IEEE Robotics & Automation Magazine - June 2023 - 56

Based on the experimental results, the fingertip design
approach needs to be extended to enable the manipulation
of complex objects and mechanisms, such as mechanical
subsystems.
Moreover, the current setup is limited to grasping objects
of smaller size at their lateral surface. Advancing the testbed to
handle finger bases of different sizes and morphology, adapted
for different kinds of grasp patterns, could improve the spectrum
of executable manipulation scenarios significantly.
Although our setup is optimized to use a specialized
fingertip for a designated object to increase manipulation
performance, an automatic design approach optimizing a
single finger surface for multiple objects could increase the
versatility of a designated finger-pair library of limited size.
To further improve this versatility and the adaptation time, the
manipulation objects and task scenes could be automatically
scanned to derive the geometrical object information independent
of the CAD data. Also, the relative position and orientation
between the gripper finger and the manipulation object
could be extracted from these recordings. In that context, a
manual demonstration of the desired assembly operation with
the corresponding objects could be used to automatically
derive and parameterize the robot motion and manipulation
skills required to conduct the desired tasks.
To further explore the potential combination of our work
with machine learning-based design approaches, an exhaustive
object dataset must be generated and used to compare different
machine learning methods via a final experimental evaluation.
ACKNOWLEDGMENT
The authors would like to thank Diego Hidalgo and Samuel
Schneider for their fruitful discussions. This work was supported
by the German Research Foundation (DFG) as part of
Germany's Excellence Strategy, EXC 2050/1, Project ID
390696704 " Centre for Tactile Internet with Human-in-theLoop "
of Technical University Dresden. The authors acknowledge
funding of KI.FABRIK (Artificial Intelligence Factory,
Bavaria) by the Bavarian Ministry of Economic Affairs,
Regional Development, and Energy, Phase 1 Infrastruktur and
Forschungs und Entwicklung (Grant DIK0249), as well as
funding of Lighthouse Initiative Geriatronics by LongLeif
GaPa gGmbH. Please note that S. Haddadin has a potential
conflict of interest as a shareholder of Franka Emika GmbH.
AUTHORS
Johannes Ringwald, Chair of Robotics and Systems Intelligence
and Munich Institute of Robotics and Machine Intelligence,
Technical University Munich, D-80797 Munich, Germany.
Email: johannes.ringwald@tum.de.
Shaochuan Zong, Chair of Robotics and Systems
Intelligence and Munich Institute of Robotics and Machine
Intelligence, Technical University Munich, D-80797 Munich,
Germany. Email: shaochuan.zong@tum.de.
Abdalla Swikir, Chair of Robotics and Systems
Intelligence and Munich Institute of Robotics and Machine
Intelligence, Technical University Munich, D-80797 Munich,
56 IEEE ROBOTICS & AUTOMATION MAGAZINE JUNE 2023
Germany; Department of Electrical and Electronic Engineering,
Omar Al-Mukhtar University, QP56 Albaida, Libya; and Centre
for Tactile Internet with Human-in-the-Loop, Technical
University Dresden, 01062 Dresden, Germany. Email: abdalla.
swikir@tum.de.
Sami Haddadin, Chair of Robotics and Systems
Intelligence and Munich Institute of Robotics and Machine
Intelligence, Technical University Munich, D-80797 Munich,
Germany; and Centre for Tactile Internet with Human-in-theLoop,
Technical University Dresden, 01062 Dresden, Germany.
Email: haddadin@tum.de.
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IEEE Robotics & Automation Magazine - June 2023

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