  We present a new framework to generate human-like lower-limb trajectories in
periodic and non-periodic walking conditions. In our method, walking dynamics
is encoded in 3LP, a linear simplified model composed of three pendulums to
model falling, swing and torso balancing dynamics. To stabilize the motion, we
use an optimal time-projecting controller which suggests new footstep
locations. On top of gait generation and stabilization in the simplified space,
we introduce a kinematic conversion method that synthesizes more human-like
trajectories by combining geometric variables of the 3LP model adaptively.
Without any tuning, numerical optimization or off-line data, our walking gaits
are scalable with respect to body properties and gait parameters. We can change
various parameters such as body mass and height, walking direction, speed,
frequency, double support time, torso style, ground clearance and terrain
inclination. We can also simulate the effect of constant external dragging
forces or momentary perturbations. The proposed framework offers closed-form
solutions in all the three stages which enable simulation speeds orders of
magnitude faster than real time. This can be used for video games and
animations on portable electronic devices with a limited power. It also gives
insights for generation of more human-like walking gaits with humanoid robots.
