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Abstract:
For robots of increasing complexity such as humanoid
robots, conventional identification of rigid body dynamics
models based on CAD data and actuator models becomes
difficult and inaccurate due to the large number of additional
nonlinear effects in these systems, e.g., stemming from stiff
wires, hydraulic hoses, protective shells, skin, etc. Data driven
parameter estimation offers an alternative model identification
method, but it is often burdened by various other problems,
such as significant noise in all measured or inferred variables
of the robot. The danger of physically inconsistent results also
exists due to unmodeled nonlinearities or insufficiently rich data.
In this paper, we address all these problems by developing a
Bayesian parameter identification method that can automatically
detect noise in both input and output data for the regression
algorithm that performs system identification. A post-processing
step ensures physically consistent rigid body parameters by
nonlinearly projecting the result of the Bayesian estimation onto
constraints given by positive definite inertia matrices and the
parallel axis theorem. We demonstrate on synthetic and actual
robot data that our technique performs parameter identification
with 5 to 20 higher accuracy than traditional methods. Due
to the resulting physically consistent parameters, our algorithm
enables us to apply advanced control methods that algebraically
require physical consistency on robotic platforms.
