  The work presented in this report introduces a framework aimed towards
learning to imitate human gaits. Humans exhibit movements like walking,
running, and jumping in the most efficient manner, which served as the source
of motivation for this project. Skeletal and Musculoskeletal human models were
considered for motions in the sagittal plane, and results from both were
compared exhaustively. While skeletal models are driven with motor actuation,
musculoskeletal models perform through muscle-tendon actuation. Model-free
reinforcement learning algorithms were used to optimize inverse dynamics
control actions to satisfy the objective of imitating a reference motion along
with secondary objectives of minimizing effort in terms of power spent by
motors and metabolic energy consumed by the muscles. On the one hand, the
control actions for the motor actuated model is the target joint angles
converted into joint torques through a Proportional-Differential controller.
While on the other hand, the control actions for the muscle-tendon actuated
model is the muscle excitations converted implicitly to muscle activations and
then to muscle forces which apply moments on joints. Muscle-tendon actuated
models were found to have superiority over motor actuation as they are
inherently smooth due to muscle activation dynamics and don't need any external
regularizers. Finally, a strategy that was used to obtain an optimal
configuration of the significant decision variables in the framework was
discussed. All the results and analysis are presented in an illustrative,
qualitative, and quantitative manner. Supporting video links are provided in
the Appendix.
