Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Acting, Interacting, Collaborative Robots
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
2017 (English)In: PROCEEDINGS OF THE 2017 ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI'17), IEEE , 2017, p. 293-293Conference paper, Published paper (Refereed)
Abstract [en]

The current trend in computer vision is development of data-driven approaches where the use of large amounts of data tries to compensate for the complexity of the world captured by cameras. Are these approaches also viable solutions in robotics? Apart from 'seeing', a robot is capable of acting, thus purposively change what and how it sees the world around it. There is a need for an interplay between processes such as attention, segmentation, object detection, recognition and categorization in order to interact with the environment. In addition, the parameterization of these is inevitably guided by the task or the goal a robot is supposed to achieve. In this talk, I will present the current state of the art in the area of robot vision and discuss open problems in the area. I will also show how visual input can be integrated with proprioception, tactile and force-torque feedback in order to plan, guide and assess robot's action and interaction with the environment. Interaction between two agents builds on the ability to engage in mutual prediction and signaling. Thus, human-robot interaction requires a system that can interpret and make use of human signaling strategies in a social context. Our work in this area focuses on developing a framework for human motion prediction in the context of joint action in HRI. We base this framework on the idea that social interaction is highly influences by sensorimotor contingencies (SMCs). Instead of constructing explicit cognitive models, we rely on the interaction between actions the perceptual change that they induce in both the human and the robot. This approach allows us to employ a single model for motion prediction and goal inference and to seamlessly integrate the human actions into the environment and task context. We employ a deep generative model that makes inferences over future human motion trajectories given the intention of the human and the history as well as the task setting of the interaction. With help predictions drawn from the model, we can determine the most likely future motion trajectory and make inferences over intentions and objects of interest.

Place, publisher, year, edition, pages
IEEE , 2017. p. 293-293
Series
ACM IEEE International Conference on Human-Robot Interaction, ISSN 2167-2121
Keywords [en]
Robotics, Human-robot collaboration, Grasping
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:kth:diva-269122DOI: 10.1145/2909824.3020260ISI: 000463724200032ISBN: 978-1-4503-4336-7 (print)OAI: oai:DiVA.org:kth-269122DiVA, id: diva2:1411841
Conference
12th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI), MAR 06-09, 2017, Vienna, AUSTRIA
Note

QC 20200304

Available from: 2020-03-04 Created: 2020-03-04 Last updated: 2020-03-04Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Kragic, Danica

Search in DiVA

By author/editor
Kragic, Danica
By organisation
Robotics, Perception and Learning, RPLCentre for Autonomous Systems, CAS
Human Computer Interaction

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 17 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf