Cognitive Architectures for Robots Learning of In-Hand Manipulation
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".
Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 7032
Special Issue Editors
Interests: robotics; human robot interaction; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: human–robot interaction; knowledge representation; wearable computing; tactile sensing
Interests: human–robot interaction; grasping and dexterous manipulation; artificial perception systems/autonomous systems; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: hybrid force/position control of robot manipulators; multi-robot cooperation; control of redundant systems with collision avoidance; modeling, planning, and multi-modal control of a robotic hand to perform dexterous manipulation
Interests: robotics; vision; planning; active perception
Special Issues, Collections and Topics in MDPI journals
Interests: robot vision; service robots; object detection; scene understanding; robots at home
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The study of human in-hand dexterous manipulation and the transfer of these skills to robotic hands is enabled through observation and understanding of hand–object interaction; more specifically, the interaction between humans, the environment, and robots. It is, therefore, of the utmost importance to focus research on how humans use their hands, in order to drive the development of novel robots’ capacity to deal with highly sophisticated tasks requiring interaction, collaboration, and communication with humans in a socially acceptable and safe manner. For example, a robot should be able to dexterously make use of tools, synchronize its movements with a human when collaborating, either for joint work, turn-taking or manipulating objects in a trustworthy manner. These activities require robot hands to coordinate with human motions as well as advanced capabilities to interpret the objects or environment in a given social context. To advance robot dexterous capabilities and to fully leverage the interaction between the human and robot when learning requires interaction between different research fields.
This Special Issue targets new studies on conceptual and engineering tools to: (i) sense, understand and interpret human hand motions; (ii) recognize objects and their physical characteristics; and (iii) model and encode this knowledge to develop new robot behaviours.
Themes of interest to this special session include, but are not limited to:
- Data extraction of human handling tasks;
- Datasets of in-hand manipulation;;
- Hand pose estimation and tracking;
- Gesture, action, and intent recognition;
- Learning from demonstration;
- Imitation learning;
- Transfer learning;
- Object modeling, recognition, pose estimation, and tracking;
- Object grasping;
- Control of anthropomorphic hands.
Extended versions of conference papers that show significant improvement (minimum of over 50%) can be considered for publication in this Special Issue.
Dr. Alessandro Carfì
Dr. Fulvio Mastrogiovanni
Dr. Diego R. Faria
Dr. Véronique Perdereau
Dr. Timothy Patten
Prof. Dr. Markus Vincze
Guest Editors
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