Project Info

Robot Planning

Neil Dantam

Project Goals and Description:

Robots require novel reasoning systems to achieve complex objectives in new environments. Everyday activities in the physical world couple discrete and continuous reasoning. For example, setting a dinner table requires a robot to make discrete decisions about which objects to pick and the order in which to do so, and execute these decisions by computing continuous motions to reach objects or desired locations. Robotics has traditionally treated these issues in isolation. Reasoning about discrete events is referred to as task planning while reasoning about and computing continuous motions is the realm of motion planning. However, several recent works have shown that separating task planning from motion planning---that is finding first a series of actions that will later be executed through continuous motion---is problematic; for example, the next discrete action may specify picking an object, but there may be no continuous motion for the robot to bring its hand to a configuration that can actually grasp the object to pick it up. Instead, Task-Motion Planning (TMP) tightly couples task planning and motion planning, producing a sequence of steps that can actually be executed by a real robot to bring the world from an initial to a final state. We will investigate various approach and algorithms for robots to perform this type of real-world planning.

More Information:

Grand Challenge: Not applicable.
The Open Motion Planning Library ( The Task-Motion Kit (

Primary Contacts:

Neil Dantam,

Student Preparation


Student must be familiar with programming and basic data structures. Prior experience with calculus and linear algebra. Necessary Courses: CSCI-262/220. Desired Courses: CSCI 400, 406




Student will develop an understanding of robot programming, task planning, and robot motion planning techniques and libraries. Student will learn various methods and algorithms for search and optimization in complex spaces.


Weekly individual meetings with the student and faculty mentor to discuss progress and provide directed guidance. Weekly lab meetings to discuss the overall project and integration. Assigned graduate student mentor for the student.


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