Robustness of Motions

The synchronous and alternate hopping motions were robust enough to perform more than 100 continuous hops. Limiting factor was only mechanical wear in the ropes as described ear-lier. The passive elastic properties of the system make it robust against variations in ground contact timing so that even with the first approach using fixed cycle times hopping is pos-sible, even though the fixed timings do not fit exactly each hopping cycle. Adapting the

cycle times based on the ground contact feedback increased the performance of the hop-ping as described above for the duty factors. But it also made the motions more robust, even against manual external disturbances in ground contact time and position as described in Section 6.1.5. Without any need to adapt the trajectory generation or motor control, hopping on changing ground heights and with manually altered landing positions of the feet is pos-sible. This can be attributed to the system’s inherent mechanical adaptation to the external disturbances through its elastic elements and validates that the design approach used in the BioBiped robot series helps to perform robust locomotion.

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7 Expert Guided Hardware-in-the-Loop Motion Optimization for Musculoskeletal Bipedal Robots

In a hardware-in-the-loop optimization, experiments are conducted on the robot hardware to determine the value of a quality criterion which is to be optimized. This chapter first mo-tivates the use of hardware-in-the-loop optimization in comparison to optimizing solely us-ing a simulation model. Then other approaches to optimization of bipedal musculoskele-tal robots are described. An example application of an established hardware-in-the-loop optimization approach for the BioBiped1 robot and its shortcomings are discussed. As the increased complexity of a musculoskeletal robot with its mono- and biarticular elastic struc-tures offers an even larger number of variables to optimize compared to conventional stiff robots, a direct hardware-in-the-loop optimization would need more hardware experiments than practically feasible. Therefore, a new concept for hardware-in-the-loop optimization is presented, integrating expert knowledge and simulation results into the optimization process to reduce the number of hardware experiments needed. This new concept is then demon-strated in an example optimization of the performance of a synchronous hopping motion for the musculoskeletal BioBiped2 robot.

7.1 Motivation and Problem Formulation

Hardware-in-the-loop optimization, where the robot hardware is used to evaluate a quality criterion, plays a very important role in the optimization of mechanical and control param-eters of musculoskeletal robots. Even though optimization using a simulation model has a much smaller cost per experiment, the hardware is its only perfect "model". A simulation model is always subject to idealization and abstraction only reflecting the properties of the real robot that were deemed relevant for the goal of the simulation. And there will always be a gap between simulations and real world systems as not all details can be identically mapped to a model for simulation [32]. Dynamic effects that are very difficult to model perfectly in-clude changes in contact with the ground or a constraining mechanism, internal friction and spring properties of physical linear springs, which are never completely linear. Therefore, the limited accuracy of current simulation models for highly dynamic motions with mus-culoskeletal robots still does not allow fine tuning parameters for direct use on the robot hardware. Furthermore, the robot hardware does not stay exactly the same over its whole lifetime as it is affected by longterm effects like wear and short term effects like changes in temperature or humidity, which are rarely accounted for in any practically usable simulation model of a complex robotic system. A simulation model is rather built with idealizations to be practically usable in terms of complexity and run-time performance by modeling only the most relevant properties while still fulfilling a specific purpose. Nevertheless, a good simu-lation model can be used to identify and exclude parameter spaces that will not give good results when applied to the robot or might even damage the system. Using the knowledge

gained from these simulation experiments to systematically plan the robotic experiments helps to reduce the number of experiments needed on the robot thereby preventing poten-tial damage and reducing the wear inflicted upon the hardware as well as the time needed to perform the experiments.

Using a hardware-in-the-loop optimization approach with a robot can pose certain addi-tional requirements on the robotic system. In order to be able to evaluate a desired optimiza-tion criterion directly using the robot, it might be necessary to add specific sensors to the system, which would otherwise not be needed in the normal operation of the system.

The parameters that are to be optimized range from mechanical passive control parame-ters like spring stiffnesses and lever arm lengths to active control parameparame-ters like controller gains or trajectory parameters. Because of the large number of these parameters in a muscu-loskeletal robot, a pure black box optimization conducted on the hardware would need more robot experiments than are practically feasible. A new concept is therefore presented in Sec-tion 7.3 to reduce the number of experiments needed in an expert guided hardware-in-the-loop optimization by the application of structured information from simulation experiments, biomechanical understanding of the system and knowledge from previous experiments. But first a conventional hardware-in-the-loop approach is discussed in the following section.

7.2 Conventional Approach of Hardware-in-the-Loop Optimization applied to BioBiped1