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Harald Staab

dblp:11/7415 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Robot manipulation · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › dexterous manipulation
extrinsic dexterity
0.422014
Regrasping objects using extrinsic dexterity · ICRA 2014
Extrinsic dexterity: In-hand manipulation with external forces · ICRA 2014
Robotics › Robot manipulation › dexterous manipulation
in-hand manipulation
0.422014
Regrasping objects using extrinsic dexterity · ICRA 2014
Extrinsic dexterity: In-hand manipulation with external forces · ICRA 2014
Robotics › Robot manipulation › grasping
regrasping
0.422014
Regrasping objects using extrinsic dexterity · ICRA 2014
Extrinsic dexterity: In-hand manipulation with external forces · ICRA 2014
Robotics › Robot manipulation
grasping
0.112014
Extrinsic dexterity: In-hand manipulation with external forces · ICRA 2014

Methods — techniques the papers use, named apart from their topics

open-loop scripted actions · 0.2
YearPublicationVenuePosition
2014 Extrinsic dexterity: In-hand manipulation with external forces
abstract
“In-hand manipulation” is the ability to reposition an object in the hand, for example when adjusting the grasp of a hammer before hammering a nail. The common approach to in-hand manipulation with robotic hands, known as dexterous manipulation [1], is to hold an object within the fingertips of the hand and wiggle the fingers, or walk them along the object's surface. Dexterous manipulation, however, is just one of the many techniques available to the robot. The robot can also roll the object in the hand by using gravity, or adjust the object's pose by pressing it against a surface, or if fast enough, it can even toss the object in the air and catch it in a different pose. All these techniques have one thing in common: they rely on resources extrinsic to the hand, either gravity, external contacts or dynamic arm motions. We refer to them as “extrinsic dexterity”. In this paper we study extrinsic dexterity in the context of regrasp operations, for example when switching from a power to a precision grasp, and we demonstrate that even simple grippers are capable of ample in-hand manipulation. We develop twelve regrasp actions, all open-loop and hand-scripted, and evaluate their effectiveness with over 1200 trials of regrasps and sequences of regrasps, for three different objects (see video [2]). The long-term goal of this work is to develop a general repertoire of these behaviors, and to understand how such a repertoire might eventually constitute a general-purpose in-hand manipulation capability.
Nikhil Chavan Dafle, Alberto Rodriguez 0003, Robert Paolini, Bowei Tang, Siddhartha S. Srinivasa, Michael A. Erdmann, Matthew T. Mason, Ivan Lundberg, Harald Staab, Thomas A. Fuhlbrigge
ICRA9
2014 Regrasping objects using extrinsic dexterity
abstract
This video presents the application of Extrinsic Dexterity to change the pose of an object in the hand, i.e., to regrasp the object.
Nikhil Chavan Dafle, Alberto Rodriguez 0003, Robert Paolini, Bowei Tang, Siddhartha S. Srinivasa, Michael A. Erdmann, Matthew T. Mason, Ivan Lundberg, Harald Staab, Thomas A. Fuhlbrigge
ICRA9