Titlebook: Geometric and Numerical Foundations of Movements; Jean-Paul Laumond,Nicolas Mansard,Jean-Bernard Las Book 2017 Springer International Publ

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https://doi.org/10.1007/978-981-16-1319-7s stability, collision avoidance, robust performance, and controller synthesis can be addressed by a synergy of classical tools from . and modern computational techniques from .. In this chapter, we give a brief overview of our recent research efforts (with various coauthors) to (i) enhance the scal

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The Problem of Economic Welfareions with potential to profoundly impact society, from safer self-aware environments and smart cities to enhanced model-based medical therapies. Yet, achieving this vision requires addressing the challenge of handling large amounts of very high dimensional data. In this chapter, we provide a tutoria

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S. Prakash Sethi,Oliver F. Williamse data. The task of inverse optimal control is to identify the precise underlying objective function that is optimized in an observed motion. The presented methods can cope with partial and imprecise measurements of the state variables which is typically the case for motion capture recordings. We in

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Elements for a Theory of Teams (1955)ructure of complex body movements in humans helps to develop flexible technical algorithms for movement synthesis. This chapter summarizes work that uses learned structured representations for the synthesis of complex human-like body movements in real-time. This work follows two different general ap

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https://doi.org/10.1007/978-94-010-9276-0performance appears to be quite different from that of robots. Human haptic perception is not compatible with Riemannian geometry, the foundation of classical mechanics and robot control. Instead, evidence suggests that human control is based on dynamic primitives, which enable highly dynamic behavi