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.

Quickly Inserting Pegs into Uncertain Holes using Multiview Images and Deep Network Trained on Synthetic Data This paper explores the use of robots to autonomously assemble parts with variations in colors and textures. Specifically, we focus on peginhole assembly with some initial position uncertainty and holes located on surfaces of different colors and textures. Two inhand cameras and a forcetorque sensor are used to account for the position uncertainty. A program sequence comprising learningbased visual servoing, spiral search, and impedance control is implemented to perform the peginhole task with feedback from the above sensors. Contributions are mainly made in the learningbased visual servoing component of the sequence, where a deep neural network is trained with various sets of synthetic data generated using the concept of domain randomization to predict where a hole is. In the experiments and analysis section, the network is analyzed and compared, and a realworld robotic system to insert pegs to holes using the proposed method is implemented. The results show that the implemented peginhole assembly system can perform successful peginhole insertions on surfaces with various colors and textures. It can generally speed up the entire peginhole process, especially when the initial position uncertainty is large.