Movement primitives (MPs) are compact representations of robot skills that can be learned from demonstrations and combined into complex behaviors. However, merely equipping robots with a fixed set of innate MPs is insufficient to deploy them in dynamic and unpredictable environments. Instead, the full potential of MPs remains to be attained via adaptable, large-scale MP libraries. In this paper, we propose a set of seven fundamental operations to incrementally learn, improve, and re-organize MP libraries. To showcase their applicability, we provide explicit formulations of the five spatial operations for libraries composed of Via-Point Movement Primitives (VMPs). By building on Riemannian manifold theory, our approach enables the incremental learning of all parameters of position and orientation VMPs within a library. Moreover, our approach stores a fixed number of parameters, thus complying with the essential principles of incremental learning. We evaluate our approach to incrementally learn a VMP library from sequentially-provided motion capture data.

Learning task models of bimanual manipulation from human demonstration and their execution on a robot should take temporal constraints between actions into account. This includes constraints on (i) the symbolic level such as precedence relations or temporal overlap in the execution, and (ii) the subsymbolic level such as the duration of different actions, or their starting and end points in time. Such temporal constraints are crucial for temporal planning, reasoning, and the exact timing for the execution of bimanual actions on a bimanual robot. In our previous work, we addressed the learning of temporal task constraints on the symbolic level and demonstrated how a robot can leverage this knowledge to respond to failures during execution. In this work, we propose a novel model-driven approach for the combined learning of symbolic and subsymbolic temporal task constraints from multiple bimanual human demonstrations. Our main contributions are a subsymbolic foundation of a temporal task model that describes temporal nexuses of actions in the task based on distributions of temporal differences between semantic action keypoints, as well as a method based on fuzzy logic to derive symbolic temporal task constraints from this representation. This complements our previous work on learning comprehensive temporal task models by integrating symbolic and subsymbolic information based on a subsymbolic foundation, while still maintaining the symbolic expressiveness of our previous approach. We compare our proposed approach with our previous pure-symbolic approach and show that we can reproduce and even outperform it. Additionally, we show how the subsymbolic temporal task constraints can synchronize otherwise unimanual movement primitives for bimanual behavior on a humanoid robot.

Learning temporal relations between actions in a bimanual manipulation task is important for capturing the constraints of actions required to achieve the task's goal. However, given several demonstrations of a bimanual manipulation task, the problem of identifying the true temporal dependencies between actions - if there are any - is very challenging due to contradictions. We propose a model-driven approach for learning temporal task models from multiple bimanual human demonstrations that represents temporal relations on two levels. First, temporal relations between sets of actions that exhibit a tight temporal coupling, and second, temporal relations between these sets of actions. We build on Allen's interval algebra as a representation to express relations between temporal intervals. Semantically defining these interval relations allows us to soften their formulation to deal with inaccuracies in real data obtained when observing humans demonstrating the task. Our temporal task models can be learned incrementally from multiple modalities, and allow us to reason about viable alternatives during task execution in case of unexpected events. We evaluated the approach quantitatively on two datasets and qualitatively on a humanoid robot. The evaluation shows how inherent properties of bimanual human manipulation tasks can be exploited to derive a model useful for the reproduction by humanoid robots.

We introduce a multi-functional robotic gripper equipped with a set of actions required for disassembly of electromechanical devices. The gripper consists of a robot arm with 5 degrees of freedom (DoF) for manipulation and a jaw gripper with a 1-DoF rotation joint and a 1-DoF closing joint. The system enables manipulation in 7 DoF and offers the ability to reposition objects in hand and to perform tasks that usually require bimanual systems. The sensor system of the gripper includes relative and absolute joint encoders, force and pressure sensors to provide feedback about interaction forces, a tool- mounted camera for screw detection and precise placement of the tool tip using image-based visual servoing. We present a data-driven method for estimating joint torques based on the output voltage and motor speed. Further, we provide methods for teaching disassembly actions based on human demonstration, their representation as movement primitives and execution based on sensory feedback. We provide quantitative results regarding positioning and torque estimation accuracy, disassembly success rate and qualitative results regarding the successful disassembly of hard disc drives.