We introduce a physics-based method to synthesize concurrent object
manipulation using a variety of manipulation strategies provided by
different body parts, such as grasping objects with the hands,
carrying objects on the shoulders, or pushing objects with the elbows
or the torso. We design dynamic controllers to physically simulate
upper-body manipulation and integrate it with procedurally generated
locomotion and hand grasping motion. The output of the algorithm is a
continuous animation of the character manipulating multiple objects
and environment features concurrently at various locations in a
constrained environment. To capture how humans deftly exploit
different properties of body parts and objects for multitasking, we
need to solve challenging planning and execution problems. We
introduce a graph structure, a manipulation graph, to describe
how each object can be manipulated using different strategies. The
problem of manipulation planning can then be transformed to a standard
graph traversal. To achieve the manipulation plan, our control
algorithm optimally schedules and executes multiple tasks based on the
dynamic space of the tasks and the state of the character. We
introduce a ''task consistency'' metric to measure the physical feasibility of multitasking. Furthermore, we exploit the redundancy of
control space to improve the character's ability to multitask. As a
result, the character will try its best to achieve the current tasks
while adjusting its motion continuously to improve the multitasking
consistency for future tasks.
