Modeling articulated figure motion with physically- and physiologically-based constraints

A methodology and algorithm are presented that generate motions imitating the way humans complete a task under various loading conditions. The path taken depends on "natural" parameters: the figure geometry, the given load, the final destination, and, especially, the strength model of the agent. Additional user controllable parameters of the motion are the comfort of the action and the perceived exertion of the agent. The algorithm uses this information to incrementally compute a motion path of the end-effector moving the load. It is therefore instantaneously adaptable to changing force, loading, and strength conditions. Various strategies are used to model human behavior (such as available torque, reducing moment and pull back) that direct the trajectories. The strength model dictates acceptable kinematic postures. The resulting algorithm also offers torque control without the tedious user expression of driving forces under a dynamics model. The algorithm runs in near-real-time and offers an agent-dependent toolkit for fast path prediction. Examples are presented for various lifting tasks, including one- and two-handed lifts, and raising the body from a seated posture.