Semantic Mates: Intuitive Geometric Constraints for Efficient Assembly Specifications In this paper, we enhance our knowledgebased and constraintbased approach of robot programming with the concept of Semantic Mates. They describe intended mechanical connections between parts of an assembly. This allows deriving appropriate assembly poses from the type of connection and the geometric properties of the involved parts. The paper presents an ontologybased representation of Semantic Mates that is used to augment object models with additional information regarding their potential use in an assembly. Such semantically annotated object models can be used in our instruction framework to program a robot to perform assembly tasks through simple draganddrop operations in a graphical user interface.We conducted a user study with 21 participants in order to evaluate the efficiency and usability of the Semantic Mates concept based on a usecase from the domain of mechanical assembly. Across different experience levels in robotics, the participants achieved a significantly faster workflow and improved perceived usability compared to the manual specification of constraintbased assembly operations.

Robust, Compliant Assembly with Elastic Parts and Model Uncertainty In this paper, we present an approach to generate robot motions for robust parts assembly. The computation of motions for parts assembly usually requires an exact model of all relevant objects. Generating detailed object models, including friction and dynamics, is often complex and timeconsuming, especially in the context of elastic parts. In addition, executing motions on real hardware will usually introduce further uncertainty. For this reason, we propose an approach that is inherently robust against model parameter uncertainties and unknown characteristics of elastic parts. Our planner explicitly takes into account the internal states of articulated objects, as well as uncertain model parameters, by constructing a search tree in the beliefparameterspace. It yields successful assembly motions from coarse object models and thus eliminates the need for detailed parameter tuning. We evaluated our approach with respect to four assembly tasks. Extensive simulations show that our planner significantly increases the successrate compared to previous approaches. Numerous experiments on a real robot confirm the simulated results.