Modern industry benefits from the automation capabilities and flexibility of robots. Consequently, the performance depends on the individual task, robot and trajectory, while application periods of several years lead to a significant impact of the use phase on the resource efficiency. In this work, simulation models predicting a robot’s energy consumption are extended by an estimation of the reliability, enabling the consideration of maintenance to enhance the assessment of the application’s life cycle costs. Furthermore, a life cycle assessment yields the greenhouse gas emissions for the individual application. Potential benefits of the combination of motion simulation and cost analysis are highlighted by the application to an exemplary system. For the selected application, the consumed energy has a distinct impact on greenhouse gas emissions, while acquisition costs govern life cycle costs. Low cycle times result in reduced costs per workpiece, however, for short cycle times and higher payloads, the probability of required spare parts distinctly increases for two critical robotic joints. Hence, the analysis of energy consumption and reliability, in combination with maintenance, life cycle costing and life cycle assessment, can provide additional information to improve the resource efficiency.

In this paper, we present an inverse optimal control-based transfer of motions from human experiments to humanoid robots and apply it to walking in constrained environments. To this end, we introduce a 3D template model, which describes motion on the basis of center-of-mass trajectory, foot trajectories, upper-body orientation, and phase duration. Despite its abstract architecture, with prismatic joints combined with damped series elastic actuators instead of knees, the model (including dynamics and constraints) is suitable for describing both human and humanoid locomotion with appropriate parameters. We present and apply an inverse optimal control approach to identify optimality criteria based on human motion capture experiments. The identified optimal strategy is then transferred to a humanoid robot template model for gait generation by solving an optimal control problem, which takes into account the properties of the robot and differences in the environment. The results of this step are the center-of-mass trajectory, the foot trajectories, the torso orientation, and the single and double support phase durations for a sequence of steps, allowing the humanoid robot to walk within a new environment. In a previous paper, we have already presented one computational cycle (from motion capture data to an optimized robot template motion) for the example of walking over irregular stepping stones with the aim of transferring the motion to two very different humanoid robots (iCub@Heidelberg and HRP-2@LAAS). This study represents an extension, containing an entirely new part on the transfer of the optimized template motion to the iCub robot by means of inverse kinematics in a dynamic simulation environment and also on the real robot.

Robot application developers program industrial robots during the commissioning or re-configuration of a production line. Not only do they require precise locations of work pieces and respective movements but also need to optimize program parameters manually. This often results in high engineering effort and stress. This paper introduces an approach to support robot application developers using distributed learning and machine learning to optimize robot application parameters in simulation. Contrary to end-to-end robot learning approaches which are often infeasible to adopt in practice, this approach leverages domain knowledge of application developers and their familiarity with writing robot programs. The developer is only additionally required to specify bounds for parameters to be optimized and an evaluation criterion. A central machine triggers as many worker machines as required to run simulations in parallel with different parameters to learn the best policy. Our approach works independent of the robot task and robot simulation software and can be configured with different algorithms. To validate the approach, it is applied to a robotic insertion task in an assembly scenario using a genetic algorithm and ABB RobotStudio.