172 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
Synergy-based Hand Pose Sensing: Reconstruction Enhancement
Low-cost sensing gloves for reconstruction posture provide measurements which
are limited under several regards. They are generated through an imperfectly
known model, are subject to noise, and may be less than the number of Degrees
of Freedom (DoFs) of the hand. Under these conditions, direct reconstruction of
the hand posture is an ill-posed problem, and performance can be very poor.
This paper examines the problem of estimating the posture of a human hand
using(low-cost) sensing gloves, and how to improve their performance by
exploiting the knowledge on how humans most frequently use their hands. To
increase the accuracy of pose reconstruction without modifying the glove
hardware - hence basically at no extra cost - we propose to collect, organize,
and exploit information on the probabilistic distribution of human hand poses
in common tasks. We discuss how a database of such an a priori information can
be built, represented in a hierarchy of correlation patterns or postural
synergies, and fused with glove data in a consistent way, so as to provide a
good hand pose reconstruction in spite of insufficient and inaccurate sensing
data. Simulations and experiments on a low-cost glove are reported which
demonstrate the effectiveness of the proposed techniques.Comment: Submitted to International Journal of Robotics Research (2012
MetaSpace II: Object and full-body tracking for interaction and navigation in social VR
MetaSpace II (MS2) is a social Virtual Reality (VR) system where multiple
users can not only see and hear but also interact with each other, grasp and
manipulate objects, walk around in space, and get tactile feedback. MS2 allows
walking in physical space by tracking each user's skeleton in real-time and
allows users to feel by employing passive haptics i.e., when users touch or
manipulate an object in the virtual world, they simultaneously also touch or
manipulate a corresponding object in the physical world. To enable these
elements in VR, MS2 creates a correspondence in spatial layout and object
placement by building the virtual world on top of a 3D scan of the real world.
Through the association between the real and virtual world, users are able to
walk freely while wearing a head-mounted device, avoid obstacles like walls and
furniture, and interact with people and objects. Most current virtual reality
(VR) environments are designed for a single user experience where interactions
with virtual objects are mediated by hand-held input devices or hand gestures.
Additionally, users are only shown a representation of their hands in VR
floating in front of the camera as seen from a first person perspective. We
believe, representing each user as a full-body avatar that is controlled by
natural movements of the person in the real world (see Figure 1d), can greatly
enhance believability and a user's sense immersion in VR.Comment: 10 pages, 9 figures. Video:
http://living.media.mit.edu/projects/metaspace-ii
An optical sensor for tracking hand articulations
Recognizing and tracking articulations of the human hand is key to the development of areas such as robotics, virtual reality systems and physical rehabilitation. Based on the principle of crossed-polarization detection, a novel optical sensor with a hinge configuration, is proposed to monitor finger articulation. Using 3D printing technology, we fabricated a lightweight and compact sensor suited to attaching on fingers. The weighted average method was applied to the sensor's output data to determine angular positions corresponding to finger joint articulations. The experimental results show excellent consistency with theoretical predictions. The sensor features good accuracy (±0.5% of full scale) and repeatability, improved sensitivity, and an improved measuring range of 180°. The performance of the sensor is a promising development for monitoring finger articulation. Future work will focus on integrating multiple sensors as part of an instrumented glove to evaluate the true potential for monitoring hand articulation
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