Martin Tykal

dblp:177/8640 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author

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.

Human-computer interaction and pervasive computing
1 paper
User interface design and tools · 50% Human-robot interaction · 50%
Artificial intelligence
1 paper
Motion planning and robot control · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction › learning from demonstration
kinesthetic teaching
0.212016
Incrementally Assisted Kinesthetic Teaching for Programming by Demonstration · HRI 2016
User interface design and tools › end-user programming
programming by demonstration
0.212016
Incrementally Assisted Kinesthetic Teaching for Programming by Demonstration · HRI 2016
Robotics › Motion planning and robot control
robot learning
0.112016
Incrementally Assisted Kinesthetic Teaching for Programming by Demonstration · HRI 2016

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

virtual tool dynamics · 0.5user study · 0.5incremental learning · 0.5cartesian impedance control · 0.5
YearPublicationVenuePosition
2016 Incrementally Assisted Kinesthetic Teaching for Programming by Demonstration
abstract
Kinesthetic teaching is an established method of teaching robots new skills without requiring robotics or programming knowledge. However, the inertia and uncoordinated motions of individual joints decrease the intuitiveness and naturalness of interaction and impair the quality of the learned skill. This paper proposes a method to ease kinesthetic teaching by combining the idea of incremental learning through warping several demonstrations into a common frame with virtual tool dynamics to assist the user during teaching. In fact, during a sequence of demonstrations the stiffness of the robot under Cartesian impedance control is gradually increased, to provide stronger assistance to the user based on the demonstrations accumulated up to that moment. Therefore, the operator has the opportunity to progressively refine the task's model while the robot more docilely follows the learned action. Robot experiments and a user study performed on 25 novice users show that the proposed approach improves both usability as well as resulting skill quality.
Martin Tykal, Alberto Montebelli, Ville Kyrki
HRI1