Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories

Wojtynek M, Steil JJ, Wrede S. Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories. KI - Künstliche Intelligenz. German Journal of Artificial Intelligence. 2019;33(2):151-161.The transformation of today's manufacturing lines into truly adaptive systems facilitating individualized mass production requires new approaches for the efficient integration, configuration and control of robotics and automation components. Recently, various types of Plug-and-Produce architectures were proposed that support the discovery, integration and configuration of field devices, automation equipment or industrial robots during commissioning or even operation of manufacturing systems. However, in many of these approaches, the configuration possibilities are limited, which is a particular problem if robots operate in dynamic environments with constrained workspaces and exchangeable automation components as typically required for flexible manufacturing processes. In this article, we introduce an extended Plug-and-Produce concept based on dynamic motion planning, co-simulation and a collaborative human-robot interaction scheme that facilitates the quick adaptation of robotics behaviors in the context of a modular production system. To confirm our hypothesis on the efficiency and usability of this concept, we carried out a feasibility study where participants performed a flexible workcell setup. The results indicate that the assistance and features for planning effectively support the users in tasks of different complexity and that a quick adaption is indeed possible. Based on our observations, we identify further research challenges in the context of Plug, Plan and Produce applied to smart manufacturing

Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto

Queisser JF, Hammer B, Ishihara H, Asada M, Steil JJ. Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. In: 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE; 2018: 39-45.n this work, we propose an extension of parameterized skills to achieve generalization of forward control signals for action primitives that result in an enhanced control quality of complex robotic systems. We argue to shift the complexity of learning the full dynamics of the robot to a lower dimensional task related learning problem. Due to generalization over task variability, online learning for complex robots as well as complex scenarios becomes feasible. We perform an experimental evaluation of the generalization capabilities of the proposed online learning system through simulation of a compliant 2DOF arm. Scalability to a complex robotic system is demonstrated on the pneumatically driven humanoid robot Affetto including 6DOF

Reliable Integration of Continuous Constraints into Extreme Learning Machines

Neumann K, Rolf M, Steil JJ. Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2013;21(Suppl 2):35-50.The application of machine learning methods in the engineering of intelligent technical systems often requires the integration of continuous constraints like positivity, mono- tonicity, or bounded curvature in the learned function to guarantee a reliable perfor- mance. We show that the extreme learning machine is particularly well suited for this task. Constraints involving arbitrary derivatives of the learned function are effectively implemented through quadratic optimization because the learned function is linear in its parameters, and derivatives can be derived analytically. We further provide a construc- tive approach to verify that discretely sampled constraints are generalized to continuous regions and show how local violations of the constraint can be rectified by iterative re- learning. We demonstrate the approach on a practical and challenging control problem from robotics, illustrating also how the proposed method enables learning from few data samples if additional prior knowledge about the problem is available

Online learning and generalization of parts-based image representations by Non-Negative Sparse Autoencoders

Lemme A, Reinhart F, Steil JJ. Online learning and generalization of parts-based image representations by Non-Negative Sparse Autoencoders. Neural Networks. 2012;33:194-203