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To generate dynamic motions such as hopping and running on legged robots, model-based approaches are usually used to embed the well studied spring-loaded inverted pendulum (SLIP) model into the whole-body robot. In producing controlled SLIP-like behaviors, existing methods either suffer from online incompatibility or resort to classical interpolations based on lookup tables. Alternatively, this paper presents the application of a data-driven approach which obviates the need for solving the inverse of the running return map online. Specifically, a deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics. The trained network is applied online to generate reference foot placements for the humanoid robot. The references are then mapped to the whole-body model through a QP-based inverse dynamics controller. Simulation experiments on the WALK-MAN robot are conducted to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions.

In recent years, many different feedback controllers for robotic applications have been proposed and implemented. However, the high coupling between the different software modules made their integration into one common architecture difficult. Consequently, this has hindered the ability of a user to employ the different controllers into a single, general and modular framework.To address this problem, we present Ctrl-MORE, a software architecture developed to fill the gap between control developers and other users in robotic applications. On one hand, Ctrl-MORE aims to provide developers with an opportunity to integrate easily and share their controllers with other roboticists working in different areas. For example, manipulation, locomotion, vision and so on. On the other hand, it provides to end-users a tool to apply the additional control strategies that guarantee the execution of desired behaviors in a transparent, yet efficient way. The proposed control architecture allows an easier integration of general purpose feedback controllers, such as stabilizers, with higher control layers such as trajectory planners, increasing the robustness of the overall system.

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