  Model-based control usually relies on an accurate model, which is often
obtained from CAD and actuator models. The more accurate the model the better
the control performance. However, in bipedal robots that demonstrate high
agility actions, such as running and hopping, the robot hardware will suffer
from impacts with the environment and deform in vulnerable parts, which
invalidates the predefined model. Thus, it is desired to have an adaptable
kinematic structure that takes deformation into consideration. To account for
this we propose an approach that models all of the robotic joints as 6-DOF
joints and develop an algorithm that can identify the kinematic structure from
motion capture data. We evaluate the algorithm's performance both in simulation
- a three link pendulum, and on a bipedal robot - ATRIAS. In the simulated case
the algorithm produces a result that has a 3.6% error compared to the ground
truth, and on the real life bipedal robot the algorithm's result confirms our
prior assumption where the joint deforms on out-of-plane degrees of freedom. In
addition our algorithm is able to predict torques and forces using the
reconstructed joint mode.
