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Proceedings Paper

Humanlike agents with posture planning ability
Author(s): Moon R. Jung; Norman I. Badler
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Paper Abstract

Human body models are geometric structures which may be ultimately controlled by kinematically manipulating their joints, but for animation, it is desirable to control them in terms of task-level goals. We address a fundamental problem in achieving task-level postural goals: controlling massively redundant degrees of freedom. We reduce the degrees of freedom by introducing significant control points and vectors, e.g., pelvis forward vector, palm up vector, and torso up vector, etc. This reduced set of parameters are used to enumerate primitive motions and motion dependencies among them, and thus to select from a small set of alternative postures (e.g., bend versus squat to lower shoulder height). A plan for a given goal is found by incrementally constructing a goal/constraint set based on the given goal, motion dependencies, collision avoidance requirements, and discovered failures. Global postures satisfying a given goal/constraint set are determined with the help of incremental mental simulation which uses a robust inverse kinematics algorithm. The contributions of the present work are: (1) There is no need to specify beforehand the final goal configuration, which is unrealistic for the human body, and (2) the degrees of freedom problem becomes easier by representing body configurations in terms of `lumped' control parameters, that is, control points and vectors.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1829, Cooperative Intelligent Robotics in Space III, (1 November 1992); doi: 10.1117/12.131699
Show Author Affiliations
Moon R. Jung, Univ. of Pennsylvania (United States)
Norman I. Badler, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 1829:
Cooperative Intelligent Robotics in Space III
Jon D. Erickson, Editor(s)

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