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Simulating the task-level control of human motion: a methodology and framework for implementation

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Abstract

A task-level control framework is proposed for providing feedback control in the simulation of goal-directed human motion. An operational space approach, adapted from the field of robotics, is used for this purpose. This approach is augmented by a significant new extension directed at addressing the control of muscle-driven systems. Task/posture decomposition is intrinsically exploited, allowing human musculoskeletal properties to direct postural behavior during the performance of a task. This paper also describes a simulation architecture for generating musculoskeletal simulations of human characters. The evolving capabilities of the collective environment are directed toward autonomously generating realistic motion control for virtual actors in interactive computer graphics applications, as well as synthesizing the control of human-like motion in robotic systems.

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Authors and Affiliations

  1. Artificial Intelligence Laboratory, Computer Science Department, Stanford University, Stanford, CA, 94305, USA

    Vincent De Sapio, James Warren & Oussama Khatib

  2. Neuromuscular Biomechanics Laboratory, Mechanical Engineering and Bioengineering Departments, Stanford University, Stanford, CA, 94305, USA

    Scott Delp

Authors
  1. Vincent De Sapio
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  2. James Warren
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  3. Oussama Khatib
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  4. Scott Delp
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Correspondence to Vincent De Sapio.

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De Sapio, V., Warren, J., Khatib, O. et al. Simulating the task-level control of human motion: a methodology and framework for implementation. Vis Comput 21, 289–302 (2005). https://doi.org/10.1007/s00371-005-0284-4

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  • DOI: https://doi.org/10.1007/s00371-005-0284-4

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