The Leeds Autonomous Service Robots (LASR) team is a newly established team to compete in service robot challenges. LASR is the incarnation of the Sensible Robots research, and at the same time gives our undergraduate and master’s students a chance to do real-world robot programming.
We run a “robot club” in the School of Computing once a week, contact Matteo if you are a student at Leeds and would be interested in participating.
Dr Matteo Leonetti – Lecturer, team leader.
Logan Dunbar – PhD student
Jit Hong Cheah – 3rd year UG student, Laidlaw scholar
Mohammed Alshammasi – 3rd year UG student
Joe Jeffcock – 2nd year UG student
Georgy Gunkin – 2nd year UG student
The focus of our research is on adaptive decision making, to build systems that can plan quickly at different levels, improve over time, and constantly adapt to the people and the environment. Previous work (partly carried out by Matteo at the University of Texas at Austin, with Prof. Peter Stone and his group), focuses on planning and learning algorithms for both symbolic and motion planning (see the research page, especially the sections on service robots, and high-level decision making). In the near future, we intend to apply the same vision to human-robot interaction, merging an online and continuous learning component into planning for HRI.
Within his work at UT Austin, Matteo developed the actasp library for integrated reinforcement learning, reasoning, planning, and execution monitoring in Answer Set Programming (as part of this work). The library enables both the bwi_kr_execution and plan_execution packages in the BWI system, made publicly available and maintained by the group at UT. The code is platform independent and can be used on any robot, through implementing appropriate actions.
As we develop new packages the will be added here. We intent to use a modular architecture, isolating references to Pal Robotics’ packages, so as to allow our software to be used on different platforms (just like the actasp library mentioned above).
Our winning team!