Efficient Learning from Demonstration for adaptation of behaviour based on force feedback

Today, robots are used in various aspects of our lives, and there are many situations where humans and robots collaborate or work closely together. However, there are still many challenges to be overcome before robots can be used in a wider range of fields. Robots that perform everyday tasks, such as cooking, or that work with humans in rehabilitation or assisted tasks, still have problems with the effort required for learning, adaptability and safe operation around humans. Therefore, my project aims to develop a method for robots to efficiently learn motor skills involving force interaction from human demonstrations and to adapt their behaviour using force feedback from the objects they manipulate or the human partner they interact with. It also aims to develop a framework to enable the robot to explain its behaviour, failures and understanding of the task and environment so that it can operate safely around humans. From a practical point of view, efficient learning of tasks and improved adaptability and explainability will contribute to the wider use of collaborative robots, especially in areas where physical human-robot interaction is required, such as manufacturing, hospitality, nursing care and welfare.



To achieve this goal, I will explore how robots can learn to obtain information about the objects they manipulate or the human partner they interact with and use it to adapt their behaviour according to the properties of the objects such as hardness and adhesiveness, and human capabilities such as joint flexibility and muscle strength, through Learning from Demonstrations. Learning from Demonstration is a fast, intuitive, and flexible way of transferring motor skills from humans to robots, however, teaching how to interact with the world and use force feedback to adapt behaviour can take a lot of time and effort for human demonstrators. Therefore, in my project, I will also develop an algorithm that allows efficient learning of motor skills involving force interaction by taking two approaches: improving the way of giving demonstrations and allowing to learn tasks in a way that is transferable across different tasks, objects, humans and tools so that they do not have to re-learn for each task, object, person or tool. Finally, since safety is extremely important, especially for robots working closely or together with humans, I will explore how robots can compute the uncertainty in their actions and their causes and explain the reasons for their actions and failures.



In my first year, I will explore efficient ways of teaching robots to identify object properties using force interactions and verify the methods for adapting their behaviour based on identified object properties in daily manipulation tasks such as cutting and stirring. In my second year, I will investigate how robots can learn motor skills in one task and apply them to another task without re-learning. I will also work on developing a framework for robots to compute uncertainties in their behaviour and their causes and to explain the reasons for their actions and failures. In my third year, I will explore how these methods and frameworks can be applied to modelling of the physical characteristics and capabilities of individual human partners to enable smooth and safe human-robot collaboration.