10,745 research outputs found
Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations
Sensor gloves are popular input devices for a large variety of applications
including health monitoring, control of music instruments, learning sign
language, dexterous computer interfaces, and tele-operating robot hands. Many
commercial products as well as low-cost open source projects have been
developed. We discuss here how low-cost (approx. 250 EUROs) sensor gloves with
force feedback can be build, provide an open source software interface for
Matlab and present first results in learning object manipulation skills through
imitation learning on the humanoid robot iCub.Comment: 3 pages, 3 figures. Workshop paper of the International Conference on
Robotics and Automation (ICRA 2015
Assistive robotics: research challenges and ethics education initiatives
Assistive robotics is a fast growing field aimed at helping healthcarers in hospitals, rehabilitation centers and nursery homes, as well as empowering people with reduced mobility at home, so that they can autonomously fulfill their daily living activities. The need to function in dynamic human-centered environments poses new research challenges: robotic assistants need to have friendly interfaces, be highly adaptable and customizable, very compliant and intrinsically safe to people, as well as able to handle deformable materials.
Besides technical challenges, assistive robotics raises also ethical defies, which have led to the emergence of a new discipline: Roboethics. Several institutions are developing regulations and standards, and many ethics education initiatives include contents on human-robot interaction and human dignity in assistive situations.
In this paper, the state of the art in assistive robotics is briefly reviewed, and educational materials from a university course on Ethics in Social Robotics and AI focusing on the assistive context are presented.Peer ReviewedPostprint (author's final draft
Constraining the Size Growth of the Task Space with Socially Guided Intrinsic Motivation using Demonstrations
This paper presents an algorithm for learning a highly redundant inverse
model in continuous and non-preset environments. Our Socially Guided Intrinsic
Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both
social learning and intrinsic motivation, to specialise in a wide range of
skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a
fishing skill learning experiment.Comment: JCAI Workshop on Agents Learning Interactively from Human Teachers
(ALIHT), Barcelona : Spain (2011
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