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We describe an exploratory approach which explores trade-offs between competing objectives for a specified task.
Pareto-optimality can be used to explore various degrees of task achievement for a given style of physics-based character animation.
Various techniques have been developed to help drive this performance in an effort to create believable character animations.
However, automatic style exploration and generalization from motion data are still open problems.
Exploration of style of task achievement for physics-based character animation can be performed automatically by exploiting ''null spaces'' defined by the task.
Second, we perform automatic style generalization by generalizing a controller for varying degree of task achievement for a specified task.