Combining epistemic planning and belief-based programming for unified task and motion planning

To combine state-of-the-art epistemic planning with belief-based stochasic programming techniques for unified task and motion planning
Description of the Project: 

This project will combine advances from epistemic planning (embodied in planners such as PKS) with high-level programming constructs enabling the representation of beliefs and stochastic information (such as the ALLEGRO language) for the purpose of unified task and motion planning. A new planning framework will be built, supporting the generation of robot plans that involve bridging the gap between reasoning about high-level task actions and low-level robot motion control. Plans will be built for dynamic real-world environments, such as those involving collaborative human-robot interaction or complex object interactions. Appropriate interfaces to these tools will be developed in ROS to integrate them with existing robot simulators and ecosystems.

Resources required: 
High-specification computing environment for planning in simulated environments
Project number: 
123019
First Supervisor: 
University: 
Heriot-Watt University
Second Supervisor(s): 
First supervisor university: 
Heriot-Watt University
Essential skills and knowledge: 
Strong programming skills
Desirable skills and knowledge: 
Knowledge of recent planning systems, and belief-based programming, familiarity with ROS
References: 

A. Gaschler, R. Petrick. O. Khattib, and A. Knoll (2018). KABouM: Knowledge-Level Action and Boundary Geometry Motion Planner, JAIR 61:323-362.

V. Belle and H.J. Levesque (2015). ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains, IJCAI.