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Introduction to Haptics
Margaret L. McLaughlin, João P. Hespanha, and Gaurav S. Sukhatme

Haptics refers to the modality of touch and associated sensory feedback. Researchers working in the area are concerned with the development, testing, and refinement of tactile and force feedback devices and supporting softwarethat permit users to sense (“feel”) and manipulate three-dimensional virtual objects with respect to such features as shape, weight, surface textures, and temperature. In addition to basic psychophysical research on human haptics, and issues in machine haptics such as collision detection, force feedback, and haptic data compression, work is being done in application areas such as surgical simulation, medical training, scientific visualization, and assistive
technology for the blind and visually impaired.
How can a device emulate the sense of touch? Let us consider one of the devices from
SensAble Technologies. The 3 DOF (degrees-of-freedom) PHANToM is a small robot arm
with three revolute joints, each connected to a computer-controlled electric DC motor. The
tip of the device is attached to a stylus that is held by the user. By sending appropriate volt-2

Introduction to Haptics Chapter 1

ages to the motors, it is possible to exert up to 1.5 pounds of force at the tip of the stylus, inany direction.
The basic principle behind haptic rendering is simple: Every millisecond or so, thecomputer that controls the PHANToM reads the joint encoders to determine the precise position of the stylus. It then compares this position to those of the virtual objects the user istrying to touch. If the user is away from all the virtual objects, a zero voltage is sent to themotors and the user is free to move the stylus (as if exploring empty space). However, if thesystem detects a collision between the stylus and one of the virtual objects, it drives the motors so as to exert on the user’s hand (through the stylus) a force along the exterior normal to
the surface being penetrated. In practice, the user is prevented from penetrating the virtual
object just as if the stylus collided with a real object that transmits a reaction to the user’shand. Different haptic devices—such as Immersion Corporation’s CyberGrasp—operate
under the same principle but with different mechanical actuation systems for force generation.
Although the basic principles behind haptics are simple, there are significant technicalchallenges, such as the construction of the physical devices (cf. Chapter 4), real-time collision detection (cf. Chapters 2 and 5), simulation of complex mechanical systems for precise
computation of the reaction forces (cf. Chapter 2), and force control (cf. Chapters 3 and 5).
Below we provide an overview of haptics research; we consider haptic devices, applications,
haptic rendering, and human factors issues.

HAPTIC DEVICES
Researchers have been interested in the potential of force feedback devices such as
stylus-based masters like SensAble's PHANToM (Salisbury, Brock, Massie, Swarup, &Zilles, 1995; Salisbury & Massie, 1994) as alternative or supplemental input devices to themouse, keyboard, or joystick. As discussed above, the PHANToM is a small, desk-grounded
robot that permits simulation of single fingertip contact with virtual objects through a thimble or stylus. It tracks the x, y, and z Cartesian coordinates and pitch, roll, and yaw of thevirtual probe as it moves about a three-dimensional workspace, and its actuators communicate forces back to the user's fingertips as it detects collisions with virtual objects, simulating
the sense of touch. The CyberGrasp, from Immersion Corporation, is an exoskeletal device
that fits over a 22 DOF CyberGlove, providing force feedback. The CyberGrasp is used inconjunction with a position tracker to measure the position and orientation of the forearm inthree-dimensional space. (A newly released model of the CyberGrasp is self-contained anddoes not require an external tracker.) Similar to the CyberGrasp is the Rutgers Master II(Burdea, 1996; Gomez, 1998; Langrana, Burdea, Ladeiji, & Dinsmore, 1997), which has an
actuator platform mounted on the palm that gives force feedback to four fingers. Position
tracking is done by the Polhmeus Fastrak.
Alternative approaches to haptic sensing have employed vibrotactile display, whichapplies multiple small force vectors to the fingertip. For example, Ikei, Wakamatsu, and Fu-

Representative Applications of Haptics

3

kuda (1997) used photographs of objects and a contact pin array to transmit tactile sensations of the surface of objects. Each pin in the array vibrates commensurate with the local
intensity (brightness) of the surface area, with image intensity roughly correlated with theheight of texture protrusions. There is currently a joint effort underway at MIT and Carnegie
Mellon (Srinivasan, 2001) to explore the incorporation of microelectromechanical systems(MEMS) actuator arrays into haptic devices and wearables. Researchers at the University of
Wisconsin are experimenting with tactile strips containing an array of sensors that can be
attached to various kinds of force-feedback devices (Tyler, 2001). Howe (1994) notes thatvibrations are particularly helpful in certain kinds of sensing tasks, such as assessing surface
roughness or detecting system events (for example, contact and slip in manipulation control).
Researchers at the Fraunhofer Institute for Computer Graphics in Darmstadt have developed a glove-like device they call the ThermoPad, a haptic temperature display based on
Peltier elements and simple heat transfer models. They are able to simulate not only the “environmental” temperature but also the sensation of heat or cold one experiences when grasping or colliding with an object. At the University of Tsukuba in Japan, Iwata, Yano, andHashimoto (1997) are using the HapticMaster, a 6 DOF device with a ball grip that can be
replaced by various real tools for surgical simulations and other specialized applications. A
novel type of haptic display is the Haptic Screen (Iwata, Yano, and Hashimoto, 1997), a
device with a rubberized elastic surface with actuators, each with force sensors, underneath.
The surface of the Haptic Screen can be deformed with the naked hand. An electromagnetic
interface couples the ISU Force Reflecting Exoskeleton (Luecke & Chai, 1997), developed
at Iowa State University, to the operator's two fingers, eliminating the burdensome heavinessusually associated with exoskeletal devices. Researchers at the University of California,
Berkeley (Chapter 4, this volume) developed a high performance 3 DOF hand-scale haptic
interface. Their device exhibits high stiffness due to a 10-link design with three closed kinematic loops and a direct-drive mechanical system that avoids transmission elements.
Finally, there is considerable interest in 2D haptic devices. For example, Pai and Reissell at the University of British Columbia have used the Pantograph 2D haptic interface, a
two-DOF force-feedback planar device with a handle the user moves like a mouse, to feelthe edges of shapes in images (Pai & Reissell, 1997). A new 2D haptic mouse from Immersion Corporation is based on optical technology and is free-moving rather than tethered to abase. Other 2D devices, like the Moose, Sidewinder, and the Wingman mouse, are described
below in the section on “Assistive Technology for the Blind and Visually Impaired.”

REPRESENTATIVE APPLICATIONS OF HAPTICS

Surgical Simulation and Medical Training
A primary application area for haptics has been in surgical simulation and medical
training. Langrana, Burdea, Ladeiji, and Dinsmore (1997) used the Rutgers Master II haptic

4Introduction to Haptics Chapter 1

device in a training simulation for palpation of subsurface liver tumors. They modeled tumors as comparatively harder spheres within larger and softer spheres. Realistic reaction
forces were returned to the user as the virtual hand encountered the “tumors,” and thegraphical display showed corresponding tissue deformation produced by the palpation. Finite Element Analysis was used to calculate the reaction forces corresponding to deformationfrom experimentally obtained force/deflection curves. Researchers at the Universidade Catolica de Brasilia-Brasil (D’Aulignac & Balaniuk, 1999) have produced a physical simulation
system providing graphic and haptic interfaces for an echographic examination of the humanthigh, using a spring damper model defined from experimental data. Machaco, Moraes andZuffo (2000) have used haptics in an immersive simulator of bone marrow harvest for transplant. Andrew Mor of the Robotics Institute at Carnegie Mellon (Mor, 1998) employed the
PHANToM in conjunction with a 2 DOF planar device in an arthroscopic surgery simulation. The new device generates a moment measured about the tip of a surgical tool, thus providing more realistic training for the kinds of unintentional contacts with ligaments and fibrous membranes that an inexperienced resident might encounter. At Stanford, Balaniuk andCosta (2000) have developed a method to simulate fluid-filled objects suitable for interactive
deformation by “cutting,” “suturing,” and so on. At MIT, De and Srinivasan (1998) havedeveloped models and algorithms for reducing the computational load required to generatevisual rendering of organ motion and deformation and the communication of forces back to
the user resulting from tool-tissue contact. They model soft tissue as thin-walled membranes
filled with fluid. Force-displacement response is comparable to that obtained in in vivo experiments. At Berkeley, Sastry and his colleagues (Chapter 13, this volume) are engaged in ajoint project with the surgery department of the University of California at San Francisco andthe Endorobotics Corporation to build dexterous robots for use inside laparoscopic and endoscopic cannulas, as well as tactile sensing and teletactile display devices and masters for
surgical teleoperation (2001). Aviles and Ranta of Novint Technologies have developed the
Virtual Reality Dental Training System dental simulator (Aviles & Ranta, 1999). They employ a PHANToM with four tips that mimic dental instruments; they can be used to explore
simulated materials like hard tooth enamel or dentin. Giess, Evers, and Meinzer (1998) integrated haptic volume rendering with the PHANToM into the presurgical process of classifying liver parenchyma, vessel trees, and tumors. Surgeons at the Pennsylvania State University School of Medicine, in collaboration with Cambridge-based Boston Dynamics, used two
PHANToMs in a training simulation in which residents passed simulated needles throughblood vessels, allowing them to collect baseline data on the surgical skill of new trainees.
Iwata, Yano, and Hashimoto (1998) report the development of a surgical simulator with a“free form tissue” which can be “cut” like real tissue. There are few accounts of any systematic testing and evaluation of the simulators described above. Gruener (1998), in one of the
few research reports with hard data, expresses reservations about the potential of haptics inmedical applications; he found that subjects in a telementoring session did not profit fromthe addition of force feedback to remote ultrasound diagnosis.

Representative Applications of Haptics

5

Museum Display
Although it is not yet commonplace, a few museums are exploring methods for 3D
digitization of priceless artifacts and objects from their sculpture and decorative arts collections, making the images available via CD-ROM or in-house kiosks. For example, the Canadian Museum of Civilization collaborated with Ontario-based Hymarc to use the latter's ColorScan 3D laser camera to create three-dimensional models of objects from the museum's
collection (Canarie, Inc., 1998; Shulman, 1998). A similar partnership was formed between
the Smithsonian Institution and Synthonic Technologies, a Los Angeles-area company. At
Florida State University, the Department of Classics has worked with a team to digitize
Etruscan artifacts using the RealScan 3D imaging system from Real 3D (Orlando, Florida),
and art historians from Temple University have collaborated with researchers from the Watson Research Laboratory's visual and geometric computing group to create a model ofMichaelangelo's Pieta, using the Virtuoso shape camera from Visual Interface (Shulman,
1998).
Few museums have yet explored the potential of haptics to allow visitors access to
three-dimensional museum objects such as sculpture, bronzes, or examples from the decorative arts. The “hands-off” policies that museums must impose limit appreciation of threedimensional objects, where full comprehension and understanding rely on the sense of touch
as well as vision. Haptic interfaces can allow fuller appreciation of three-dimensional objects
without jeopardizing conservation standards, giving museums, research institutes, and otherconservators of priceless objects a way to provide the public with a vehicle for object exploration in a modality that could not otherwise be permitted (McLaughlin, Goldberg, Ellison,& Lucas, 1999). At the University of Southern California, researchers at the Integrated Media Systems Center (IMSC) have digitized daguerreotype cases from the collection of the
Seaver Center for Western Culture at the Natural History Museum of Los Angeles County
and made them available at a PHANToM-equipped kiosk alongside an exhibition of the“real” objects (see Chapter 15, this volume). Bergamasco, Jannson and colleagues (Jansson,2001) are undertaking a “Museum of Pure Form”; their group will acquire selected sculptures from the collections of partner museums in a network of European cultural institutions
to create a digital database of works of art for haptic exploration.
Haptics raises the prospect of offering museum visitors not only the opportunity to examine and manipulate digitized three-dimensional art objects visually, but also to interact
remotely, in real time, with museum staff members to engage in joint tactile exploration of
the works of art such that someone from the museum’s curatorial staff can interact with astudent in a remote classroom and together they can jointly examine an ancient pot or bronze
figure, note its interesting contours and textures, and consider such questions as “What is themark at the base of the pot?” or “Why does this side have such jagged edges?” (Hespanha,
Sukhatme, McLaughlin, Akbarian, Garg, & Zhu, 2000; McLaughlin, Sukhatme, Hespanha,
Shahabi, Ortega, & Medioni, 2000; Sukhatme, Hespanha, McLaughlin, Shahabi, & Ortega,
2000).

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Introduction to Haptics Chapter 1

Painting, Sculpting, and CAD
There have been a few projects in which haptic displays are used as alternative input
devices for painting, sculpting, and computer-assisted design (CAD). Dillon and colleagues(Dillon, Moody, Bartlett, Scully, Morgan, & James, 2000) are developing a “fabric language” to analyze the tactile properties of fabrics as an information resource for haptic fabricsensing. At CERTEC, the Center of Rehabilitation Engineering in Lund, Sweden, Sjostrom(Sjostrom, 1997) and his colleagues have created a painting application in which the
PHANToM can be used by the visually impaired; line thickness varies with the user's force
on the fingertip thimble and colors are discriminated by their tactual profile. At Dartmouth,
Henle and Donald (1999) developed an application in which animations are treated as palpable vector fields that can be edited by manipulation with the PHANToM. Marcy, Temkin,
Gorman, and Krummel (1998) have developed the Tactile Max, a PHANToM plug-in for
3D Studio Max. Dynasculpt, a prototype from Interval Research Corporation (Snibbe,
Anderson, & Verplank, 1998) permits sculpting in three dimensions by attaching a virtual
mass to the PHANToM position and constructing a ribbon through the path of the mass
through the 3D space. Gutierrez, Barbero, Aizpitarte, Carrillo, and Eguidazu (1998) haveintegrated the PHANToM into DATum, a geometric modeler. Objects can be touched,
moved, or grasped (with two PHANToMs), and the assembly/disassembly of mechanicalobjects can be simulated.

Visualization
Haptics has also been incorporated into scientific visualization. Durbeck, Macias,
Weinstein, Johnson, and Hollerbach (1998) have interfaced SCIrun, a computation software
steering system, to the PHANToM. Both haptics and graphics displays are directed by the
movement of the PHANToM stylus through haptically rendered data volumes. Similar systems have been developed for geoscientific applications (e.g., the Haptic Workbench, Veldkamp, Truner, Gunn, and Stevenson, 1998). Green and Salisbury (1998) have produced aconvincing soil simulation in which they have varied parameters such as soil properties,
plow blade geometry, and angle of attack. Researchers at West Virginia University (Van
Scoy, Baker, Gingold, Martino, & Burton, 1999) have applied haptics to mobility training.
They designed an application in which a real city block and its buildings could be exploredwith the PHANToM, using models of the buildings created in CANOMA from digital photographs of the scene from the streets. At Interactive Simulations, a San Diego-based company, researchers have added a haptic feedback component to Sculpt, a program for analyzing chemical and biological molecular structures, which will permit analysis of molecularconformational flexibility and interactive docking. At the University of North Carolina,
Chapel Hill (Chapter 5, this volume), 6 DOF PHANToMs have been used for haptic rendering of high-dimensional scientific datasets, including three-dimensional force fields and tetrahedralized human head volume datasets. We consider further applications of haptics toRepresentative Applications of Haptics

7

visualization below, in the section “Assistive Technology for the Blind and Visually Impaired.”

Military Applications
Haptics has also been used in aerospace and military training and simulations. There
are a number of circumstances in a military context in which haptics can provide a useful
substitute information source; that is, there are circumstances in which the modality of touch
could convey information that for one reason or another is not available, not reliably communicated, nor even best apprehended through the modalities of sound and vision. In somecases, combatants may have their view blocked or may not be able to divert attention from adisplay to attend to other information sources. Battlefield conditions, such as the presence ofartillery fire or smoke, might make it difficult to hear or see. Conditions might necessitatethat communications be inaudible (Transdimension, 2000). For certain applications, for example where terrain or texture information needs to be conveyed, haptics may be the mostefficient communication channel. In circumstances like those described above, haptics is analternative modality to sound and vision that can be exploited to provide low-bandwidthsituation information, commands, and threat warning (Transdimension, 2000). In other circumstances haptics could function as a supplemental information source to sound or vision.
For example, users can be alerted haptically to interesting portions of a military simulation,
learning quickly and intuitively about objects, their motions, what persons may interact withthem, and so on.
At the Army’s National Automotive Center, the SimTLC (Simulation Throughout theLife Cycle) program has used VR techniques to test military ground vehicles under simulated battlefield conditions. One of the applications has been a simulation of a distributed
environment where workers at remote locations can collaborate in reconfiguring a single
vehicle chassis with different weapons components, using instrumented force-feedback
gloves to manipulate the three-dimensional components (National Automotive Center,1999). The SIRE simulator (Synthesized Immersion Research Environment) at the Air Force
Research Laboratory, Wright-Patterson Air Force Base, incorporated data gloves and tactile
displays into its program of development and testing of crew station technologies (WrightPatterson Air Force Base, 1997). Using tasks such as mechanical assembly, researchers at
NASA-Ames have been conducting psychophysical studies of the effects of adding a 3 DOF
force-feedback manipulandum to a visual display, noting that control and system dynamics
have received ample research attention but that the human factors underlying successful haptic display in simulated environments remain to be identified (Ellis & Adelstein, n.d.). The
Naval Aerospace Medical Research Laboratory has developed a “Tactile Situation Awareness System” for providing accurate orientation information in land, sea, and aerospace environments. One application of the system is to alleviate problems related to the spatial disorientation that occurs when a pilot incorrectly perceives the attitude, altitude, or motion of his
aircraft; some of this error may be attributable to momentary distraction, reduced visibility,
or an increased workload. Because the system (a vibrotactile transducer) can be attached to a
portable sensor, it can also be used in such applications as extravehicular space exploration

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Introduction to Haptics Chapter 1

activity or Special Forces operations. Among the benefits claimed for integration of haptics
with audio and visual displays are increased situation awareness, the ability to track targets
and information sources spatially, and silent communication under conditions where sound is
not possible or desirable (e.g., hostile environments) (Naval Aerospace Medical ResearchLaboratory, 2000).

Interaction Techniques
An obvious application of haptics is to the user interface, in particular its repertoire ofinteraction techniques, loosely considered that set of procedures by which basic tasks, suchas opening and closing windows, scrolling, and selecting from a menu, are performed(Kirkpatrick & Douglas, 1999). Indeed, interaction techniques have been a popular application area for 2D haptic mice like the Wingman and I-Feel, which work with the Windowsinterface to add force feedback to windows, scroll bars, and the like. For some of theseforce-feedback mice, shapes, textures, and other properties of objects (spring, damping) canbe “rendered” with Javascript and the objects delivered for exploration with the haptic mice
via standard Web pages. Haptics offers a natural user interface based on the human gestural
system. The resistance and friction provided by stylus-based force feedback adds an intuitive feel to such everyday tasks as dragging, sliding levers, and depressing buttons. There
are more complex operations, such as concatenating or editing, for which a grasping metaphor may be appropriate. Here the whole-hand force feedback provided by glove-based
devices could convey the feeling of stacking or juxtaposing several objects or of plucking an
unwanted element from a single object. The inclusion of palpable physics in virtual environments, such as the constraints imposed by walls or the effect of altered gravity on weight,
may enhance the success of a user’s interaction with the environment (Adelstein & Ellis,
2000).
Sometimes too much freedom to move is inefficient and has users going down wrong
paths and making unnecessary errors that system designers could help them avoid by theappropriate use of built-in force constraints that encourage or require the user to do things in
the “right” way (Hutchins & Gunn, 1999). Haptics can also be used to constrain the user’s
interaction with screen elements, for example, by steering him or her away from unproductive areas for the performance of specific tasks, or making it more difficult to trigger procedures accidentally by increasing the stiffness of the controls.

Assistive Technology for the Blind and Visually Impaired
Most haptic systems still rely heavily on a combined visual/haptic interface. This dual
modality is very forgiving in terms of the quality of the haptic rendering. This is because
ordinarily the user is able to see the object being touched and naturally persuades herself thatthe force feedback coming from the haptic device closely matches the visual input. However,in most current haptic interfaces, the quality of haptic rendering is actually poor and, if theRepresentative Applications of Haptics

9

user closes her eyes, she will only be able to distinguish between very simple shapes (such asballs, cubes, etc.).
To date there has been a modest amount of work on the use of machine haptics for theblind and visually impaired. Among the two-dimensional haptic devices potentially useful inthis context, the most recent are the Moose, the Wingman, the I-Feel, and the Sidewinder.
The Moose, a 2D haptic interface developed at Stanford (O’Modhrain & Gillespie, 1998),
reinterprets a Windows screen with force feedback such that icons, scroll bars, and otherscreen elements like the edges of windows are rendered haptically, providing an alternative
to the conventional graphical user interface (GUI). For example, drag-and-drop operations
are realized by increasing or decreasing the apparent mass of the Moose’s manipulandum.
Although not designed specifically with blind users in mind, the Logitech Wingman, developed by Immersion Corporation and formerly known as the “FEELit” mouse, similarly renders the Windows screen haptically in two dimensions and works with the Web as well, allowing the user to “snap to” hyperlinks or feel the “texture” of a textile using a“FeeltheWeb” ActiveX control programmed through Javascript. (The Wingman mouse is
now no longer commercially available). Swedish researchers have experimented, with mixed
results, with two-dimensional haptic devices like the Microsoft Sidewinder joystick in games
devised for the visually impaired, such as “Labyrinth,” in which users negotiate a maze using
force feedback (Johansson & Linde, 1998, 1999).
Among the three-dimensional haptic devices, Immersion’s Impulse Engine 3000 has
been shown to be an effective display system for blind users. Colwell et al. (1998) had blindand sighted subjects make magnitude estimations of the roughness of virtual textures using
the Impulse Engine and found that the blind subjects were more discriminating with respect
to the roughness of texture and had different mental maps of the location of the haptic probe
relative to the virtual object than sighted users. The researchers found, however, that forcomplex virtual objects, such as models of sofas and chairs, haptic information was simply
not sufficient to produce recognition and had to be supplemented with information fromother sources for all users.
Most of the recent work in 3D haptics for the blind has tended to focus on SensAble’s
PHANToM. At CERTEC, the Center of Rehabilitation Engineering in Lund, Sweden, in
addition to Sjöstrom’s painting application, described earlier (Sjöstrom, 1997), a program
has been developed for “feeling” mathematical curves and surfaces, and a variant of thegame “Battleship” that uses force feedback to communicate the different sensations of the“water surface” as bombs are dropped and opponents are sunk. The game is one of the few
that can also be enjoyed by deaf-blind children. Blind but hearing children may play “TheMemory Game,” a variation on “Concentration” based on sound-pair buttons that disappear
tactually when a match is made (Rassmuss-Gröhn & Sjöstrom, 1998).
Jansson and his colleagues at Uppsala University in Sweden have been at the forefrontof research on haptics for the blind (Jannson, 1998; Jansson & Billberger, 1999; Jansson,
Faenger, Konig, & Billberger, 1998). Representive of this work is an experiment reported in
Jansson and Billberger (1999), in which blindfolded subjects were evaluated for speed andaccuracy in identifying virtual objects (cubes, spheres, cylinders, and cones) with the PHANToM and corresponding physical models of the virtual objects by hand exploration. Jansson

10Introduction to Haptics Chapter 1

Jansson and Billberger found that both speed and accuracy in shape identification were significantly poorer for the virtual objects. Speed in particular was affected by virtue of the factthat the exploratory procedures most natural to shape identification, grasping and manipulating with both hands, could not be emulated by the single-point contact of the PHANToM tip.
They also noted that subject performance was not affected by the type of PHANToM interface (thimble versus stylus). However, shape recognition of virtual objects with the PHANToM was significantly influenced by the size of the object, with larger objects being more
readily identified. The authors noted that shape identification with the PHANToM is a considerably more difficult task than texture recognition, in that in the case of the latter a single
lateral sweep of the tip in one direction may be sufficient, but more complex procedures are
required to apprehend shape. In Chapter 9 of this volume Jansson reports on his work withnonrealistic haptic rendering and with the method of successive presentation of increasingly
complex scenes for haptic perception when visual guidance is unavailable.
Multivis (Multimodal Visualization for Blind People) is a project currently being undertaken at the University of Glasgow, which will utilize force feedback, 3D sound rendering, braille, and speech input and output to provide blind users access to complex visual displays. Yu, Ramloll, and Brewster (2000) have developed a multimodal approach to providing blind users access to complex graphical data such as line graphs and bar charts. Among
their techniques are the use of “haptic gridlines” to help users locate data values on thegraphs. Different lines are distinguished by applying two levels of surface friction to them(“sticky” or “slippery”). Because these features have not been found to be uniformly helpful
to blind users, a toggle feature was added so that the gridlines and surface friction could beturned on and off. Subjects in their studies had to use the PHANToM to estimate the x and ycoordinates of the minimum and maximum points on two lines. Both blind and sighted subjects were effective at distinguishing lines by their surface friction. Gridlines, however, weresometimes confused with the other lines, and counting the gridlines from right and left margins was a tedious process prone to error. The authors recommended, based on their observations, that lines on a graph should be modeled as grooved rather than raised (“engraving”
rather than “embossing”), as the PHANToM tip “slips off” the raised surface of the line.
Ramloll, Yu, and their colleagues (2000) note that previous work on alternatives tographical visualization indicates that for blind persons, pitch is an effective indicator of thelocation of a point with respect to an axis. Spatial audio is used to assist the user in tasks
such as detecting the current location of the PHANToM tip relative to the origin of a curve(Ramloll, Yu, et al., 2000). Pitches corresponding to the coordinates of the axes can beplayed in rapid succession to give an “overview” picture of the shape of the curve. Suchglobal information is useful in gaining a quick overall orientation to the graph that purely
local information can provide only slowly, over time. Ramloll et al. also recommend a
guided haptic overview of the borders, axes, and curves—for example, at intersections ofaxes, applying a force in the current direction of motion along a curve to make sure that theuser does not go off in the wrong direction.
Other researchers working in the area of joint haptic-sonification techniques for visualization for the blind include Grabowski and Barner (Grabowski, 1999; Grabowski & Barner, 1998). In this work, auditory feedback—physically modeled impact sound—is inte-Issues in Haptic Rendering

11

grated with the PHANToM interface. For instance, sound and haptics are integrated suchthat a virtual object will produce an appropriate sound when struck. The sound varies depending on such factors as the energy of the impact, its location, and the user’s distance fromthe object (Grabowski, 1999).

ISSUES IN HAPTIC RENDERING

Acquisition of Models
There are several commercial 3D digitizing cameras available for acquiring models ofobjects, such as the ColorScan and the Virtuoso shape cameras mentioned earlier. The latter
uses six digital cameras, five black and white cameras for capturing shape information and
one color camera that acquires texture information that is layered onto the triangle mesh. At
USC’s IMSC one of the approaches to the digitization process begins with models acquired
from photographs, using a semiautomatic system to infer complex 3-D shapes from photographs (Chen & Medioni, 1997, 1999, 2001). Images are used as the rendering primitives
and multiple input pictures are allowed, taken from viewpoints with different position,orientation, and camera focal length. The direct output of the IMSC program is volumetric
but is converted to a surface representation for the purpose of graphic rendering. The
reconstructed surfaces are quite large, on the order of 40 MB. They are decimated with amodified version of a program for surface simplification using quadric error metrics written
by Garland and Heckbert (1997). The LightScribe system (formerly known as the 3Scan
system) incorporates stereo vision techniques developed at IMSC, and the process ofmatching points between images has been fully automated. Other comparable approaches todigitizing museum objects (e.g., Synthonics) use an older version of shape-from-stereo
technology that requires the cameras to be calibrated whenever the focal length or relative
position of the two cameras is changed.
Volumetric data is used extensively in medical imaging and scientific visualization.
Currently the GHOST SDK, which is the development toolkit for the PHANToM, construes
the haptic environment as scenes composed of geometric primitives. Huang, Qu, and Kaufman of SUNY-Stony Brook have developed a new interface that supports volume rendering,based on volumetric objects, with haptic interaction. The APSIL library (Huang, Qu, &Kaufman, 1998) is an extension of GHOST. The Stony Brook group has developed successful demonstrations of volume rendering with haptic interaction from Computer Tomography
data of a lobster, a human brain, and a human head, simulating stiffness, friction, and texture
solely from the volume voxel density. The development of the new interface may facilitate
working directly with the volumetric representations of the objects obtained through view
synthesis methods.
The surface texture of an object can be displacement mapped with thousands of tiny
polygons (Srinivasan & Basdogan, 1997), although the computational demand is such that

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Introduction to Haptics Chapter 1

force discontinuities can occur. More commonly, a “texture field” can be constructed from
2D image data. For example, as described above, Ikei, Wakamatsu, and Fukuda (1997) created textures from images converted to grayscale, then enhanced them to heighten brightness
and contrast, such that the level and distribution of intensity corresponds to variations in theheight of texture protrusions and retractions.
Surface texture may also be rendered haptically through techniques like force perturbation, where the direction and magnitude of the force vector is altered using the local gradientof the texture field to simulate effects such as coarseness (Srinivasan & Basdogan, 1997).
Synthetic textures, such as wood, sandpaper, cobblestone, rubber, and plastic, may also becreated using mathematical functions for the height field (Anderson, 1996; Basdogan, Ho, &Srinivasan, 1997). The ENCHANTER environment (Jansson, Faenger, Konig, & Billberger,
1998) has a texture mapper which can render sinusoidal, triangular, and rectangular textures,
as well as textures provided by other programs, for any haptic object provided by the
GHOST SDK.
In many applications of haptics, it is desirable to be able to explore and manipulate deformable objects as well as rigid-body objects like vases and teapots. One area that IMSC
researchers are beginning to explore is the development of reliable vision-based control systems for robotic applications such as the acquisition of images for 3D modeling. Two topics
that have been identified as crucial for the development of such systems for robotic applications (e.g., 3D and 4D modeling for haptics) are the development of self-calibrating control
algorithms (Hager, Chang, & Morse, 1995) and the use of single-camera image acquisition
systems in feedback control. One can use images of an object taken from multiple viewpoints to construct a 3D model of the object to be used for haptics. To automate the procedure of collecting the multiple views, one needs to have a camera mounted on a computercontrolled robot arm. This is particularly important for constructing 4D models of objects
whose shape is evolving (e.g., a work of art as it is being produced). From a controls perspective the research problem is to build algorithms to position the camera. The desired position can be specified directly in terms of its Cartesian coordinates or indirectly in terms ofdesired locations of parts of the object in the image. The latter falls in the area of visionbased control and is much more interesting, because the use of vision in the feedback loop
allows for great accuracy with not very precise, therefore relatively inexpensive, robotic manipulators.

Latency
The realism of haptic rendering can be adversely affected by slow update rates, as canoccur in the case of the extreme computation time required by real-time rendering of deformable objects, or the delays induced by network congestion and bandwidth limitations in
distributed applications.
Floyd (1999) deals with the issue of computational latency and haptic fidelity in bitmapped virtual environments. In traditional systems with some latency there is a lack of fidelity if, say, the user penetrates a virtual object and the lag is such that there is no immediate feedback of force to indicate that a collision has occurred and that penetration is not pos-

Issues in Haptic Rendering

13

sible. Floyd proposes that the server inform the haptic client when the user has penetrated asurface in the environment, and where that contact occurred. The client uses this information
to offset the coordinate system the user is operating in so that instead of having significantly
penetrated the surface, the user is just within it, computes an appropriate force response, andcaches the constraint implicit in the existence of that surface so that forces to impede further
progress in that direction are computed on the client alone.
Mark and his colleagues (Mark, Randolph, Finch, van Verth, & Taylor, 1996) haveproposed a number of solutions to recurring problems in haptics, such as improving the update rate for forces communicated back to the user. They propose the use of intermediate
representation of force through a “plane and probe” method: A local planar approximation to
the user's hand location is computed when the probe or haptic tool penetrates the plane, andthe force is updated at approximately 1 kHz by the force server, while the application recomputes the position of the plane and updates it at approximately 20 kHz. Balaniuk (1999)
has proposed a buffer model to transmit information to the PHANToM at the necessary rate.
The buffer can also be used to implement a proxy-based calculation of the haptic forces.
Networked virtual reality (VR) applications may require that force and positional data
be transmitted over a communication link between computers where significant and unpredictable delays are the norm, resulting in instability in the haptic system. The potential forsignificant harm to the user exists in such circumstances due to the forces that the haptic devices can generate. Buttolo, Oboe, Hannaford, and McNeely (1996) note that the addition offorce feedback to multiuser environments demands low latency and high collision detection
sampling rates. Local area networks (LANs), because of their low communication delay,
may be conducive to applications in which users can touch each other, but for wide area
networks, or any environment where the demands above cannot be met, Buttolo et al. propose a “one-user-at-a-time” architecture. While some latency can be tolerated in “static”
applications with a single user and no effect of the user's action on the 3D object, in collaborative environments where users make modifications to the environment it is important tomake sure that any alterations from individual clients are coordinated through the server. In
effect the server can queue the users so that only one can modify the object at a time and canlock the object until the new information is uploaded to the server and incorporated into the“official” version of the virtual environment. Then and only then can the next user make amodification. Delay can be tolerated under these conditions because the haptic rendering isdone on a local copy of the virtual environment at each user's station.
Hespanha, McLaughlin, and Sukhatme (Chapter 8, this volume) note that latency is a
critical factor that governs whether two users can truly share a common haptic experience.
They propose an algorithm where the nature of the interaction between two hosts is decided
dynamically based on the measured network latency between them. Users on hosts that arenear each other (low communication latency) are dynamically added to fast local groups. If
the communication latency is high, users are allowed a slower form of interaction where they
can touch and feel objects but cannot exert forces on them. Users within a fast local groupexperience true haptic collaboration since the system is able to resolve the interaction forces
between them quickly enough to meet stability criteria.

14Introduction to Haptics Chapter 1

Fukuda and Matsumoto (Chapter 7, this volume) have also addressed the issue of the
impact of network delay on collaborative haptic environments. They conducted a study of amultiuser environment with force feedback. They found that the performance of the PHANToM is sensitive to network delay, and that their SCES (Sharing Contact Entity's State) solution demonstrated good performance, as compared to taking no countermeasure against delay. Other approaches for dealing with random time delays, including Transmission Line
Modeling and Haptic Dead Reckoning, are considered in Wilson et al. (1999).

Contact Detection
A fundamental problem in haptics is to detect contact between the virtual objects andthe haptic device (a mouse, a PHANToM, a glove, etc.). Once this contact is reliably detected, a force corresponding to the interaction physics is generated and rendered using theprobe. This process usually runs in a tight servo loop within a haptic rendering system. Lin etal. (1998, 1999) have proposed an extensible framework for contact detection that deconstructs the workspace into regions and at runtime identifies the region(s) of potential contacts. The algorithm takes advantage of temporal and spatial coherence by caching the contact geometry from the immediately prior step to perform incremental computations. Mascarenhas et al. (Chapter 5, this volume) report on a recent application of this system to thevisualization of polygonal and scientific datasets. The contact detection problem is well studied in computer graphics. The reader is referred to Held (1995) and to Lin and Gottschalk(1998) for a survey.
Another technique for contact detection is to generate the so-called surface contact
point (SCP), which is the closest point on the surface to the actual tip of the probe. The force
generation can then happen as though the probe were physically at this location rather than
within the object. Existing methods in the literature generate the SCP by using the notion ofa god-object (Zilles & Salisbury, 1995), which forces the SCP to lie on the surface of thevirtual object. A technique which finesses contact point detection using a voxel-based approach to 6 DOF haptic rendering is described in McNeely et al. (1999). The authors use ashort-range force field to repel the manipulated object in order to maintain a minimum separation distance between the (static) environment and the manipulated object. At USC’s
IMSC, the authors are developing algorithms for SCP generation that use information fromthe current contact detection cycle and past information from the contact history to predict
the next SCP effectively. As a first step, we are experimenting with a well-known linear predictor, the Kalman Filter, by building on our prior results in applying similar techniques tothe problem of robot localization (Roumeliotis, Sukhatme, & Bekey, 1999).

Force Feedback
Two requirements drive the force feedback research in haptics: high fidelity renderingand stability. It turns out that these two goals are somewhat conflicting because high fidelity

Issues in Haptic Rendering

15

haptic rendering generally calls for high force-feedback gains that often lead to self-induced
oscillations and instability.
Inspired by electrical networks, Adams and Hannaford (1999) regard the haptic interface as a two-port system terminated on one side by the human operator and on the otherside by the virtual environment (cf. Figure 1-1). The energy exchange between the humanoperator and the haptic interface is characterized by a force Fh and velocity vh , whereas theexchange between the interface and the simulated virtual environment is characterized by aforce Fe and velocity ve . For ideal rendering, the haptic interface should be transparent (inthe sense that Fh = Fe and vh = ve ), but stability requirements generally force the designerof the haptic interface to introduce some haptic distortion.

humanoperatorvh
Fh

hapticinterfaceveFevirtualenvironment

Figure 1-1: Two-port framework for haptic interfaces.

It is generally assumed that a human operator interacting with a haptic interface behaves passively (Hogan, 1989) in the sense that he or she does not introduce energy in thesystem. Since most mechanical virtual environments are also passive, the stability of theoverall system could be guaranteed by simply designing the interface to be passive. However, as observed by Colgate, Grafing, Stanley, and Schenkel (1993), time-sampling candestroy the natural passivity of a virtual environment. In fact, these authors showed that thesmaller the sampling rate, the more energy can be “generated” by a virtual wall.
Several approaches have been proposed to deal with this difficulty. Colgate andSchenkel (1997) determined conditions on the simplest virtual environment (the virtual wall)that guarantee the stability of the haptic interface for any passive human operator. These
conditions reflect the fact that the amount of energy generated by a time-discretized virtual
wall depends on the sampling rate. They also involve the virtual wall’s stiffness and damping
coefficient, posing constraints on the range of stiffness/damping parameters that can be rendered. This range is referred to by Colgate and Brown (1994) as the z-width of the haptic
interface and is an important measure of its performance.
Adams and Hannaford (1999) followed a distinct approach by advocating the introduction of virtual coupling in the haptic interface so as to guarantee the stability of the system
for any continuous-time passive virtual environment, even if its discrete-time version is nolonger passive. The virtual coupling can be designed to provide sufficient energy dissipation
to guarantee the stability of the overall system for any passive virtual environment. This approach decouples the haptic interface control problem from the design of the virtual envi-

16

Introduction to Haptics Chapter 1ronment. Miller, Colgate, and Freeman (1999) extended this work to virtual environmentsthat are not necessarily passive. The drawback of virtual coupling is that it introduces haptic
distortion (because the haptic interface is no longer transparent). Hannaford, Ryu, and Kim(Chapter 3, this volume) present a new method to control instability that depends on the time
domain definition of passivity. They define the “Passivity Observer,” and the “Passivity
Controller,” and show how they can be applied to haptic interface design in place of fixedparameter virtual couplings. This approach minimizes haptic distortion.
The work described above assumes that the human operator is passive, but poses noother constraints on her behavior. This can lead to small z-width, significant haptic distortion, or both. Tsai and Colgate (1995) tried to overcome this by modeling the human as amore general discrete-time linear time-invariant system. They derive conditions for stability
that directly exclude the possibility of periodic oscillations for a virtual environment consisting of a virtual wall. Gillespie and Cutkosky (1996) address the same issue by modeling thehuman as a second order continuous-time system. They conclude that to make the approachpractical, online estimation of the human mechanical model is needed, because the model’s
parameters change from operator to operator and, even with the same operator, from posture
to posture. The use of multiple-model supervisory control (Anderson et al., 1999; Hespanha
et al., 2001; Morse, 1996) to estimate online the operator’s dynamics promises to bring significant advantages to the field, because it is characterized by very fast adaptation to suddenchanges in the process or the control objectives. Such changes are expected in haptics due tothe unpredictability of the human-in-the-loop. In fact, it is shown in Hajian and Howe (1995)
that changes in the parameters of human limb dynamics become noticeable over periods oftime larger than 20 ms.
Although most of the work referenced above focuses on simple prototypical virtualenvironments, a few researchers developed systems capable of handling very complex ones.
Ruspini and Khatib (Chapter 2, this volume) are among these, having developed a generalframework for the dynamic simulation and haptic exploration of complex interaction between generalized articulated virtual mechanical systems. Their simulation tool permits direct “hands-on” interaction with the virtual environment through the haptic interface.

Capture, Storage, and Retrieval of Haptic Data
One of the newest areas in haptics is the search for optimal methods for the description, storage, and retrieval of moving-sensor data of the type generated by haptic devices.
With such techniques we can capture the hand or finger movement of an expert performing askilled movement and “play it back,” so that a novice can retrace the expert’s path, with realistic touch sensation; further, we can calculate the correlation between the two exploratory
paths as time series and determine if they are significantly different, which would indicate aneed for further training. The INSITE system (Faisal, Shahabi, McLaughlin, & Betz, 1999)
is capable of providing instantaneous comparison of two users with respect to duration,
speed, acceleration, and thumb and finger forces. Techniques for recording and playing backraw haptic data (Shahabi, Ghoting, Kaghazian, McLaughlin, & Shanbhag, forthcoming; Shahabi, Kolahdouzan, Barish, Zimmermann, Yao, & Fu, 2001) have been developed for theIssues in Haptic Rendering

17

PHANToM and CyberGrasp. Captured data include movement in three dimensions, orientation, and force (contact between the probe and objects in the virtual environment). Shahabi
and colleagues address haptic data at a higher level of abstraction in Chapter 14, in which
they describe their efforts to understand the semantics of hand actions (see also Eisenstein,
Ghandeharizadeh, Huang, Shahabi, Shanbhag, & Zimmermann, 2001).

Haptic Data Compression
Haptic data compression and evaluation of the perceptual impact of lossy compressionof haptic data are further examples of uncharted waters in haptics research (see Ortega, thisvolume, Chapter 6). Data about the user's interaction with objects in the virtual environment
must be continually refreshed if they are manipulated or deformed by user input. If data are
too bulky relative to available bandwidth and computational resources, there will be improper registration between what the user sees on screen and what he “feels.” Ortega’s work
begins by analyzing data obtained experimentally from the PHANToM and the CyberGrasp,
exploring compression techniques, starting with simple approaches (similar to those used inspeech coding) and continuing with methods that are more specific to the haptic data. One of
two lossy methods to compress the data may be employed: One approach is to use a lower
sampling rate; the other is to note small changes during movement. For example, for certain
grasp motions not all of the fingers are involved. Further, during the approaching and departing phases tracker data may be more useful than the CyberGrasp data. Vector coding may
prove to be more appropriate to encode the time evolution of a multifeatured set of data suchas that provided by the CyberGrasp. For cases where the user employs the haptic device tomanipulate a static object, compression techniques that rely on knowledge of the object may
be more useful than the coding of an arbitrary trajectory in three-dimensional space.

Haptic Collaboration
The many potential applications in industry, the military, and entertainment for force
feedback in multiuser environments, where two or more users orient to and manipulate thesame objects, have led to work such as that of Buttolo and his colleagues (Buttolo, Oboe, &Hannaford, 1997; Buttolo, Hewitt, Oboe, & Hannaford, 1997; Buttolo, Oboe, Hannaford, &McNally, 1996), who as noted above remind us that adding haptics to multiuser environments creates additional demand for frequent position sampling for collision detection andfast update.
It is also reasonable to assume that in multiuser environments, there may be a heterogenous assortment of haptic devices with which users interact with the system. One of ourprimary concerns thus would be to ensure proper registration of the disparate devices withthe 3D environment and with each other. Of potential use in this regard is work by Iwata,
Yano, and Hashimoto (1997) on LHX (Library for Haptics), which is modular software thatcan support a variety of different haptic displays. LHX allows a variety of mechanical configurations, supports easy construction of haptic user interfaces, allows networked applica-

18

Introduction to Haptics Chapter 1

tions in virtual spaces, and includes a visual display interface. The chapter by Hespanha,
McLaughlin, and Sukhatme (Chapter 8, this volume) proposes an architecture for distributed
haptic collaboration with heterogeneous devices.
There have only been a few studies of cooperation/collaboration between users of haptic devices. In a study by Basdogan, Ho and their colleagues (Basdogan, Ho, Slater, & Srinavasan, 1998; Ho, Basdogan, Slater, Durlach, & Srinivasan, 1998), partners at remote locations were assigned three cooperative tasks requiring joint manipulation of 3D virtual objects, such as moving a ring back and forth along a wire while minimizing contact with thewire. Experiments were conducted with visual feedback only, and with both visual and haptic feedback. Both performance and feelings of togetherness were enhanced in the dual modality condition. Performance was best when visual feedback alone was followed by theaddition of haptic feedback rather than vice versa. Durlach and Slater (n.d.) note that factorsthat contribute to a sense of copresence include being able to observe the effect on the environment of actions by one's interlocutors, and being able to work collaboratively with copresent others to alter the environment. Point of view (egocentric vs. exocentric) with respect toavatars may also influence the sense of copresence. Touching, even virtual touching, is believed to contribute to the sense of copresence because of its associations with closeness andintimacy.

HUMAN FACTORS

Human Haptics
The behavior of the human haptic system has been the subject of far more systematic
study than has machine haptics. There are several haptically important dimensions of object
recognition, including texture, hardness, shape, and thermal conductivity (Klatzky, Lederman, & Reed, 1987). Most researchers report that subjects are able to discriminate textures
and to a lesser extent shapes using the haptic sense only. For example, Ballesteros, Manga,
and Reales (1997) reported a moderate level of accuracy for single-finger haptic detection ofraised-line shapes, with asymmetric shapes being more readily discriminated. Hatwell (1995)
found that recall of texture information coded haptically was successful when memorization
was intentional, but not when it was incidental, indicating that haptic information processing
may be effortful for subjects.
Available evidence indicates that haptic information processing in adults involves construal of a stimulus object, such as a texture sample, as coordinates on a set of underlying
perceptual dimensions, like “hard-soft” or “rough-smooth.” Developmental studies by Berger and Hatwell (1993, 1995) have shown that in discriminating texture samples varying
with respect to the density and hardness dimensions, older subjects were less likely than
younger ones to make global assessments of the stimuli and more likely to invoke the separate dimensions as judgment criteria. Hughes and Jansson (1994) note that texture gradients,

Human Factors

19

like textures, can be multidimensional and suggest candidate dimensions such as variationsin the size, height, and shape of elements. Hollins, Faldowski, Rao, and Young (1993)
passed samples of 17 textures over the fingertips of subjects whose view of the samples was
restricted. The subjects sorted the texture samples into categories based on similarity, andthen rated the samples against a series of scales measuring well-established perceptual dimensions such as roughness and hardness, and several other less-well studied potential dimensions such as “slippery-sticky.” Co-occurrence data from the sorting task were converted
to dissimilarities and submitted to a multidimensional scaling analysis. The researchers reported that there were two clear, orthogonal perceptual dimensions, “rough-smooth” and“soft-hard,” underlying the classification of samples and speculated about a possible third
dimension, “springiness.”
Hughes and Jansson (1994) lament the inadequacy of embossed maps and other devices intended to communicate information through the sense of touch, a puzzling state ofaffairs insomuch as perception by active touch (purposeful motion of the skin surface relative to the surface of some distal object) appears to be comparatively accurate, and evenmore accurate than vision in apprehending certain properties such as smoothness (Hughes &Jansson, p. 302). The authors note in their critical review of the literature on active-passive
equivalence that active and passive touch (as when a texture is presented to the surface of thefingers, see Hollins et al., 1993) have repeatedly been demonstrated by Lederman and hercolleagues (Lederman, 1985; Lederman, Thorne, & Jones, 1986; Loomis & Lederman,
1986) to be functionally equivalent, in that touch modality does not seem to account for asignificant proportion of the variation in judgments of such basic dimensions as roughness,even though the two types of touch may lead to different sorts of attributions (respectively,
about the texture object and about the cutaneous sensing surface) and motor information
should clearly be useful in assessing the size and distribution of surface protrusions and retractions. Active and passive touch are more likely to be equivalent in certain types of perceptual tasks; active touch should be less relevant to judgments of “hardness” than it is toassessments of “springiness.” Such findings should be of interest to those working with machine haptics, as most of the application development in this field involves using the displays
under conditions of active rather than passive touch.
Some researchers have reported that shape and texture recognition improve with theaddition of vision, although there is not uniform agreement as to the extent of the similarity
between haptic and visual information processing. Balakrishnan, Klatzky, and Loomis(1989), although reporting that length-distortion effects (attributing greater distances between two points as a path between them becomes more winding) were less pronounced under visual path tracing than had been found in previous experiments using haptic exploration, nonetheless concluded that the encoding processes are similar in the two domains.
Klatzky, Lederman, and Reed (1987), however, concluded after a series of experiments thatthe encoding pathways are fundamentally different in the haptic and visual systems, such thatthe visual system is more oriented to the discrimination of shape and the haptic system tosubstance. Heller's work (1982) suggests that the addition of visual information about“where the hand is” (as opposed to what the surface texture looks like) is the critical contributory factor in any improved performance arising from bimodal information acquisition.

20

Introduction to Haptics Chapter 1

Work reported by Lederman, Thorne, and Jones (1986) indicates that the dominance of one
system over the other in texture discrimination tasks is a function of the dimension of judgment being employed. In making judgments of density, the visual system tends to dominate,while the haptic system is most salient when subjects are asked to discriminate textures on
the basis of roughness.
Lederman, Klatzky, Hamilton, and Ramsay (1999) studied the psychophysical effectsof haptic exploration speed and mode of touch on the perceived roughness of metal objects
when subjects used a rigid probe, not unlike the PHANToM stylus (see also Klatzky andLederman, Chapter 10, this volume). In earlier work, Klatzky and Lederman found that subjects wielding rigid stick-like probes were less effective at discriminating surface textures
than with the bare finger. In a finding that points to the importance of tactile arrays to haptic
perception, the authors noted that when a subject is actively exploring an object with thebare finger, speed appears to have very little impact on roughness judgments, because subjects may have used kinesthetic feedback about their hand movements; however, when arigid probe is used, people should become more reliant on vibrotactile feedback, since thedegree of displacement of fingertip skin no longer is commensurate with the geometry of thesurface texture.

Machine Haptics
Psychophysical studies of machine haptics are now beginning to accumulate. Experiments performed by von der Heyde and Hager-Ross (1998) have produced classic perceptual
errors in the haptic domain: For instance, subjects who haptically sorted cylinders by weight
made systematic errors consistent with the classical size-weight illusion. Experiments by
Jansson, Faenger, Konig, and Billberger (1998) on shape sensing with blindfolded sighted
observers were described above. Ernst and Banks (2001) reported that although vision usually “captures” haptics, in certain circumstances information communicated haptically (via
two PHANToMs) assumes greater importance. They found that when noise is added to visual data, the haptic sense is invoked to a greater degree. Ernst and Banks concluded that theextent of capture by a particular sense modality is a function of the statistical reliability of
the corresponding sensory input.
Kirkpatrick and Douglas (1999) argue that if the haptic interface does not support certain exploratory procedures, such as enclosing an object in the case of the single-point
PHANToM tip, then the quick grasping of shape that enclosure provides will have to bedone by techniques that the interface does support, such as tracing the contour of the virtual
object. Obviously, this is slower than enclosing. The extent to which the haptic interface
supports or fails to support exploratory processes contributes to its usability. Kirkpatrick andDouglas evaluated the PHANToM interface’s support for the task of shape determining,
comparing and contrasting its usability in three modes: vision only; haptics only; and haptics
and vision combined, in a non-stereoscopic display. When broad exploration is required forquick object recognition, haptics alone is not likely to be very useful when the user is limited
to a single finger whose explorations must be recalled and integrated to form an overall impression of shape. Vision alone may fail to provide adequate depth cues (e.g., the curvedReferences

21

shape of a teapot). Kirkpatrick and Douglas assert that the effect of haptics and vision is not
additive and that the combination of them would provide a result exceeding what an additivemodel might predict.
Kirkpatrick and Douglas (1999) also report that among the factors that influence thespeed of haptic recognition of objects are the number of different object attributes that canbe perceived simultaneously and the number of fingers that are employed. This work suggests that object exploration with devices like the PHANToM, which offer kinesthetic but
not cutaneous feedback, will yield suboptimal results with respect both to exploration speed
and accuracy when compared to the bare hand. It further suggests that speed and accuracy
may improve with additional finger receptors.
With the advent of handheld devices and the possibility of the incorporation of haptics
into such devices, it is becoming increasingly important to determine just how small a haptic
effect can be perceived. Dosher, Lee, and Hannaford (Chapter 12, this volume) report thatusers can detect haptic effects whose maximum force is about half the measured Coulomb
friction level of the device and about one-third the measured static friction level. They notethat their results can be expected to vary by device and that it remains to be seen whether or
not a measurable effect is necessarily one that can help users accomplish their tasks.
Srinivasan and Basdogan (1997) note the importance of other modalities in haptic perception (e.g., sounds of collision with objects, etc.). They report that with respect to object
deformation, visual sensing dominates over proprioception and leads to severe misjudgmentsof object stiffness if the graphic display is intentionally skewed (Srinavasan, Beauregard, &Brock, 1996). Sound appears to be a less important perceptual mediator than vision. In an
unpublished study by Hou and Srinivasan, reported in Srinivasan and Basdogan (1997), subjects navigating through a maze were found to prefer large visual-haptic ratios and small
haptic workspaces. Best results were achieved in the dual-modality condition, followed byhaptic only and then vision only. It is apparent that the relative contribution of visual andhaptic perception will vary as a function of task, but it is also apparent, as Srinivasan andBasdogan conclude, that the inadequacies of force-feedback display (e.g., limitations ofstiffness) can be overcome with appropriate use of other modalities. In Chapter 11 Jeongand Jacobson consider the question of how effective haptic and auditory displays are whencombined, whether or not they interfere with one another, and how a user’s previous experience with a modality affects the success of the integration and the efficacy of the multimodal
display.

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