed manner to bias towards finding a high-quality solution trajectory fast. The planner also maintains an exploratory behavior for cases where the guidance from the machine learning process is not effective. The results show that the proposed integration of learning and planning can produce higher quality paths than standard, sampling-based kinodynamic planning with random controls in fewer iterations and computation time.


Meeting URL: https://rutgers.zoom.us/j/92043377290?pwd=bVl2Z05DQkJuYzZ0Ty81VnhUdmI0QT09&from=addon

Meeting ID: 920 4337 7290
Password: 446167