Simulating the Task-level Control of Human Motion: A Methodology and Framework for Implementation

Vincent De Sapio, James Warren, Oussama Khatib, Scott Delp

The Visual Computer

Volume 21, Number 5, Pages 289-302, 2005

Motivation

In robotics, there is emerging interest in generating human-like motion for humanoid robots in real physical environments. In computer graphics, there is a similar desire to automatically generate realistic motion for human models in virtual environments.

Goal of This Research

The goal is to develop a taskl-level architecture for providing feedback control in physics-based simulations of goal-directed human motion.

Goal of This Paper

An operational space approach from robotics is used to create a task-level control architecture for feedback control of simulations of goal-directed human motion. Added to the approach is an extension that addresses the control of muscle-driven systems. Task/posture decomposition is exploited, allowing human musculoskeletal properties to direct postural behavior during performance of a task. Also presented in this paper is an environment for generating musculoskeletal simulations of human movement.

Related Work

The computer graphics community has developed method for automatically generating realistic motion for virtual actors using high-level commands [5] [4] [29]. The robotics community is similarly interested in developing a high-level control framework to generate human-like motion for complex humanoid robots [9] [18] [24]. This paper draws on operational space approaches that have been shown to be effective in robotics [11] [12] [14]. However, operational space approaches have had only limited application in biomechanics [27].

The biomechanics community has investigated the use of computational muscle models for neuromuscular dynamics [2] [3] [31]. Many biomechanics labs use SIMM [3] for modeling musculoskeletal geometry and joint kinematics. SD/FAST [8] is used to generate multi-body equations of motion for a human model and simulate the feed-forward dynamic response of the musculoskeletal system to neural inputs. SIMM has no native control capabilities so users must specify the control in open loop using their own feed-forward optimization routine [21] or in closed loop using their own feedback control routine [28]. Other musculoskeletal simulation systems have also been developed [7] [17] [16] [22].

The software environment presented in this paper is based on SAI [13], a set of libraries developed to perform interactive simulation of complex robotic systems.

Results

No results are presented in this paper.

What This Paper Contributes

This work integrates a musculoskeletal dynamics model and a task-level control method, allowing objectives for the control of a musculoskeletal model to be stated in terms of a natural set of task coordinates. The feedback nature of the framework provides a stable response to disturbances and external interactions unlike feed-forward approaches (e.g. dynamic optimization).

What This Paper Does Not Contribute

The goal of this paper was postural control. No walking or other gait control has been addressed. Also, the stiff tendon muscle model used for control neglects some important dynamic properties of muscle.

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