IEEE Robotics & Automation Magazine - September 2018 - 91

Figure 7. The renowned pizza chef Enzo Coccia wearing the
Xsens MVN suit, with the RoDyMan avatar in the background
acquiring and repeating the movements of the chef.

uncertain dynamics mainly due to friction; parameter estimation and/or robust controllers are thus essential. Moreover,
physics terms causing nonsmooth behavior are often neglected when deriving the mathematical model of a given nonprehensile task, which makes the nonprehensile system look like
a prehensile one. This happens with rolling, sliding, and pushing nonprehensile manipulation primitives. The proof that
the designed controller does not violate the given assumptions is usually performed a posteriori. A method to directly
control the contact forces should be addressed, and this might
be a future research direction, leading to the design of nonsmooth and hybrid controllers that are also new frontiers for
the research community.
Another approach might be to observe a pizza chef 's
activities to learn task simplification and to synthesize
human-inspired control strategies, e.g., an integrated
robotic platform able to acquire and transfer human-body
motion to a robotic system is obtained by interfacing
RoDyMan with a low-cost motion-capture system (see
Figure 7). Once the teleoperation algorithm for real-time
replication of human motion on RoDyMan is developed, a
comprehensive taxonomy of dynamic prehensile and nonprehensile tasks, ordered for different levels of hand-arm
and dual-arm coordination, can be built from scratch. To
this aim, taking inspiration from the research conducted
on anthropomorphic hands [21] or a single hand-arm system [22], a study on postural synergies for dual-arm robotic manipulation can be conducted to develop a framework
simplifying learning strategies from human imitation.
Such an approach will also take advantage of dimensionality reduction strategy to successfully apply supervised reinforcement-learning algorithms using synergistic motion.
These observations showed that the motion planner is crucial for nonprehensile tasks because the repetitive actions
seem well imprinted in the pizza chef 's mind, while the
corrections made by the hands are very small despite the
difference between various doughs. Therefore, a good
motion planner is the essential instrument within nonprehensile manipulation.

Another question that may arise is why the pizza-making
procedure was used as an example. Is there a need to have a
robot make pizza? In truth, the pizza-making process is only
a convenient media expedient with scientific purpose. It is
clear that, if a robot is able to manipulate pizza dough, it
might be able to perform similar difficult manipulation
tasks. In 1997, the RoboCup began; the intent certainly was
not to replace real soccer players, but rather to advance the
state of the art while facing both gaming and difficult problems for robots. With the same aim, RoDyMan is trying to
mimic the artistic ability of a pizza chef. While facing this
big challenge, many subproblems have to be addressed in
parallel, which could have an impact in other domains. The
perception of elastic objects is currently being applied in the
medical context to shape variations in muscles and organs.
The manipulation performed while tossing the deformable
dough is currently under investigation to improve the automation of gluing a shoe's lower surfaces. The batting process
has similar dynamics to walking gaits, and it could be used
to improve autonomy of humanoids or employed for actuated prostheses.
Acknowledgment
The research leading to these results was supported by the
RoDyMan project, which received funding from the European Research Council FP7 Ideas under Advanced Grant agreement 320992. The authors are solely responsible for the
content of this manuscript.
References
[1] D. Prattichizzo and J. Trinkle, "Grasping," in Springer Handbook of
Robotics, B. Siciliano and O. Khatib, Eds. New York: Springer-Verlag,
2016, pp. 955-988.
[2] R. M. Murray, Z. Li, and S. S. Sastry, A Mathematical Introduction to
Robotic Manipulation. Boca Raton, FL: CRC, 1994.
[3] K. Lynch and T. D. Murphey, "Control of nonprehensile manipulation," in Control Problems in Robotics (Springer Tracts in Advanced
Robotics 4), A. Bicchi, D. Prattichizzo, and H. Christensen, Eds. New
York: Springer-Verlag, 2003, pp. 39-57.
[4] A. Satici, F. Ruggiero, V. Lippiello, and B. Siciliano, "A coordinatefree framework for robotic pizza tossing and catching," in Proc. 2016
IEEE Int. Conf. Robotics and Automation, Stockholm, Sweden,
pp. 3932-3939.
[5] G. Bätz, A. Yaqub, H. Wu, K. Kuhnlenz, D. Wollherr, and M. Buss,
"Dynamic manipulation: nonprehensile ball catching," in Proc. 18th
Mediterranean Conf. Control and Automation, Marrakesh, Morocco,
2010, pp. 365-370.
[6] D. Serra, A. Satici, F. Ruggiero, V. Lippiello, and B. Siciliano, "An
optimal trajectory planner for a robotic batting task: The table tennis
example," in Proc. 13th Int. Conf. Informatics in Control, Automation,
and Robotics, Lisbon, Portugal, 2016, pp. 90-101.
[7] P. Reist and R. D'Andrea, "Design and analysis of a blind juggling
robot," IEEE Trans. Robot., vol. 28, no. 6, pp. 1228-1243, 2012.
[8] G. Bätz, U. Mettin, A. Schimdts, M. Scheint, D. Wollherr, and
A. Shiriaev, "Ball dribbling with an underactuated continuoustime control phase: Theory & experiments," in Proc. 2010 IEEE/

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