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Robust and Reliable Grasping

Even though grasping has been a central topic in robotics for decades, robots still have great difficulty to pick up arbitrary objects when they operate in open, unknown environments or under uncontrolled conditions. Lifting this limitation would make using robots fit for many repetitive chores: robots could check and restock supermarket aisles, tidy up households, dispatch mail orders at distribution centers, collect ripe fruits, or do ecological pest control by selectively removing bugs.

To make this vision a reality, robots have to learn to grasp as reliable as humans. We try to discover the tricks people unconsciously employ when they cannot rely on their perception, and transfer these insights to robots. One such strategy is to exploit the structure of the environment to guide hand and finger motion quickly and reliably during the grasp.

 

The lead effort for the grasping projects discussed below are funded by the Soft Manipulation (SOMA) Horizon 2020 EU project. Please visit the project website for more information.

Human Grasping Strategies

Lupe

We study human grasping under a variety of conditions in order to identify and characterize different grasping strategies. Specially, we are interested in strategies that are robust to be performed under different kinds of impairment, e.g. visual. Our final goal, is to transfer those robust strategies to a robot. 

Compliant Manipulators

How can we build robots that can exploit constraints to motion rather than avoid them? How do we control such robots? We explore these questions in our research project on soft hands.

Contact: Raphael Deimel

Perception and Planning of Grasps

Lupe

Our approach to grasping is motivated by the fact that humans don't avoid contact with the environment but rather exploit it to generate haptic feedback complementing visual feedback. This exploitation of environmental constraints simplifies the grasping problem by converting a high-dimensional configuration search problem into successive local searches guided by these environmental constraints, such as surfaces or edges.

We are developing algorithms that model the environment as a collection of environmental constraints which can be used to generate reactive feedback plans that lead to robust and reliable grasps.

Contact: Clemens Eppner, Jessica Abele

 

 

Funding

Lupe
Lupe

Soft Manipulation (SoMa) -  funded by the European Commission in the Horizon 2020 program, award number 645599, May 2015 - April 2019. Alexander von Humboldt professorship - awarded by the Alexander von Humboldt foundation and funded through the Ministry of Education and Research, BMBF,
July 2009 - June 2014

Publications

Clemens Eppner and Oliver Brock. Visual Detection of Opportunities to Exploit Contact in Grasping Using Contextual Multi-Armed Bandits. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.

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Steffen Puhlmann and Fabian Heinemann and Oliver Brock and Marianne Maertens. A Compact Representation of Human Single-Object Grasping. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1954-1959, 2016.

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Raphael Deimel and Oliver Brock. A Novel Type of Compliant and Underactuated Robotic Hand for Dexterous Grasping. The International Journal of Robotics Research 35(1-3):161-185, 2016.

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Clemens Eppner and Raphael Deimel and Jose Alvarez-Ruiz and Marianne Maertens and Oliver Brock. Exploitation of Environmental Constraints in Human and Robotic Grasping. The International Journal of Robotics Research 34(7):1021-1038, 2015.

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Clemens Eppner and Oliver Brock. Planning Grasp Strategies That Exploit Environmental Constraints. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4947 - 4952, 2015.

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