80 research outputs found
Haptic interfaces: Hardware, software and human performance
Virtual environments are computer-generated synthetic environments with which a human user can interact to perform a wide variety of perceptual and motor tasks. At present, most of the virtual environment systems engage only the visual and auditory senses, and not the haptic sensorimotor system that conveys the sense of touch and feel of objects in the environment. Computer keyboards, mice, and trackballs constitute relatively simple haptic interfaces. Gloves and exoskeletons that track hand postures have more interaction capabilities and are available in the market. Although desktop and wearable force-reflecting devices have been built and implemented in research laboratories, the current capabilities of such devices are quite limited. To realize the full promise of virtual environments and teleoperation of remote systems, further developments of haptic interfaces are critical. In this paper, the status and research needs in human haptics, technology development and interactions between the two are described. In particular, the excellent performance characteristics of Phantom, a haptic interface recently developed at MIT, are highlighted. Realistic sensations of single point of contact interactions with objects of variable geometry (e.g., smooth, textured, polyhedral) and material properties (e.g., friction, impedance) in the context of a variety of tasks (e.g., needle biopsy, switch panels) achieved through this device are described and the associated issues in haptic rendering are discussed
Lump detection with a gelsight sensor
A GelSight sensor is a tactile sensing device comprising a clear elastomeric pad covered with a reflective membrane, coupled with optics to measure the membrane's deformations. When the pad is pressed against an object's surface, the membrane changes shape in accord with mechanical and geometrical properties of the object. Since soft tissue is more compliant than hard tissue, one can detect an embedded lump by pressing the GelSight pad against the tissue surface and observing the hump that forms over the lump. We tested this system's sensitivity by constructing phantoms of soft rubber with hard embedded lumps. The system is quite sensitive; for example it could detect a 2mm lump at a depth of 5mm. The sensor was more sensitive than previous tactile lump detectors. It was also better than human observers using their fingertips. Such a capability could help in tumor screening, and could augment the sensory information available in telemedicine or minimally invasive surgery.National Science Foundation (U.S.) (Grant 6922551
Shape-independent hardness estimation using deep learning and a GelSight tactile sensor
Hardness is among the most important attributes of an object that humans learn about through touch. However, approaches for robots to estimate hardness are limited, due to the lack of information provided by current tactile sensors. In this work, we address these limitations by introducing a novel method for hardness estimation, based on the GelSight tactile sensor, and the method does not require accurate control of contact conditions or the shape of objects. A GelSight has a soft contact interface, and provides high resolution tactile images of contact geometry, as well as contact force and slip conditions. In this paper, we try to use the sensor to measure hardness of objects with multiple shapes, under a loosely controlled contact condition. The contact is made manually or by a robot hand, while the force and trajectory are unknown and uneven. We analyze the data using a deep constitutional (and recurrent) neural network. Experiments show that the neural net model can estimate the hardness of objects with different shapes and hardness ranging from 8 to 87 in Shore 00 scale
Expertise and Trust-Aware Social Web Service Recommendation
With the increasing number of Web services, the personalized recommendation of Web services has become more and more important. Fortunately, the social network popularity nowadays brings a good alternative for social recommendation to avoid the data sparsity problem that is not treated very well in the collaborative filtering approach. Since the social network provides a big data about the users, the trust concept has become necessary to filter this abundance and to foster the successful interactions between the users. In this paper, we firstly propose a trusted friend detection mechanism in a social network. The dynamic of the users’ interactions over time and the similarity of their interests have been considered. Secondly, we propose a Web service social recommendation mechanism which considers the expertise of the trusted friends according to their past invocation histories and the active user’s query. The experiments of each mechanism produced satisfactory results
Catalytic Properties and Partial Amino Acid Sequence of an Actinomycete Endo-(1→4)-β-D-Xylanase from Chainia Species
An endo-(l→4)-β-D-xylanase from a cellulase-free Chainia strain was substantially purified and subjected to amino acid sequencing. The first forty N-terminal amino acid residues show high homology with endo-xylanases from Bacillus pumilus, B. subtilis, B. circulans, andSchizophylum commune, less homology with endo-xylanases from Aureobasidium sp. andPseudomonas fluorescens, and slight homology, but including a possible catalytic Asp residue, with catalytic domains of endo-xylanases from Clostridium thermocellum,Cryptococcus albidus, and an alkalophilic Bacillus and with a cellobiohydrolase fromCellulomonas fimi. The enzyme attacks substrates as small as xylotetraose and has xylosyltransferase activity. It is most active at pH 6 and 60°C and most stable between pHs 5 and 7
Localization and Manipulation of Small Parts Using GelSight Tactile Sensing
Robust manipulation and insertion of small parts can be challenging because of the small tolerances typically involved. The key to robust control of these kinds of manipulation interactions is accurate tracking and control of the parts involved. Typically, this is accomplished using visual servoing or force-based control. However, these approaches have drawbacks. Instead, we propose a new approach that uses tactile sensing to accurately localize the pose of a part grasped in the robot hand. Using a feature-based matching technique in conjunction with a newly developed tactile sensing technology known as GelSight that has much higher resolution than competing methods, we synthesize high-resolution height maps of object surfaces. As a result of these high-resolution tactile maps, we are able to localize small parts held in a robot hand very accurately. We quantify localization accuracy in benchtop experiments and experimentally demonstrate the practicality of the approach in the context of a small parts insertion problem.National Science Foundation (U.S.) (NSF Grant No. 1017862)United States. National Aeronautics and Space Administration (NASA under Grant No. NNX13AQ85G)United States. Office of Naval Research (ONR Grant No. N000141410047
Two novel hexadepsipeptides with several modified amino acid residues isolated from the fungus isaria
Two new cyclohexadepsipeptides have been isolated from the fungus Isaria. Fungal growth in solid media yielded hyphal strands from which peptide fractions were readily isolable by organic-solvent extraction. Two novel cyclodepsipeptides, isaridin A and isaridin B, have been isolated by reverse-phase HPLC, and characterized by ESI-MS and 1H-NMR. Single crystals of both peptides have been obtained, and their 3D structures were elucidated by X-ray diffraction. The isaridins contain several unusual amino acid residues. The sequences are cyclo(β-Gly-HyLeu-Pro-Phe-NMeVal-NMePhe) and cyclo(β-Gly-HyLeu-β-MePro-Phe-NMeVal-NMePhe), where NMeVal is N-methylvaline, NMePhe N-methylphenylalanine, and HyLeu hydroxyleucine (=2-hydroxy-4-methylpentanoic acid). The two peptides differ from one another at residue 3, isaridin A having an (S)-proline at this position, while β-methyl-(S)-proline (=(2S,3S)-2,3,4,5-tetrahydro-3-methyl-1H-pyrrole-2-carboxylic acid) is found in isaridin B. The solid-state conformations of both cyclic depsipeptides are characterized by the presence of two cis peptide bonds at HyLeu(2)-Pro(3)/HyLeu(2)-β-MePro(3) and NMeVal(5)-NMePhe(6), respectively. In isaridin A, a strong intramolecular H-bond is observed between Phe(4)CO...HNβ-Gly(1), and a similar, but weaker, interaction is observed between β-Gly(1)COHNPhe(4). In contrast, in isaridin B, only a single intramolecular H-bond is observed between β-Gly(1)CO...HNPhe(4)
Sensory Communication
Contains table of contents for Section 2, an introduction, reports on nine research projects and a list of publications.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Grant 2 R01 DC00270National Institutes of Health Grant 1 P01 DC00361National Institutes of Health Grant 2 R01 DC00100National Institutes of Health Grant FV00428National Institutes of Health Grant 5 R01 DC00126U.S. Air Force - Office of Scientific Research Grant AFOSR 90-200U.S. Navy - Office of Naval Research Grant N00014-90-J-1935National Institutes of Health Grant 5 R29 DC0062
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