We present a biped locomotion controller for humanoid models actuated by more than a hundred Hill-type muscles. The key component of the controller is our novel algorithm that can cope with stepbased biped locomotion balancing and the coordination of many nonlinear Hill-type muscles simultaneously. Minimum effort muscle activations are calculated based on muscle contraction dynamics and online quadratic programming. Our controller can faithfully reproduce a variety of realistic biped gaits (e.g., normal walk, quick steps, and fast run) and adapt the gaits to varying conditions (e.g., muscle weakness, tightness, joint dislocation, and external pushes) and goals (e.g., pain reduction and efficiency maximization). We demonstrate the robustness and versatility of our controller with examples that can only be achieved using highly-detailed musculoskeletal models with many muscles.

Figure 1: Our data-driven controller allows the physically-simulated biped character to reproduce challenging motor skills captured in motion data. AbstractWe present a dynamic controller to physically simulate underactuated three-dimensional full-body biped locomotion. Our datadriven controller takes motion capture reference data to reproduce realistic human locomotion through realtime physically based simulation. The key idea is modulating the reference trajectory continuously and seamlessly such that even a simple dynamic tracking controller can follow the reference trajectory while maintaining its balance. In our framework, biped control can be facilitated by a large array of existing data-driven animation techniques because our controller can take a stream of reference data generated on-thefly at runtime. We demonstrate the effectiveness of our approach through examples that allow bipeds to turn, spin, and walk while steering its direction interactively.

Figure 1: Our data-driven controller allows the physically-simulated biped character to reproduce challenging motor skills captured in motion data. AbstractWe present a dynamic controller to physically simulate underactuated three-dimensional full-body biped locomotion. Our datadriven controller takes motion capture reference data to reproduce realistic human locomotion through realtime physically based simulation. The key idea is modulating the reference trajectory continuously and seamlessly such that even a simple dynamic tracking controller can follow the reference trajectory while maintaining its balance. In our framework, biped control can be facilitated by a large array of existing data-driven animation techniques because our controller can take a stream of reference data generated on-thefly at runtime. We demonstrate the effectiveness of our approach through examples that allow bipeds to turn, spin, and walk while steering its direction interactively.

Understanding the complexity of anisotropic turbulent processes in engineering and environmental fluid flows is a formidable challenge with practical significance because energy often flows intermittently from the smaller scales to impact the largest scales in these flows. Conceptual dynamical models for anisotropic turbulence are introduced and developed here which, despite their simplicity, capture key features of vastly more complicated turbulent systems. These conceptual models involve a large-scale mean flow and turbulent fluctuations on a variety of spatial scales with energy-conserving wave-mean-flow interactions as well as stochastic forcing of the fluctuations. Numerical experiments with a six-dimensional conceptual dynamical model confirm that these models capture key statistical features of vastly more complex anisotropic turbulent systems in a qualitative fashion. These features include chaotic statistical behavior of the mean flow with a sub-Gaussian probability distribution function (pdf) for its fluctuations whereas the turbulent fluctuations have decreasing energy and correlation times at smaller scales, with nearly Gaussian pdfs for the large-scale fluctuations and fat-tailed non-Gaussian pdfs for the smaller-scale fluctuations. This last feature is a manifestation of intermittency of the small-scale fluctuations where turbulent modes with small variance have relatively frequent extreme events which directly impact the mean flow. The dynamical models introduced here potentially provide a useful test bed for algorithms for prediction, uncertainty quantification, and data assimilation for anisotropic turbulent systems.wave-mean interaction | stochastic model U nderstanding the complexity of anisotropic turbulence processes over a wide range of spatiotemporal scales in engineering shear turbulence (1-3) as well as climate atmosphere ocean science (4-6) is a grand challenge of contemporary science. This is especially important from a practical viewpoint because energy often flows intermittently from the smaller scales to affect the largest scales in such anisotropic turbulent flows. The typical features of such anisotropic turbulent flows are the following (2-4):(A) The large-scale mean flow is usually chaotic but more predictable than the smaller-scale fluctuations. The overall single point probability distribution function (pdf) of the flow field is nearly Gaussian whereas the mean flow pdf is subGaussian, in other words, with less extreme variability than a Gaussian random variable. The goal here is to develop the simplest conceptual dynamical model for anisotropic turbulence that captures all of the features in (A)-(D) in a transparent qualitative fashion. In contrast with deterministic models of turbulence which are derived by Galerkin truncation of the Navier-Stokes equation (7) and do not display all of the features in (A)-(D), the conceptual models developed here are low-dimensional stochastic dynamical systems; the nonlinear interactions between the large-scale meanflow component and the sm...