Learning Task Parameterised Dynamical Systems with Gaussian Process Regression and Classification

Recent research has shown that a Learning from Demonstration (LfD) approach is useful for teaching robots flexible skills efficiently, and it opens the possibility for non-expert users to program these skills. When learning from demonstration data, learning frameworks should learn representations that are flexible and can generalize to unseen...

Learning from Few Demonstrations with Frame-Weighted Motion Generation

Learning from Demonstration (LfD) aims to learn versatile skills from human demonstrations. The field has been gaining popularity since it facilitates transferring knowledge to robots without requiring much expert knowledge. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this,...

Incremental Adaptation of Behaviour Trees for Applications in Learning from Demonstration Frameworks

This thesis proposes the novel Behaviour Tree Update Framework (BTUF) for the initial construction and continuous incremental adaptation of Behaviour Trees (BTs) for applications in Learning from Demonstration (LfD) frameworks to create complex robot behaviours associated with Activities of Daily Living (ADL) without requiring the user to have a...

Teaching Robots Impact Tasks by Performing Demonstrations

While robots execute many tasks where physical interaction with the environment is required, it is still challenging to control a robot that deliberately makes contact at a non-zero velocity, especially with multiple contact points that are impacted simultaneously.<br/>When there is a mismatch between planned and actual impact time, the robot...

Teleoperated online Learning from Demonstration in a partly unknown environment: using a semi-autonomous care robot

The general approach to generate collision free motion in a constraint environment is to use path planners, which demand a known environment and potentially fail otherwise. Learning from Demonstration (LfD) can be used instead to teach the robot unknown parts of the environment, such as a goal deviation or an unforeseen obstacle. The general...