Location: RI, CMU

Developed a combined learning and search based planning method that reduces planning time by using prior experiences learnt through CVAEs. The planner used (MR-MHA*) exploits loose coupling between high dimensional state representations such as between the base and arm of a mobile manipulator and CVAEs are used to better exploit this loose coupling based on prior experiences.

Links : Report, Presentation / Videos : Clip 1 / Skills : Python, C++, ROS, Planning