A methodology and algorithm is presented that generates motions imitating the way humans complete a lifting 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 pull back, add additional joints, and jerk) that compute the driving torques as the situation changes. The strength model dictates acceptable kinematic postures. The resulting algorithm offers torque control without the tedious user expression of driving forces under a dynamics model. The algorithm runs in near-realtime 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.