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major goal of humanoid robotics is to enable safe and reliable human-robot collaboration in realworld scenarios. In this article, we present ARMAR-6, a new high-performance humanoid robot for various tasks, including but not limited to grasping, mobile manipulation, integrated perception, bimanual collaboration, compliant-motion execution, and natural language understanding. We describe how the requirements arising from these tasks influenced our major design decisions, resulting in vertical integration during the joint hardware and software development phases. In particular, the entire hardware-including its structure, sensor-actuator units, and low-level controllers-as well as its perception, grasping and manipulation skills, task coordination, and the entire software architecture were all developed by one team of engineers. Component interaction is facilitated by our software framework ArmarX, which

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Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e. g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Via-points Movement Primitive (VMP), that can adapt to arbitrary via-points using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities.

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