IEEE Robotics & Automation Magazine - March 2016 - 63
Correcting for Changes
Expected Perception-Based Control for Reaching a Moving Target
By Nino cauli, Egidio Falotico, alexandre Bernardino,
José Santos-Victor, and cecilia Laschi
E
motion of an external object. The new control system is
implemented in a humanoid robot, the iCub. The robot
reaches in anticipation for an object's future position by
predicting its trajectory and correcting the arm's position
only when necessary. The results of the EP-based controller
are analyzed and compared against a standard controller.
The new EP-based controller is less computationally
demanding and more energy efficient for a marginal loss in
the tracking error.
State-of-the-Art of Expected Perception
Robots are gradually becoming a part of our daily life.
Robotic toys for children and robotic home cleaners are now
easy to find in commodity stores, and robotic companions
and caretakers for elderly people may be available in the near
future. Robots' working environments are no longer the
strictly structured ones of manufacturing industries but
hyperdynamic worlds with multiple uncertainties.
For a robot to deal with a constantly changing environment, it needs to anticipate future conditions to properly control its own movements, just as a human does. Humans appear
to solve this problem by predicting changes in their sensory
systems as a consequence of their actions [3]. Predictions are
obtained using internal models that represent their own bodies' as well as external objects' dynamics [8], [14], [16].
There are three main types of internal models [14]:
● Forward models make it possible to predict the future data
from past perceptions and planned actions.
images licensed by ingram publishing
xpected perception (EP)-based control systems use
the robotic system's internal models and interaction
with the environment to predict the future response
of their sensory inputs. By comparing the sensory
predictions with actual sensory data, the EP control
system monitors the error between the predicted and the
actual sensor observations. If the error is small, the system
may decide to neglect the input and skip any corrective
action, thus saving computational and energy resources. If
the mismatch is large, the system will further process the
sensor signal to compute a corrective action through
feedback. So far, EP systems have been implemented for
predictions based on a robot's motion. In this article, an EP
system is applied to predict the dynamics and anticipate the
Digital Object Identifier 10.1109/MRA.2015.2505958
Date of publication: 19 February 2016
1070-9932/16©2016IEEE
march 2016
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