Learning Robot Behaviours

Demonstration of a robot learning the correct tool to use to solve a particular task.

Demonstration of a robot learning to climb a low-step, a high-step and staircase.

An ongoing focus of our group is developing machine learning techniques for robotics. Manually programming new robot behaviours is very challenging. A robot that can learn its own behaviours can better and more quickly adapt to new situations and tasks, without relying on domain experts and programmers. This learning is very beneficial for robots for home environments, as end users could instruct their robots to learn new skills, rather than rely and wait on teams of experts to design and implement new software.

We have developed several robot learning systems using learning from demonstration, explanation-based learning and reinforcement learning. Often these are combined, for example, using observations of another agent to create an abstract description of a behaviour and then using that to guide trial-and-error learning to refine the behaviour. This hybrid paradigm has been applied to locomotion for bipedal robots and rescue robots. It has also been used to learn how to use simple objects as tools, for example, learning that a hook shaped object can be used to retrieve another object that is out of reach of the robot’s hand.

Publications include:

  • Wiley, T., Bratko, I., and Sammut, C.  A machine learning system for controlling a rescue robot. In Akiyama, H., Obst, O., Sammut, C., and Tonidandel, F., editors, RoboCup 2017: Robot World Cup XXI, pp 108–119, Cham. Springer International Publishing. 2018.
  • Muggleton, S., Dai, W.-Z., Sammut, C., Tamaddoni-Nezhad, A., Wen, J., and Zhou, Z.-H. Meta-interpretive learning from noisy images. Machine Learning, 107(7):1097 – 1118. 2018.
  • Wicaksono, H. and Sammut, C. Tool use learning for a real robot. International Journal of Electrical and Computer Engineering (IJECE), 8(2):1230–1237. 2018.
  • T. Wiley, C. Sammut, B. Hengst, and I. Bratko, A Planning and Learning Hierarchy using Qualitative Reasoning for the On-Line Acquisition of Robotic Behaviors. Advances in Cognitive Systems. 4, pp. 93-112, 2016.
  • T. Wiley, C. Sammut, and I. Bratko. Qualitative Planning with Quantitative Constraints for Online Learning of Robotic Behaviours. Proceedings of the 28th AAAI Conference on Artificial Intelligence. Quebec City, Canada, pp. 2578-2584, 2014.
  • S. Brown, and C. Sammut, A Relational Approach to Tool-use Learning in Robots. Inductive Logic Programming, pp. 1–15. Springer Berlin Heidelberg, 2013.
  • C. Sammut, R. K.-M. Sheh, A. Haber, and H. Wicaksono, The Robot Engineer, Proceedings of the 25th International Conference on Inductive Logic Programming, 2015.
  • R. K.-M. Sheh, B. Hengst, and C. Sammut, Behavioural Cloning for Driving Robots over Rough Terrain, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 732–737, 2011.
  • S. Brown and C. Sammut, Learning tool use in robots, Advances in Cognitive Systems: Papers from the AAAI Fall Symposium, Menlo Park, CA, USA, pp. 58–65, 2011.