Force Estimation and Slip Detection for Grip Controlusing a Biomimetic Tactile Sensor

Zhe Su Karol Hausman Yevgen Chebotar Artem MolchanovGerald E. Loeb Gaurav S. Sukhatme Stefan Schaal

Abstract— We introduce and evaluate contact-based tech-niques to estimate tactile properties and detect manipulationevents using a biomimetic tactile sensor. In particular, weestimate finger forces, and detect and classify slip events. Inaddition, we present a grip force controller that uses theestimation results to gently pick up objects of various weightsand texture. The estimation techniques and the grip controllerare experimentally evaluated on a robotic system consisting ofBarrett arms and hands. Our results indicate that we are ableto accurately estimate forces acting in all directions, detect theincipient slip, and classify slip with over 80% success rate.

I. INTRODUCTION

A service robot deployed in human environments mustbe able to perform dexterous manipulation tasks under manydifferent conditions. These tasks include interacting with un-known objects (e.g. grasping). Recent advances in computervision and range sensing enable robots to detect objectsreliably [1]. However, even with correct pose and locationof an object, reliable grasping remains a problem.

Tactile sensors can be used to monitor gripper-object inter-actions that are very important in grasping, especially whenit comes to fragile objects (see Fig. 1). These interactionsare otherwise difficult to observe and model.

Achieving human level performance in dexterous graspingtasks will likely require richer tactile sensing than is currentlyavailable [2]. Recently, biomimetic tactile sensors, designedto provide more humanlike capabilities, have been developed.These new sensors provide an opportunity to significantlyimprove the robustness of robotic manipulation. In orderto fully use the available information, new estimation tech-niques have to be developed. This paper presents a firststep towards estimating some tactile properties and detectingmanipulation events, such as slip, using biomimetic sensors.

In this work, we use the BioTac sensors [3] (Fig. 3) inorder to estimate forces, detect slip events and classify thetype of slip. Additionally, we present a grip controller thatuses the above techniques to improve grasp quality. The keycontributions of this work are: a) a force estimation techniquethat outperforms the state of the art, b) two different slipdetection approaches that are able to detect the slip event upto 35ms before it is detected by an accelerometer attachedto the object, c) a slip classifier that is able to classify thetypes of the slip with over 80% accuracy, and d) potentialapplications of the above techniques to robotic grasp control.

Zhe Su and Gerald E. Loeb are with the Department of BiomedicalEngineering; Karol Hausman, Yevgen Chebotar, Artem Molchanov, StefanSchaal and Gaurav S. Sukhatme are with the Department of Computer Sci-ence, University of Southern California, Los Angeles. zhesu@usc.edu

Fig. 1: Robotic arm grasping a fragile object using a stan-dard position controller (left) and the proposed force gripcontroller (right).

II. RELATED WORK

Humans are capable of manipulating novel objects withuncertain surface properties even when experiencing randomexternal perturbations [4]. Tactile sensing plays a crucial roleduring these tasks [5]. As reported in [6], humans mainlyrely on tactile feedback for slip detection and contact forceestimation.

Previous work has taken inspiration from human gripcontrol. Romano et al. [7] propose and evaluate a roboticgrasp controller for a two-finger manipulator based onhuman-inspired processing of data from tactile arrays. In[8] an approach to control grip force using the BioTac ispresented. The approach adopts a conservative estimate ofthe friction coefficient instead of estimating it on-the-fly.However, a conservative estimate may result in damagingfragile objects with excessive grip force. De Maria et al.[9] propose a new slipping avoidance algorithm based onintegrated force/tactile sensors [10]. The algorithm includesa tactile exploration phase aiming to estimate the frictioncoefficient before grasping. It also uses a Kalman filter totrack the tangential component of the force estimated fromtactile sensing in order to adaptively change the grip forceapplied by the manipulator. In our work, instead of a tactileexploration phase, we continuously re-estimate the frictioncoefficient while grasping the object.

Significant work has also focused on slip detection andslip-based controllers. Heyneman and Cutkosky [11] presenta method for slip detection and try to distinguish betweenfinger/object and object/world slip events. Their approach

is based on multidimensional coherence which measureswhether a group of signals is sampling a single input ora group of incoherent inputs. Schoepfer et al. [12] present afrequency-domain approach for incipient slip detection basedon information from a Piezo-Resistive Tactile Sensor. Ourwork, however, is novel in using the BioTac sensors for thesetasks, which provide the robot with increased sensitivity andfrequency range over traditional sensors.

The slip classification problem has not been explored asmuch as the other aspects of tactile estimation. Melchiorri[13] addresses the problem of detecting both linear androtational slip by using an integrated suite comprised ofa force/torque and tactile sensors. However, this approachneglects the temporal aspect of tactile data, which may beuseful in classifying manipulation events.

The BioTac sensors have been previously used to esti-mate contact forces. In [14] an analytical approach basedon electrode impedances was used to extract normal andtangential forces. In this work, we show that our machinelearning methods outperform this method substantially.

In [15] the authors also use the BioTac sensors to esti-mate forces acting on a finger. Machine learning (ArtificialNeural Networks and Gaussian Mixture Models) are usedfor learning the mapping from sensor values to forces. Thebest performance is achieved by using neural networks withregularization techniques. Here we extend this approach to anetwork with multiple layers and show that it leads to betterestimation performance.

III. BIOMIMETIC TACTILE SENSOR

We present a haptically-enabled robot with the Barrettarm/hand system whose three fingers are equipped withnovel biomimetic tactile sensors (BioTacs). Each BioTac (seeFig. 2) consists of a rigid core housing an array of 19electrodes surrounded by an elastic skin. The skin is inflatedwith an incompressible and conductive liquid.

The BioTac consists of three complementary sensorymodalities: force, pressure and temperature. When the skinis in contact with an object, the liquid is displaced, resultingin distributed impedance changes in the electrode arrayon the surface of the rigid core. The impedance of eachelectrode tends to be dominated by the thickness of the liquidbetween the electrode and the immediately overlying skin.Slip-related micro-vibrations in the skin propagate throughthe fluid and are detected as AC signals by the hydro-acousticpressure sensor. Temperature and heat flow are transducedby a thermistor near the surface of the rigid core. For eachBioTac, we introduce a coordinate system that is attached tothe fingernail. (Fig. 3).

IV. APPROACH

In this section, we introduce different aspects of tactile-based estimation that are useful in various manipulationscenarios. The high-resolution and multi-modal properties ofthe BioTac sensor enables us to estimate forces, detect andclassify the slip, and control the gripper using reaction forcesexerted on the fingers.

Rigid Core w/ Integrated Electronics

Incompressible Conductive

Fluid

Elastomeric Skin

Thermistor

Impedance Sensing

Electrodes

Hydroacoustic Fluid Pressure Transducer

External Texture Fingerprints

Fingernail

Fig. 2: Cross-sectional schematic of the BioTac sensor(adapted from [14]).

Z

Y

X

Y

X Z

Fig. 3: The coordinate frame of the BioTac sensor (adaptedfrom [14]).

A. Force Estimation

Reliable estimation of tri-axial forces (Fx

, Fy

, Fz

) appliedon the robot finger, which are shown in Fig. 3, is importantfor a robust finger control. In this work, we employ andevaluate four methods to estimate these forces based on thereadings from the BioTac sensor.

Previous studies have shown that tri-axial forces can becharacterized based on the impedance changes on the 19electrodes [14]. This method makes an assumption that eachelectrode is only sensitive to forces that are normal to itssurface. In our first approach, tri-axial contact forces areanalytically estimated by a weighted sum of the normalvectors (N

x,i

, Ny,i

, Nz,i

) of the electrodes. The weights arethe impedance changes (E

i

) on the electrodes:0

@Fx

Fy

Fz

1

A=

19X

i=1

0

@Sx

Ei

Nx,i

Sy

Ei

Ny,i

Sz

Ei

Nz,i

1

A ,

where (Sx

, Sy

, Sz

) are scaling factors that convert calculatedcontact forces into Newtons (N). They are learned with linearregression using ground truth data [14].

To improve the quality of force estimation we apply twoother machine learning methods: Locally Weighted Projec-tion Regression (LWPR) [16] and regression with neuralnetworks. LWPR is a nonparametric regression techniquethat uses locally linear models to perform nonlinear functionapproximation. Given N local linear models

k

(x), theestimation of the function value is performed by computinga weighted mean of the values of all local models:

f(x) =

PN

k=1 wk

(x) k

(x)

PN

k=1 wk

(x)

.

The weights determine how much influence each local modelhas on the function value based on its distance from theestimation point. The weights are commonly modelled by aGaussian distribution:

wk

(x) = exp

✓�1

2

(x� ck)D(x� ck)

◆,

where ck are the centers of the Gaussians and D is thedistance metric. Locally weighted partial least squares re-gression is used to learn the weights and the parameters ofeach local model.

As our third approach, we use a single-hidden-layer neuralnetwork (NN) that was proposed by [15]. The hidden layerconsists of 38 neurons, which is the doubled number ofinputs .

We also propose a fourth approach, where we use a multi-layer NN to learn the mapping from BioTac electrode valuesto the finger forces. The network consists of input, outputand three hidden layers with 10 neurons each.

For both NN approaches we use neurons with the hyper-bolic tangent sigmoid transfer function:

a =

2

1 + exp(�2n)� 1.

For the activation of the output layer we use a linear transferfunction, i.e. the output is a linear combination of the inputsfrom the previous layer.

NNs are trained with the error back-propagation andLevenberg-Marquardt optimization technique [17]. In orderto avoid overfitting of the training data we employ the earlystopping technique during training [18]. The data set isdivided into mutually exclusive training, validation and testsets. While the network parameters are optimized on thetraining set, the training stops once the performance on thevalidation set starts decreasing.

B. Slip DetectionRobust slip detection is one of the most important features

needed in a manipulation task. Knowledge about slip mayhelp the robot to react such that the object does not fall outof its gripper. In order to detect a slip event, two differentestimation techniques are used: a force-derivative method anda pressure-based method.

The force-derivative method uses changes in the estimatednormal force to detect slip. Since the gripper force becomessmaller as the object slips, the negative derivative of thenormal force is used to detect the slip event. Based on theexperience from the experimentation, the threshold on thenegative normal force derivative is set to 1N/s.

Slip is also detected using the pressure sensor. Since theBioTac skin contains a pattern of human-like fingerprints,it is possible to detect slip-related micro-vibration on theBioTac skin when rubbing against textured surface of anobject. A bandpass filter (60-700Hz) is first employed tofilter the pressure signal. Second, the absolute value of thesignal is calculated since we are interested in the absolute vi-bration. Due to differences between pressure sensor samplingfrequency (2.2kHz) and the onboard controller (300Hz), the

slip detection algorithm considers a 10ms time window (3cycles of the onboard controller). This guarantees 22 samplesof pressure readings in the time window. Slip is detected if 11out of 22 pressure sensor values exceed the threshold. Basedon the experiments, the slip threshold is set to be twice aslarge as the baseline vibration caused by the motors of therobot.

C. Slip Classification

In the course of our experiments we observed two maincategories of object slip: linear and rotational. During linearslip, the object maintains its orientation with respect to thelocal end-effector frame but gradually slides out of the robotfingers. During rotational slip, the center of mass of theobject tends to rotate about an axis normal to the graspsurface, although the point of contact with the robot’s fingersmight stay the same. It is important to discriminate betweenthese two kinds of slip to react and control finger forcesaccordingly. We notice that rotational slip requires muchstronger finger force response than linear slip in order torobustly keep the object grasped within the robot hand [19].

To be able to classify linear and rotational slip, we train aneural network to learn the mapping from the time-varyingBioTac electrode values to the slip class. To construct the fea-tures, we take a certain time interval of electrode values andcombine all values inside the window into one long featurevector, e.g. 100 consecutive timestamps of 19-dimensionalelectrode values result in a 1900-dimensional input vector.The architecture of the NN consists of input, output and onehidden layer with 50 neurons. The hidden layer has a sigmoidtransfer function. The softmax activation function is used inthe output neurons. It produces the probabilities of the signalsequence belonging to one of the slip classes.

Similar to the force estimation we use early stopping toprevent overfitting. The network is trained with the ScaledConjugate Gradient back-propagation algorithm [20].

D. Grip Controller

In order to test the estimation of the forces and detectionof the slip event, we design a grip controller that is ableto take advantage of the estimated information. Appropriategrip force control is required for the robot to manipulatefragile objects without damaging or dropping them.

The control algorithm consists of two main stages, gripinitiation, and object lifting (Fig. 4). In grip initiation, therobot fingers are position controlled to close on an objectuntil the estimated normal force (F

z

) is above a certainthreshold. The threshold is chosen to be very small (0.2N ) inorder to avoid damaging the object. Once all the fingers arein contact with the object, the position controller is stopped,and the grip force controller is employed. The force controlis used for the entire object-lifting phase.

In order to establish the minimal required grip force, theforce tangential to the BioTac sensor F

t

is estimated:

Ft

=

qF 2x

+ F 2y

.

Start

Close all fingers

Finger normal force FZ > 0.2N

Yes

All fingers in contact

No

No

Yes

Closing fingers with contact detection

Update the grip force FZ= FT / ? + safety margin

No

End

? estimated by Coulomb friction law

Slip?

Yes

Lifting the object with force control, slip

detection and online friction coefficient

estimation

Start picking up an object using ? = 2

Fig. 4: Control diagram of the grip controller.

Since the tangential force is directly proportional to theweight of the object, the grip force F

z

is controlled based onthe current estimation of the friction coefficient µ in additionto some safety margin:

Fz

=

Ft

µ+ safety margin.

The friction coefficient is initially set to 2 based on theknown friction of the silicon skin of the BioTac and othercommon materials. Since the initial friction coefficient isnot estimated accurately, slip may occur during the liftingphase. Once slip is detected using the force-derivative-basedslip detection described earlier, the friction coefficient isestimated more accurately online using the Coulomb frictionlaw:

µ =

Ft

Fz

.

The safety margin was chosen to be 10-40% to account forobject acceleration during manipulation and additional un-certainties of the friction coefficient. Finally, the commandedgrip force F

z

is updated according to the newly estimatedfriction coefficient that provides the minimal force, whichis sufficient to lift the object. The grip control algorithm isshown in Fig. 4.

V. EVALUATION AND DISCUSSION

A. Force EstimationIn order to evaluate different force estimation methods,

we collected a data set consisting of raw signals of 19electrodes. The ground truth data were acquired using a forceplate that was rigidly attached to the table. The BioTac wasrigidly attached to the force plate as shown in Fig. 5. Inthe experiment, the BioTac was perturbed manually multiple

Fig. 5: Experimental setup for the force estimation com-parison: the finger is pressed at different positions andorientations against the force plate.

0.0

1.0

2.0

3.0

4.0

Fx Fy Fz

MSE Full Data Set

0.00

0.04

0.08

0.12

0.16

Fx Fy Fz

SMSE Full Data Set

0.0

1.0

2.0

3.0

4.0

Fx Fy Fz

MSE Test Set

0.00

0.04

0.08

0.12

0.16

Fx Fy Fz

SMSE Test Set

Analytical LWPR NN 1 layer NN 3 layers

Fig. 6: The performance comparison between force estima-tion techniques. Analytical approach is outperformed by theother methods. LWPR and 1-layer NN perform well on thefull data set but have low performance on the test set. 3-layerNN avoids overfitting and yields good results on the test set.

times from various directions with a wide range of forces.The data were collected with frequency 300Hz (over 17000individual force readings). The collected data sets weredivided into 30 seconds intervals of continuous electrodereadings. Afterwards, these intervals were randomly shuffledand divided into 80% training and 20% test sets. Addition-ally, during the training of NNs, 20% of the training set wasused for the validation set to prevent overfitting with the earlystopping technique.

Fig. 6 shows the results of the four compared methodsevaluated on the full and test sets. In both cases, commonestimation metrics were chosen: Mean Squared Error (MSE)of the force in N2 and unitless Standardized Mean SquaredError (SMSE). SMSE is computed by dividing the MSE bythe variance of the data.

The analytical approach developed previously [14] is out-performed by the other three methods. From the results, wedraw the conclusion that the LWPR and 1-layer-NN methods

0 1 2 3 4 5 6 7 8 9 10−6

−4

−2

0

2

4

6

8

Time, s

Fo

rce F

x, N

Force PlateAnalyticalLWPRNN 1 layerNN 3 layers

0 1 2 3 4 5 6 7 8 9 10−1

0

1

2

3

4

5

6

7

8

Fo

rce F

y, N

Time, s

0 1 2 3 4 5 6 7 8 9 10−5

0

5

10

15

20

Fo

rce

Fz,

N

Time, s

Fig. 7: Example of force estimation with different methodsover time. From top to bottom: force estimation for dimen-sions: F

x

, Fy

, Fz

overfitted to the data, i.e. they perform better in the fulldataset than the other methods but they yield inferior perfor-mance in the set that was not exposed in the training. The3-layer neural network approach, however, achieved goodresults on the test set and avoided overfitting. It illustratesthat the deeper structure of the NN was able to capture thehigh-dimensional force mapping more accurately. On the testset, we could achieve the best MSE of 0.19N2 in the x-direction, 0.28N2 in the y-direction and 0.72N2 in the z-direction.

It is also worth noting that there exists a significantdifference between different force directions in the case ofthe MSE evaluation. It can be explained by the range offorces that were exerted on the sensor. Since F

z

is thevertical axis of the BioTac, the forces experienced during theexperiments vary more than in the other directions. SMSEcomparison is more appropriate in this case as it incorporatesthe range of the data. The best SMSE values on the test setwere achieved with the 3-layer NN: 0.08 for the x-direction,0.03 for the y-direction and 0.02 for the z-direction.

In addition to the absolute errors, it is important to see

1.34

1.36

1.38

1.4right finger position (rads)

-20

-10

0

10

20force derivative slip right finger force derivative (N/s)

left finger force derivative (N/s)

0500

1000150020002500

vibration slip right finger vibration (bits)left finger vibration (bits)

Time (s)0 0.05 0.1 0.15 0.2 0.25

0

0.5

1

1.5IMU acceleration (m/s2)IMU slip

Fig. 8: An example run of the slip detection experiment.Using the BioTac sensor we are able to detect the slipevent before the IMU accelerometer attached to the objectmeasures any acceleration due to slip.

how the estimation errors correspond to the actual forcesover time. An exemplary result is depicted in Fig. 7. Oneproblem of the analytical approach is that it has an offsetthat differs in various situations. The assumption that eachelectrode is mostly sensitive to skin compression along itsnormal vector is not able to capture the non-linear patternsgiven by the highly non-linear deformation on the siliconskin of the BioTac. In the case of LWPR and NNs, the resultsare similar. One can notice, however, that the LWPR forceestimation produces forces that are not as smooth as the NNapproaches. The difference between the two NN approachesis too small to be noticed on this data set. Given the resultsobtained from the test data set, the 3-layer NN approachyields better performance than the other methods.

B. Slip DetectionWe tested the previously described slip detection algo-

rithms on two objects with distinctive textures: a plastic jarwith smooth surface and a wooden block with rough texture(see Fig. 9). In both cases, we attached an IMU to the objectsin order to detect the moment when the object starts moving.In order to make the object slip, the robot first grasps andpicks up the object, and then opens its right finger by 0.04rad. The collected data set consists of 20 slip events perobject.

An example run of the slip detection experiment using thewooden block is depicted in Fig. 8. One can see that usingthe force-derivative and the pressure-based method, we wereable to detect slip even before it was noticed by the IMU.It is also worth noting that the pressure-based method candetect slip sooner than the force-derivative method. This maybe caused by the higher sampling rate of the pressure sensor.However, it is also the case that in the very initial stage ofslip (incipient slip) the microscopical slip effects are not yetvisible at the electrodes. Nonetheless, the slight movement of

Fig. 9: Different objects used for the experiments.

the fingerprints is picked up by the high-frequency pressure-based slip detection signal.

Statistical analysis of the experiments shows that therobot is able to detect slip using the force-derivative method5.7ms ± 4.5ms (the plastic jar) and 7.8ms ± 3.6ms (thewooden block) before the movement is noticed by theIMU. The pressure-based method detects slip even sooner:32.8ms ± 4.2ms (the plastic jar) and 35.7ms ± 6.0ms(thewooden block) before the object motion is detected by theIMU. These results indicate that the BioTac is able to quicklyand reliably detect slip which is important for robust gripcontrol.

C. Slip ClassificationTo evaluate the NN approach for the classification of two

kinds of slip events, four objects were chosen: a woodenblock, oil bottle, wipes box and a jar with added weights(see Fig. 9). For training, the robot grasped an object eitherapproximately at the center of mass of the object or at theedge of the object. These two grasping methods caused eitherlinear (if grasped at the center of mass) or rotational slip ofthe object while it was being picked up. In order to detectslip, an IMU was attached to the object. For each object,over 80 grasps were performed (40 for the linear slip and 40for the rotational slip). The data set was randomly shuffledand divided into the 80% training and 20% test sets. Similarto the force estimation, 20% of the training set was used forthe validation during the NN training.

Results of the experiments are depicted in Fig. 10. For theinput of the NN, points from 100 consecutive timestampswere selected, resulting in a 1900-dimensional input vector.Each point in Fig. 10 corresponds to the last timestamp thatwas taken into account as the NN input, i.e. the point whenwe classify slip given 100 previous values. The momentwhen slip was detected by the IMU is depicted by avertical line. As more data are gathered during an actualslip, classification accuracy improves as expected.However,it is worth noting that using the NN approach, the robot

−300 −200 −100 0 100 200 300 4000.4

0.5

0.6

0.7

0.8

0.9

1

IMU slip

Time of prediction, ms

Cla

ssif

icati

on

accu

racy

Fig. 10: Linear/rotational slip classification accuracy depen-dent on the time of prediction. Red line shows the point whenslip is detected based on the IMU readings.

0

0.5

1

1.5

right finger position (rads)left finger position (rads)

-20

0

20

40

right finger grip force (N)left finger grip force (N)

-20

0

30right finger force derivative (N/s)left finger force derivative (N/s)

0

500

1000right finger vibration (bits)left finger vibration (bits)

Time (s)0 10 20 30 40 50 60 70

0.5

1

1.5

2IMU acceleration (m/s2)

Reach Grip / Lift Hold Replace Open

Mechanical transients•  Slip detection•  Collision with the environment

Contact responses•  Contact detection•  Tri-axial contact forces•  Friction Coefficient

Fig. 11: An example run of the grip controller that includesall of the grasping phases.

is able to achieve approximately 80% classification rate,before the IMU is even able to notice that the slip eventstarted. Our algorithm accurately detects the slip class evenbefore significant object motion is detected (using an IMU),allowing more time for the robot to respond appropriately.

D. Grip Controller

The grip controller was evaluated using two differentobjects with varying weight: a plastic jar (see Fig. 9) withthe weight ranging from 100g to 1500g and a plastic cup(see Fig. 1, top) with the weight ranging from 10g to 500g. Ineach experiment, the robot grasped the object approximatelyat its center of mass, lifted it off the table, held in the airand placed it back on the table.

Fig. 11 shows an example run of the grip controller.

During the reaching phase, the robot’s fingers detect thecontact with the jar using the normal force estimation. Thisis the moment when the grip force control is employed (10seconds in the experiment). When the robot starts to liftthe jar, the grip force (F

z

) starts to increase proportionallyto the tangential force (F

t

) sensed on the BioTac. Thefriction coefficient (µ) is updated at approximately the 18thsecond of the experiment, when the slip event is detectedby the force-derivative method. After the 20th second of theexperiment, the jar was successfully picked up and held in theair. Two 150g weight plates were added to the jar at the 32ndsecond and the 38th second, consecutively. It is worth notingthat the grip controller detected the two slip events usingthe force-derivative method and increased the grip force toprevent further slip. When the robot placed the jar back onthe table, there are large spikes in the slip detection signal (at48th second). These may be used to detect the collision withthe environment and release the objects without pressing thejar on the table with excessive force.

VI. CONCLUSIONS AND FUTURE WORK

In this work, we explored how one can use biomimetictactile sensors to extract useful tactile information neededfor robust robotic grasping and manipulation.

We performed estimation of normal and tangential forcesthat normally occur during holding and manipulating objects.Machine learning techniques were employed and evaluatedto learn the non-linear mapping from raw sensor values toforces. As the experiments demonstrated, the best perfor-mance was achieved using 3-layer neural network regression.

Different modalities available from the BioTac sensorwere used to perform detection of the slip event. The bestperformance was observed with the pressure-based method,where slip was detected more than 30ms before it was pickedup by an IMU accelerometer.

Slip classification into linear or rotational slip was ob-served to be important for robust object handling due todifferent requirements for finger force response. We achieved80% classification success rate using a neural networkapproach before the slip event was detected by an IMUaccelerometer. This indicates that the robot should be ableto change finger forces at a very early stage of the slip andtherefore, prevent the moving of the object inside the hand.In future work, the controller that uses this classification willbe employed and evaluated.

In order to test the above mentioned estimation techniques,we created a grip force controller that adjusts the grippingforce according to the forces acting on the fingers. Wepresented an example run of the controller during the entiregrasping experiment. Our results indicate that, by using thegrip controller, the robot is able to successfully grasp eveneasily deformable objects such as a plastic cup (Fig. 1).

At present, we are able to detect simple manipulationevents and estimate forces. In the future, we plan to predictmore high-level features such as grasp stability, which canbe used to plan high-level decisions to manipulate objectssuccessfully.

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