When robots are used to automate repetitive production tasks, the productivity of the manufacturing system crucially depends on the robot's task execution speed. An out-of-the-box solution is typically slow, whereas achieving shorter cycle times typically requires large efforts with respect to controller design and tuning. This dilemma can be resolved by learning control algorithms that autonomously improve performance without requiring any system-specific tuning. In the present work, we propose a novel learning control scheme that autonomously reduces the execution times of robotic systems that perform repetitive manufacturing tasks. To this end, we combine an Iterative Learning Control (ILC) approach with a trial-varying reference adaptation. The reference trajectory is slowly adapted to ensure that the given task is performed successfully on every single iteration without constraint violations. Therefore, the learning process can be carried out during operation. We validate the practical applicability of the method by real-world experiments on a 6-axis robot that performs a linear motion and a contact-force task. Despite the fundamentally different characteristics of these two tasks, the proposed algorithm achieves a remarkable reduction of cycle times, namely, by a factor of 4 in the linear motion task and a factor of 10 in the contact-force task. These results provide an important step toward robotic manufacturing systems that autonomously optimize their own performance during operation.

Programming-by-Demonstration (PbD) or Imitation Learning (IL) provides a powerful approach to program robots intuitively. In industrial settings, these approaches are not commonly deployed since several factors negatively impact their productive use. The most common reasons are safety concerns on various levels. First, industrial robots are only allowed to be operated in direct contact if the operator has sufficient experience. Secondly, many PbD systems do not incorporate force measurements in their model directly. This renders them ineffective for assembly tasks, such as snap- fit connections. In this paper we (a) present an approach to incorporate force measurements into a generative model (b) using only external sensors without relying on measurements from the robot during demonstrating to decouple the teaching process from the robot and (c) show the benefit of explicit force measurement and modeling on a Franka Emika Panda robot by the example of assembling terminal clamps on a DIN rail.

Recent intuitive robot programming approaches operate on task level, enabling programmers to intuitively arrange or compose encapsulating robot capabilities (skills). This paper presents an approach to intuitively create (sub-)skills. General guidelines for assembly process descriptions #2860 provided by the Association of German Engineers VDI are applied to robot programming, in particular addressing assembly applications. The guidelines are exemplarily applied to the constraint-based approach iTaSC (instantaneous Task Specification using Constraints), presenting a procedure to hierarchically combine elementary processes to (sub-)skills. Six elementary processes are identified to be sufficient to implement a wide variety of assembly tasks. An iTaSC implementation was developed and two exemplarily assembly tasks were realized to evaluate the approach.

In recent years, a number of publications described approaches for model-based manipulation skills and their applicability to a variety of robot tasks-be it assembly, industrial robotics in general, or service robotics. These approaches roughly follow the same pattern: They model robot task description based on the Task Frame Formalism, the Task Function Approach, or iTaSC. They model coordination mechanisms in form of statecharts or Petri nets. And almost all models are accompanied by domain-specific languages (DSLs) that facilitate creating applications based on those models. While one advantage of using models is their reusability across applications, how to explicitly model the reuse itself has not been fully addressed by these publications. Our paper contributes to this field of research by investigating how reuse can be explicitly modeled using prototype-based inheritance. We base our model on iTaSC and provide a simple yet effective DSL for populating the model and creating applications. We demonstrate our approach by creating a comprehensive library of skills, and by showing the use, reuse and incremental refinement of skills for diverse industrial assembly applications.

High motion performance, stiffness, and accuracy are crucial for industrial robot applications, but these requirements are in practice contradictory. Using a novel type of robot, the so called Gantry Tau, new combinations of stiffness and accuracy are in principle possible, except for the backlash in the drive-trains of each joint. Existing techniques for backlash reduction are either mechanically complex or limit the mechanical bandwidth. This paper presents an approach based on dual motors connected in parallel to the load, such that the entire robot structure can be made practically backlash free by means of software and feedback control. Different control strategies are presented and evaluated in experiments ranging from a lab servo process via a table-sized robot to a large industrial implementation with several meters of working range. Special emphasis was on a dual motor test rig with a linear high-resolution scale (not yet used for feedback) where the combined motor torque was fully utilized for high acceleration, while reducing backlash by over 96%.