Fast paced industry requirements call for fast and easy robot programming, especially for Small and Medium sized Enterprises (SME) that often lack robot programming experience. Even with the advancement of graphical activity representation languages such as Behaviour Trees (BTs), it can still be time consuming to program robots for new behaviors due to the shifting product specifications and the dynamic production environments. This paper presents an extension of BTs that offers more flexibility as well as higher reactivity and robustness by introducing Mixed Initiative Planning (MIP) to BTs using Dynamic Sequence Nodes (DSNs). DSNs reduce the human effort needed to design a BT as well as the number of nodes to achieve a certain task while maintaining robustness, readability, and modularity of the tree. Additionally, it introduces run-time optimization to BTs, as opposed to tree synthesis approaches that guarantee convergence but overlook performance.

Continual learning (CL) is a machine learning (ML) paradigm for learning continually from non-stationary data streams while simultaneously transferring and protecting past knowledge. Therefore, CL avoids catastrophic forgetting, a common problem that arises when training ML-models on new data. This paper presents a CL framework for data-driven learning of the dynamics model of a 6-degree-of-freedom serial industrial robot. This model can be used for model-based control algorithms, without the need for extensive identification of robot specific parameters such as mass inertia, and can additionally model complex effects such as friction. Furthermore, using CL, it can adapt to changes of the robot e.g., due to wear or new tasks. With the help of CL, the ML-based dynamics model is continually fed new data and improves over the operating period of the robot.