Learning from Demonstration (LfD) is a widely used approach for teaching robot motion, but more sophisticated strategies are required to address complex tasks such as surface processing. Sanding is an example where comprehensive strategies are necessary to ensure complete and efficient coverage of the surface of a workpiece. In this paper, we present a system that captures human motions and contact forces during surface processing using a powered sanding tool. We provide a publicly available dataset that consists of demonstrations for various geometric shapes with the goal to extract robot execution strategies through LfD from a variety of users. This is in contrast to conventional LfD, which generates a policy directly from one or multiple trajectories provided by a single user. Further, we provide a data analysis that reveals key insights into how humans adapt their strategies to different surface geometries and extract robot execution strategies from it. Finally, we conduct two basic robotic experiments justifying the approach of strategy extraction. Our findings contribute to the understanding of human surface-processing behavior and lay the foundation for developing more effective robotic surface processing strategies.

Technology has sufficiently matured to enable, in principle, flexible and autonomous robotic assembly systems. However, in practice, it requires making all the relevant (implicit) knowledge that system engineers and workers have – about products to be assembled, tasks to be performed, as well as robots and their skills – available to the system explicitly. Only then can the planning and execution components of a robotic assembly pipeline communicate with each other in the same language and solve tasks autonomously without human intervention. This is why we have developed the Factory of the Future (FoF) ontology. At its core, this ontology models the tasks that are necessary to assemble a product and the robotic skills that can be employed to complete said tasks. The FoF ontology is based on existing standards. We started with theoretical considerations and iteratively adapted it based on practical experience gained from incorporating more and more components required for automated planning and assembly. Furthermore, we propose tools to extend the ontology for specific scenarios with knowledge about parts, robots, tools, and skills from various sources. The resulting scenario ontology serves us as world model for the robotic systems and other components of the assembly process. A central runtime interface to this world model provides fast and easy access to the knowledge during execution. In this work, we also show the integration of a graphical user front-end, an assembly planner, a workspace reconfigurator, and more components of the assembly pipeline that all communicate with the help of the FoF ontology. Overall, our integration of the FoF ontology with the other components of a robotic assembly pipeline shows that using an ontology is a practical method to establish a common language and understanding between the involved components.

Conventional robot programming methods are not suited for non-experts to intuitively teach robots new tasks. For this reason, the potential of collaborative robots for production cannot yet be fully exploited. In this work, we propose an active learning framework, in which the robot and the user collaborate to incrementally program a complex task. Starting with a basic model, the robot's task knowledge can be extended over time if new situations require additional skills. An on-line anomaly detection algorithm therefore automatically identifies new situations during task execution by monitoring the deviation between measured- and commanded sensor values. The robot then triggers a teaching phase, in which the user decides to either refine an existing skill or demonstrate a new skill. The different skills of a task are encoded in separate probabilistic models and structured in a high-level graph, guaranteeing robust execution and successful transition between skills. In the experiments, our approach is compared to two state-of-the-art Programming by Demonstration frameworks on a real system. Increased intuitiveness and task performance of the method can be shown, allowing shop-floor workers to program industrial tasks with our framework.

Learning from Demonstrations is a promising technique to transfer task knowledge from a user to a robot. We propose a framework for task programming by observing the human hand pose and object locations solely with a depth camera. By extracting skills from the demonstrations, we are able to represent what the robot has learned, generalize to unseen object locations and optimize the robotic execution instead of replaying a non-optimal behavior. A two-staged segmentation algorithm that employs skill template matching via Hidden Markov Models has been developed to extract motion primitives from the demonstration and gives them semantic meanings. In this way, the transfer of task knowledge has been improved from a simple replay of the demonstration towards a semantically annotated, optimized and generalized execution. We evaluated the extraction of a set of skills in simulation and prove that the task execution can be optimized by such means.

The recent generation of compliant robots enables kinesthetic teaching of novel skills by human demonstration. This enables strategies to transfer tasks to the robot in a more intuitive way than conventional programming interfaces. Programming physical interactions can be achieved by manually guiding the robot to learn the behavior from the motion and force data. To let the robot react to changes in the environment, force sensing can be used to identify constraints and act accordingly. While autonomous exploration strategies in the whole workspace are time consuming, we propose a way to learn these schemes from human demonstrations in an object targeted manner. The presented teaching strategy and the learning framework allow to generate adaptive robot behaviors relying on the robot's sense of touch in a systematically changing environment. A generated behavior consists of a hierarchical representation of skills, where haptic exploration skills are used to touch the environment with the end effector, and relative manipulation skills, which are parameterized according to previous exploration events. The effectiveness of the approach has been proven in a manipulation task, where the adaptive task structure is able to generalize to unseen object locations. The robot autonomously manipulates objects without relying on visual feedback.