VisionBased Kinematic Configuration Recognition for Reconfigurable Modular Robots Reconfigurable modular robot systems are advantageous for their scalability and versatility. However, creating kinematic models for every new assembly is a timeconsuming and errorprone task. In this paper, a visionbased kinematic configuration recognition method is proposed. The modular graph is firstly extracted by recognition of Quick Response (QR) Codes placed on each module. String information, obtained by searching kinematic chains on the modular graph, is then compared with that in a preset database, to figure out the current configuration. Experiments are conducted to verify the effectiveness of the proposed method, and to test the accuracy of the recognition. Results show that the intersection angle between the camera and the QR codes should be less than 45 degrees, to achieve the best recognition effect.

Automatic Configuration of the Structure and Parameterization of Perception Pipelines The configuration of perception pipelines is a complex procedure that requires substantial amounts of engineering effort and knowledge. A pipeline consists of interconnected individual perception operators and their parameters, which leads to a large configuration space of pipeline structures and parameterizations. This configuration space has to be explored efficiently in order to find a solution that fulfills the specific requirements of the target application. In this paper, we present an approach to perform automatic configuration based on structure templates and sequential modelbased optimization. The structure templates allow to reduce the search space and encode prior engineering knowledge. We introduce a structure template based on hypothesis generation, hypothesis refinement, and hypothesis testing to demonstrate the effectiveness of the approach. Experimental evaluation with stateoftheart operators is performed on data from the TLESS dataset as well as in a realworld robotic assembly task.