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Electric Field Pretouch:
Towards Mobile Manipulation
Brian Mayton
Department of Computer Science
and Engineering
University of Washington
Seattle, WA 98195-2350
Email: bmayton@cs.washington.edu
Eric Garcia
Department of Electrical Engineering
University of Washington
Seattle, WA 98195-2500
Email: eric.garcia@gmail.com
Louis LeGrand, Joshua R. Smith
Intel Research Seattle
1100 NE 45th Street
Seattle, WA 98105
Email: lllegrand@intel.com
joshua.r.smith@intel.com
Abstract— Pretouch sensing is longer range than contact, but
shorter range than vision. Our hypothesis is that closed loop
feedback based on short range but non-contact measurements
can improve the reliability of manipulation. Such feedback may
be particularly useful in mobile manipulation and personal
robotics: mobility increases the robot’s position uncertainty,
which the additional feedback can overcome. In natural human
environments in which Personal Robots must function, objects
may be moved by people. By the time the hand of a fetching robot
reaches the target, it may have moved. For human to robot hand
off, the human’s hand will not generally be perfectly stationary,
so feedback can help aligh the robot’s hand to the human’s hand.
This paper describes a series of experiments with Electric Field
Pretouch that begin to explore how pretouch sensing can be used
to aid manipulation.
I. INTRODUCTION
This paper presents a grasping system that is guided at
short range by a sense that is not common in robotics, E-Field
Pretouch. We describe two sets of experiments. The first set
of experiments involves human-to-robot and robot-to-human
handoff, including the use of EF Pretouch to detect whether or
not a human is also touching an object that the robot is holding,
which we call the “co-manipulation state.” In the second set
of experiments, the robot picks up standalone objects. We
describe a number of techniques that servo the arm and fingers
in order to both collect relevant geometrical information, and
to actually perform the manipulation task (by moving the hand
to the object, aligning with it, and preshaping the fingers to
the object before grasping). A challenge for future work is
to move from these individual examples to more general and
explicit state estimation.
Our previous work on Electric Field Pretouch[11, 13] has
demonstrated servoing of robot arms to align with sensed
objects, and, in separate experiments, pre-shaping of fingers
(on an isolated hand, not mounted to an arm) to prepare for
grasping. This paper integrates these two formerly separate
capabilities, and shows that they can be combined for grasping.
Further, this paper explores several strategies for aligning the
arm with the object (in both angle and translation) that point to
a more general problem of doing explicit state estimation from
pretouch measurements made in the course of manipulation.
Fig. 1. Photograph of Electric Field Pretouch manipulation system. The
system includes a WAM arm and Barrett Hand. Each fingertip contains one
of our custom Electric Field Sensing boards and 4 sensing electrodes. Another
EF sensing board with one transmit electrode is built into the palm. The palm
board also serves as a hub for aggregating sensor data from the fingertips. The
palm also contains a camera, which was not used in the experiments reported
in this paper. The googly eyes also were not used. A polar coordinate system
used for mapping the region of where the grasping procedure succeeds is
visible beneath an example object (a can).
A. Motivation
Our hypothesis is that closed loop feedback based on short
range but non-contact measurements in robotic hands can im-
prove the reliability of manipulation. Such feedback is partic-
ularly relevant for mobile manipulation and personal robotics:
mobility may increase the robot’s position uncertainty, which
the additional feedback may overcome. Imagine a scenario
in which the robot is attempting to pick up an object that is
moving relative to the robot, perhaps because the robot is in
motion. In the natural human environments in which Personal
Robots must function, objects may be moved by people. By
the time the hand of a fetching robot reaches a target location
selected by vision (using techniques such as those described
in [9]), it may have moved. Visual servoing[3][6] with a
palm camera can allow the arm to maintain alignment with
an object, but this technique will not be feasible at very
close range. It is at close range that the configuration of
Robotics: Science and Systems 2009
workshop on Mobile Manipulation in Human Environments
the manipulator with respect to the object becomes a critical
determinant of the success or failure of the grasp. Vision with
a camera not mounted in the palm will encounter occlusion
problems when the hand closely approaches the object.
Human-to-robot hand off is another scenario relevant to
personal robotics in which the object may not be stationary
relative to the robot hand; feedback should help align the
robot’s hand to a human’s moving hand.
II. REL ATED WORK
Recently Hsiao et al.[2] described an optical pretouch sys-
tem, with optical emitters and detectors built into the fingers of
a Barrett Hand. An advantage of optical pretouch over Electric
Field Pretouch is that it works with a wider range of materials.
However, unlike EF Pretouch, it depends on surface color
and texture and is challenged by specularity. For example,
one of the failure cases described in [2], a highly specular
metal object, would be ideal for Electric Field Pretouch. We
believe that combining more than one pretouch modality has
the potential to be very effective.
Hsiao et al. demonstrated a reactive controller for grasping
using optical pretouch sensors. Hsiao’s reactive controller
performed wrist orientation servoing based on asymmetry of
finger joint angles, a technique that we also adopt in this paper,
and begin extending. In all the experiments reported here, the
e-field sensor values can affect the entire arm state, not just the
fingers and wrist. Many of the techniques for using pretouch
sensor information presented in this paper can be applied in
the context of other pretouch sensors like optical.
Another relevant point of comparison to the present paper is
visual servoing.[3]. While the control and dynamics have much
in common with the work presented here, a major difference is
the reliance on computer vision, which provides lower update
rates. Also, it is difficult to use vision at the very close ranges
at which pretouch works.
Capacitive sensing has been explored in robotics in various
contexts. In addition to our prior work, recently Solberg,
Lynch, and MacIver presented fish-inspired underwater robot
capable of localizing object using electric field sensing.[12]
For several earlier instances of above-water capacitive sensing
for robotics, please see [7], [8], [4] and [10]. None of these
schemes were targeted specifically at sensing for manipulation.
We are not aware of prior work on co-manipulation state
detection.
III. APPAR ATUS AND METH ODS
A. Sensing
In this subsection we describe the physics of electric field
sensing, as well as the sensor hardware.
1) Physics of Electric Field Sensing: In Electric Field Sens-
ing, an AC signal is applied to a transmit electrode (labeled
TXANT in the figure). This induces an AC current in the
receive electrode, labeled RCV ANT, which is amplified and
processed by the Analog Front End (a current amplifier, which
measures current induced at the receiver) and subsequent
signal processing (in our case, an analog to digital converter
and signal processing software in a microcontroller). The
sensed object, drawn as a third antenna, modifies the current
induced in the reader by interacting with the transmit and
receive antenna.
In the case that the object is grounded, then bringing
the sensed object closer to transmit-receive pair “steals”
displacement current that would have otherwise reached the
receiver, decreasing the measured sensor value. At the opposite
extreme, bringing a floating object (i.e. with no coupling
to ground) near the transmit-receive pair causes additional
displacement current to reach the receive electrode, increasing
the measured sensor value. The floating object is “short circuit-
ing” the transmit electrode to the receive electrode (literally
shortening the distance through the air through which the field
has to propagate), while the grounded object is acting as a
shunt to ground. To re-iterate, when an object is brought near a
transmit-receive electrode pair, the sensor values can go either
up or down, depending on the coupling of the object to ground.
If the ground coupling can vary in practice, then clearly it is
crucial to understand, as it can drastically affect the sensor
behavior.
Note that at the frequencies at which we are operating, the
human is typically well-coupled to ground, often through the
shoes. (We are in the regime of AC coupling, so although there
is typically no DC electrical path from your body to ground,
there is usually a relatively good AC path to ground.) This
in turn means that conductive objects that a person holds or
touches are also relatively well-grounded.
Another important general property of the E-Field sensors
is lengthscale. The range of the sensors is determined by the
transmit-receive spacing. We will make use of this, perform-
ing long-range measurements to guide gross arm movements
such as aligning the hand with the object, and shorter range
measurements for finer finger adjuestments.
The sensors detect both conductive objects, and non-
conductive objects whose dielectric constants differ from that
of the air. Only the surface of a conductive object affects the
sensors. For dielectrics, however, the entire bulk of the material
affects the sensors. For this reason, the net dielectric constant
of an object is proportional to density. Figure 2 compares the
response of various dielectric objects to a conductor. Some of
the dielectric objects work quite well. The ones that do not
are very low density.
2) Electric Field Sensing Instrumentation: To enable the
robotic hand and arm to perform electric field sensing, the
three fingers of the BarrettHand were replaced with 3D-printed
plastic replacements containing custom sensor boards and
electrodes (both our own design). Figure 3 shows a sensor
board installed on the robot. The sensor hardware is entirely
contained within the plastic finger. An additional sensor board
was also placed in the palm of the hand to provide another
transmit channel. None of the sensing hardware described in
this paper has been published before; in our prior work, we
used sensor boards that were too large to be mounted in the
fingers; in the prior work, only the electrodes were in the
fingers. With the old hardware, it was impractical to mount
Fig. 2. Response of electric field sensors to non-conductive materials
(“dielectrics”). For dielectric objects, sensor response is typically proportional
to object density, which could be another useful pre-touch cue.
Fig. 3. Electric field sensor board installed in robotic fingertip.
the EF Sensing hand (and sensors) on the WAM arm.
3) Electrode design: The complete hand setup is capable
of making 18 distinct measurements, each consisting of a
transmit/receive pair. The receive electrodes are located at the
tips of the fingers, and are split into left and right receivers.
The placement of transmit electrode that is used determines the
range of the measurement. Two transmitters are located along
the inner surface of each finger. The one closest to the receivers
provides a short-range measurement with high resolution but is
limited to sensing about two centimeters away. The other trans-
mitter in the finger, which is farther away from the receivers,
provides mid-range measurements, with a range of about five
centimeters. The transmit electrode in the palm can also be
used to transmit to the fingertips, and provides a long range
measurement, about 10 to 15 centimeters. (For each finger,
there is a long, medium, and short range transmitter...each
finger has a left and right receiver, yielding 6 measurements
per finger x 3 fingers = 18 measurements total.)
Figure 4 shows the electrodes in the fingers. Figures 5 and 6
show calculated iso-signal surfaces generated by the mid-range
Left Receive
Short Range
Transmit
Mid-Range
Transmit
Right Receive
Fig. 4. Finger electrodes.
Fig. 5. Iso-signal surfaces for the hand’s mid-range measurements.
and long-range electodes for a small test object. The iso-signal
surfaces are computed by simulating the effect of a particular
small test object on the sensors. An iso-signal surface is a set
of locations of the test object at which the sensors return a
particular single value.
B. Actuation
1) Reactive control of WAM arm: The WAM arm is
controlled by a real-time Linux PC (“wambox”) that pro-
vides updates at 500Hz. The sensing, inverse kinematics, and
application logic execute on another PC (“wamclient”) that
connects to wambox by a network interface. Because of the
time requirements for sensing and IK computations, wamclient
provides updated commands to wambox at only around 20Hz.
It is necessary to upsample from this slow, irregular set of
commands to generate a set of smooth, regular commands at
500Hz.
Most of the existing WAM arm drivers upsample arm
commands to 500Hz, but they require pre-planned trajectories.
Given the relatively tight integration of sensing and control in
our system, planned trajectories are generally not available: the
next arm target location is not knowable before the next sensor
value is collected. Pre-planned trajectories make smoothing
Fig. 6. Iso-signal surfaces for the hand’s long-range measurements.
Z
X
Y
Fig. 7. The Barrett Hand, with coordinate axis.
relatively easy, since only interpolation is required; for dy-
namically generated trajectories, smoothing would appear to
require extrapolation.
Our solution is to introduce a small amount of lag between
the commands issued by wamclient and those sent by wambox
to the arm. This reduces extrapolation to interpolation between
previously executed commands and a future command that
can be known because of the lag time. The interpolation
is performed with cubic splines. The more lag allowed, the
smoother the resulting trajectories. Of course the lag intro-
duces undesirable latency, so we use a minimal lag value, on
the order of 50ms. The smoothing scheme is also failsafe,
meaning that if additional targets stop coming, the motion
stops at the last target received.
2) Barrett Hand: The Barrett Hand has three fingers, and
each finger has two links actuated by a single motor. Finger
3 is fixed to the palm, and the spread angle to fingers 1 and 2
is synchronously actuated by a fourth motor. Figure 7 shows
the Barrett hand and a local Cartesian coordinate system fixed
to the hand, which will be referred to as the “hand frame.”
C. Control
We have explored several different control strategies for
the fingers, wrist, and arm. Eventually we expect these to be
subsumed by more general and principled approaches.
1) Finger preshaping using mid/short range sensors: In
many grasping tasks, ensuring that the fingers contact the
object simultaneously can improve the probability of success-
fully grasping the object. Without simultaneous contact, the
first finger that contacts the object may push it out of the
way, or knock it over. Simultaneous contact can be achieve
by preshaping the hand to the object, i.e. commanding all of
the fingers to move close to the object, without touching it.
Implementing preshaping control requires a way to estimate
the distance of the finger to the object. Electric field sensors
in the fingers of the grasper can provide this kind of estimate
For all of the objects that we tested, the mid and short range
sensors have a monotonic relationship between object distance
and sensor value. Therefore, given a particular object, an e-
field sensor reading set point can act as a proxy for a distance
set point. In practice, it is possible to use the same sensor
value set point for all objects. This is because the variation
of sensor reading at a given distance for different objects is
small compared to the overall range of the sensor readings.
Thus the variation in distance at a given sensor reading for
different objects is small is well. Further, by increasing the
sensitivity of the sensor readings to distance, (e.g. short range
sensors vs mid range sensors) the distance errors at a given
reading are decreased.
The finger preshaping control loop used in our experiments
is straight forward. The current to the finger motor (and
resulting torque) is set by a PID closed loop controller that
acts to reduce the magnitude of the error between the sensor
reading, and the sensor set point. Each finger is controlled
independently.
2) Arm servoing using long range e-field sensors: Another
control mode that utilizes the long range e-field sensors to
actuate the arm, but not the fingers, can be used to move
the hand to a position around a stationary object, or track a
moving object. This is accomplished by holding the fingers
still and spread apart to give maximum position diversity.
The long range sensor readings are used to create control
signals in the frame of reference of the hand, and the arm is
actuated via inverse kinematics. Three separate PID control
loops control the motion in the three Cartesian coordinate
directions in the hand frame. The controller in the y direction
acts to reduce the magnitude of the difference between the
sensor readings in fingers 1 and 2. When the difference is
zero, the distance between the object and the sensors is equal,
and the object is centered between fingers 1 and 2. Similarly,
the controller in the x direction acts to reduce the magnitude
of difference between the finger 3 sensor reading from and the
average of the finger 1 and 2 readings. The x and y controller
act independently of one another. A third controller in the z
direction acts to drive the average of all three sensor readings
to a predefined set point, and thus position the hand the desired
distance from the object. This controller is suppressed while
the x and y controllers are moving the hand. This helps assure
that the hand does not bump into the object as it its moving
toward it.
3) Finger encoder-based wrist rotation: The rotational
asymmetry of the current finger position is computed by
subtracting the encoder value for finger 1 from the encoder
value for finger 2. This value is used as the input to a
proportional controller that rotates the wrist in order to reduce
the asymmetry of the two finger configurations. This technique
was introduced in [2]. This finger encoder-based wrist servoing
has the effect of orienting the hand to be parallel with the
object (for various simple object shapes...for complex object
shapes, the result of this rotational servoing would be harder
to characerize).
4) Arm servo control using finger encoders as sensor
inputs: This mode generalizes the wrist servoing described
above. The arm translates the hand, as in the arm servoing
section above, but the “sensor” inputs are the finger encoder
values, which in turn are set by the pretouch servoing tech-
nique. Thus in this mode, the e-field sensors do not directly
affect the arm state, but indirectly through the finger joint
angles. 1
In this control mode, the arm is positioned near an object to
be grasped, and the finger preshape controllers (as described
above) are started. As the fingers preshape, the encoder posi-
tions of each of the fingers are used as inputs to control the
velocities of the arm in order to align the arm with the object
and make the finger configuration more symmetric. This helps
ensure that when the final gripping force is applied the object
will not be displaced or rotated.
5) Finger encoder-based wrist translation: The transla-
tional asymmetry along the X-axis in the hand frame is
computed by subtracting the average encoder positions of
fingers 1 and 2 from the encoder position of finger 2. This
value is then used as the input to a proportional controller to
obtain the X component of the velocity in the hand frame.
IV. EXP ERI MEN TAL RESULTS
A. Human-robot object transfer
We have used EF Pretouch to implement human-to-robot
object transfer. The human brings an object in the vicinity of
the hand’s long-range sensors. When the object is detected, the
arm begins servoing in 3 dimensions to bring the hand into
alignment with the object. (For this experiment, we arbitrarily
chose an orientation for the hand. The hand maintains its fixed
orientation, servos in x and y to maximize alignment with the
object, and moves in and out in z to maintain a particular
distance to the object.) The y error signal is the difference of
the finger 1 and finger 2 long range sensor readings. The x
error signal is the difference between the finger 3 sensor value,
and the average of the finger 1 and finger 2 readings. When
1The advantage of this approach is that it removes uncertainty and com-
plexity associated with the non-linear response of the sensors. As long as a
particular setpoint (call the setpoint a null, without loss of generality) can be
detected reliably by the sensor values, then the control signal (finger joint
angle) needed to cause the null can be used as a sensor value, and one that
may be more linear than the underlying sensing mechanism used to detect the
null. This principle is used in fluxgate magnetometer sensors, whose “sensor”
output is actually the control value used to null a reading on a raw (highly
non-linear) magnetic field sensor.
the arm is aligned with the object and the object is stationary,
the system switches into grasping mode. The arm remains
stationary, and the fingers pre-shape to the object. When the
fingers are stationary (and in a symmetrical configuration)
grasping is initiated. The grasping procedure uses the hand’s
EF sensors, strain gages, and encoders together to execute a
reliable grasp, and detect grasp failure.
Once the robot hand has reliably grasped the object, it waits
for the human to let go. This capability is what we described
earlier as co-manipulation state measurement. If the human
fails to release the object, the robot issues a verbal reminder:
”You can let go now.” Once the human lets go, the arm
moves the object to another person in a pre-defined location.
It prompts the person to take the object, and then waits. Co-
manipulation contact detection is again used to decide when
the robot hand should release the object.
The grasp control procedure is a combination of force and
position control. As part of the grasping process, we want to
detect contact with as much sensitivity as possible. In other
words, we want to detect small amounts of force. EF Pre-
shaping allows us to use the strain gages with more precision
than would be straightforwardly possible otherwise. The strain
gages in the Barret Hand fingers are affected by gravity, which
can function as noise if not properly compensated, and also are
subject to drift (an additional source of noise). If we were to set
a contact force threshold to detect light contact of the fingers
with the object, but were uncertain about the effect of gravity
on the sensors, then we would have to set a contact force
threshold higher. Since the fixed point of the e-field finger
servoing procedure is close to the contact configuration, the
effect of gravity will be similar in the two cases. Thus the
strain gages can be read when the finger has preshaped to the
object, but has not yet attempted to grasp it. Thus, when the
fingers first make light contact with the object, this can be
detected by looking for changes in the baseline strain value
collected at the e-field servoing fixed point.
B. Co-manipulation contact detection
Figure 8 shows the effect of human touch on the long- and
mid-range sensors for one grasp.
The long range sensors are much more susceptible to
ground coupling variations than the mid-range sensors. Then
a human touches the object, which increases its coupling to
ground. This is possible to see in the mid-range sensor data.
However, only small changes occur. For the long range sensor,
human contact with the manipulated object causes drastic
and difficult-to-miss changes in signal level. This makes it
easy to detect human contact with an object that the robot
is manipulating. We have had the robot release the object to
the person when the person touches the object, and we have
found the interaction to be straightforward and reliable. We
made use of this technique in a high-visibility demo. In figure
10, the robot does not release the orange until the person
touches the orange. The system worked so reliably that we
were able to demonstrate it (arm servoing and grasping, as well
as co-manipulation detection) without being embarassed by
Fig. 8. Signal values from a mid-range and long-range sensor when object is
held in a precision grasp and contact with a human hand is made and broken.
Contact can be determined from either sensor, though the long range is much
more reliable.
Fig. 9. Human handing an object (an orange) to the robot. The arm first
servos to maintain alignment of the robot hand with the object or human hand.
Then finger pre-shaping is used for grasping.
demo failure in front of thousands of people, despite including
untrained VIP users (Governor Arnold Schwarzenegger and
Chancellor Angela Merkel) in the demo.
C. First stationary object pick up experiment
Because of the dramatic effect of ground coupling on
sensor values for the long range sensors, doing straightforward
arm servoing with the long range sensors would have been
problematic. As the hand gets closer to the object, the floating
effect becomes more pronounced. In some cases (such as a
floating object at very close range), the sensor values can even
reverse direction. This would lead to arm servoing in the wrong
direction (away from, instead of toward, the target object).
We made use of the fact that the mid-range sensors do not
enter transmit mode (“floating mode”) easily to address this
problem. First, using the long range sensors with the fingers
spread wide, the arm servos (in x,y, and z) until it is well
aligned in x and y, and at a pre-set z distance from the object.
At this point, the fingers close further, and x,y, and z servoing
continues using the mid-range electrodes, which are relatively
immune to impedance variations. When the hand is sufficienly
well positioned, the arm stops, and finger pre-shaping begins,
Fig. 10. Governor Schwarzenegger receiving orange from the robot. The
robot decides when to release the object based on co-manipulation detection.
Fig. 11. “Basin of attraction” for successful object pick up. The hand
always starts in the position and orientation shown, in the center. A can placed
anywhere inside the red polygon will be found and picked up by the hand.
A can outside the red polygon will not be detected and picked up.
using the mid-range sensors. Once the fingers are pre-shaped,
we initiate a grasping sequence that relies on encoders and
strain gages, in addition to the E-Field sensors, to grasp the
object. Figure 11 shows the “basin of attraction” within which
this procedure can reliably pick up the object. This basin is
roughly disc-shaped, and between 10cm and 15cm in radius.
Thus if the hand can be brought to within 10cm of the object
(perhaps by a longer range sensor such as a camera, or by
a human command, in a tele-operation scenario), then the E-
Field Pretouch grasping technique should typically be able to
detect it, align with it, and pick it up.
The system has a number of parameters which must be set.
The most sensitive tunable parameter is the E-Field value-set
at which to switch from long-range to mid-range arm servoing.
The system was tuned using a can of beans. Not only did it
reliably pick up the can of beans in the basin of attraction
described above, it also was able to reliably pick up an Apple,
with no retuning.
D. Second stationary object pick up experiment
In this experiment, we developed a procedure that uses the
encoder values of the fingers as the preshape controllers to
control the arm and position the hand so that the resulting
grasp is symmetric. Since only the mid- and short-range
sensors are used, the ground coupling state of the object has
little effect and the same procedure can be used for both
electrically floating and grounded objects.
When the robot is to pick up an object, the arm is moved to
a position near the object to be grasped. This position might
come from a vision system such as[9], from user input via a
laser pointer[5], or from a plan generated using a model of
the environment, as in [1]. With a mobile robot, the actual
position of the object relative to the hand might be offset by
actuation and sensing errors or uncertainties in the model.
In our preliminary experiments, the arm executed a preset
trajectory to a specific location, and errors were simulated by
moving the object.
To execute successful grasps despite these errors and uncer-
tainties, the following procedure is used. First, the preshaping
controller (as described in section III-C.1) is started using the
mid-range sensors with setpoints that will keep the fingers a
few centimeters from the surface of the object. A limit is also
set on the maximum position to which the fingers may close
to prevent them from closing too far prematurely and getting
in the way as the arm is moved to be centered on the object.
Once the error of the preshape controllers drops below a set
threshold, the controllers for the rotation and translation of the
wrist (described in Section III-C.3III-C.5) are run to optimize
the symmetry of the grasp.
Once all of the controllers have stabilized and the arm has
come to a stop, the preshape controllers switch to the short-
range sensors with setpoints that will bring the fingers within
a few millimeters of the objects surface. The controllers are
allowed to stabilize again before the hand is commanded to
close the remaining distance and apply gripping force. The
strain gages in the fingers are used to estimate and record the
encoder values at the point of contact, which can be used later
to determine whether the fingers slipped.
We used this procedure to pick up a juice bottle, and a
banana. The same control algorithm was used in both cases.
The hand approach vector was manually provided in advance;
the same approach direction would not have worked for both
objects.
1) Integration into mobile manipulation platform HERB:
The EF sensors were mounted on HERB, the Intel Re-
search Pittsburgh mobile manipulation platform. Preliminary
experiments allowed us to exercise the end to end system
functionality. With further tuning and integration, it should
be possible to use the e-field pretouch servoing on the HERB
platform. HERB has cameras and laser rangefinders which can
handle the long range measurements.
Note that the vision algorithm [9] currently used by HERB
requires a model of any objects whose pose is to be esti-
mated. In situations where object models are not available,
getting accurate shape and pose information from vision is
more difficult. In cases like these, the vision signal could do
relatively simple blob tracking to crudely estimate the position
of unknown objects; the e-field pretouch could take over for
the final manipulation steps.
1
2
3
4
1. Hand approaches object with fingers open
2. Fingers preshape to object and wrist is translated and rotated
to make grasp symmetric
3. Final preshaping is done with short-range sensors and fingers
apply grasping force
4. Object is lifted from the table
Fig. 12. The EF Pretouch-based procedure picking up an orange juice
bottle, and a banana. The same control algorithm is used in both cases.
The difference between the two cases is the hand approach vector, which
we provided manually in this case. In a full working system, the approach
vector could be provided by a vision system and a planner, or by human input.
Fig. 13. Mobile Manipulation platform HERB with EF Pretouch sensing
fingertips.
V. DISCUSSION AND FUTURE WORK
Our implemented system was able to pick up an object it
was tuned for (a can), and also succeeded with an apple, which
it was not tuned for. In a later experiment using simultaneous
arm and finger servoing, it was able to pick up both a juice
bottle and a banana, though different approach angles were
required.
The system would certainly fail for objects that are dras-
tically different in size from those it was tuned for. More
general approaches to interpreting the sensor data are needed
to allow the system to succeed with a much wider range of
object geometries.
For example, currently, as the hand moves into a grasping
position, we avoid needing to know the ground coupling of
the object by ignoring sensor readings when they are in a
regime that may be sensitive to this parameter. By combining
a time history of the sensor readings and their locations with
a model of the sensors, it should be possible to construct
and maintain a state estimate of the object that includes its
approximate dimensions, position, and ground coupling using
optimal estimation techniques (e.g. Kalman filtering), which
would enable more robust and general grasping capabilities.
Beyond simple estimates of object dimensions (which might
be sufficient for grasping in some cases), a series of mea-
surements could be interpreted to extract more detailed object
geometry, which would allow successful grasping for an even
larger class of objects. An interesting question for future work
is when should primary sensor values (such as e-field sensor
readings) be used, and when should secondary sensor values
(such as encoder values for fingers that are servoing to null
an error signal in a primary sensor).
More general approaches should also enable the system to
operate successfully when multiple objects are present. The
system described here would fail when presented with multiple
objects.
More sophisticated state estimation approaches would also
likely allow faster, as well as more general and reliable
grasping. By considering many sensor measurements jointly
(for example, a timeseries of sensor measurements collected
as the hand approaches the object) it should be possible to
produce more accurate state estimates, and thereby speed the
grasping process.
Exploring the use of EF pretouch with non-conductive
materials is one avenue for future exploration. Combining EF
with optical pretouch[2] should allow a very wide range of
materials to be sensed. The combination of sensing methods
should also allow some information about material properties
to be inferred.
An important step for a useful mobile manipulation system
will be to combine with long range sensing modalities such
as camera, laser range finder, or rfid. We demonstrated that
the E-Field Pretouch can guide the hand the object from a
distance of about 12cm; future useful systems will need other
long-range mechanisms to get the robot hand within the 12cm
basin of attraction.
Another important future step is to integrate the sensors
into a full, working mobile manipulation platform. In such a
setting, the benefits of EF Pretouch for overcoming additional
manipulation ncertainty caused by mobility can be tested.
Videos illustratiing the systems described in this
paper are available here: http://www2.seattle.
intel-research.net/˜jrsmith/rss09/
ACK NOWL EDG MEN T
Thanks to Dimitri Berenson, Siddhartha Srinivasa, and Mike
Vande Weghe for assistance with HERB.
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