To appear in IEEE HUMANOIDS, 2013.
This copy is for personal use only. c 2013 IEEE
Prints will be available at http://ieeexplore.ieee.org shortly.

A Topology-based Object Representation for
Clasping, Latching and Hooking
Johannes A. Stork

Florian T. Pokorny

Abstract— We present a loop-based topological object representation for objects with holes. The representation is used
to model object parts suitable for grasping, e.g. handles, andit incorporates local volume information about these. Furthermore, we present a grasp synthesis framework that utilizes thisrepresentation for synthesizing caging grasps that are robust
under measurement noise. The approach is complementary toa local contact-based force-closure analysis as it depends on
global topological features of the object.
We perform an extensive evaluation with four robotic handson synthetic data. Additionally, we provide real world experiments using a Kinect sensor on two robotic platforms: a Schunkdexterous hand attached to a Kuka robot arm as well as a Naohumanoid robot. In the case of the Nao platform, we provide
initial experiments showing that our approach can be used toplan whole arm hooking as well as caging grasps involving only
one hand.

I. I NTRODUCTION
Local contact-based methods for grasp stability assessment
are a well-established area in robotics. Classical, physicsbased frameworks such as the grasp quality measures of[1] determine if a grasp can withstand external forces of aknown magnitude. Such force-closure based grasp synthesis
approaches commonly assume that the manipulator and thegrasped object are rigid and have known geometry andfriction properties.
In addition to solely fixing an object in the hand, humans
employ a vastly richer repertoire of grasps affording bothin-hand manipulation and additional skills such as dragging,
toting, pulling and hauling of soft and rigid objects. These
activities are important in many situations and may requirelarge forces which the robot might not be able to exert using
point-contact based grasps. A mobile robot may also wantto latch onto objects and fixed structures in the same way
as humans do: consider for example clasping holding-bars
in buses or subways, grabbing handrails, pushing a baby
stroller, or using a pair of scissors.
While actions such as holding and lifting do depend on
stability in terms of constraining forces and contacts, theclassical methods of assessment and planning of this form ofstability require detailed surface models. For the generationof stable grasps on complex objects with many contact
points, no suitable analytical methods are currently available.
Most common methods employ some form of sampling [2]–[4], various heuristics [5]–[7] or are based on previously
learned probabilistic models of grasping poses for a specific
The authors are with the Centre for Autonomous Systems, Computer
Vision and Active Perception Lab, School of Computer Science andCommunication, KTH Royal Institute of Technology, Stockholm, Sweden.
{jastork,fpokorny,dani}@kth.se

Danica Kragic

Fig. 1. Two example grasps are displayed: a Nao grasping a basket using
a chain forming grasp and a whole arm hooking grasp.

class of objects [8], [9]. Furthermore, sparse or noisy data
often prohibit a sufficiently good object reconstruction suchas volumetric representations [10], [11] derived from a watertight surface reconstruction [12], or a sufficiently accurate
normal direction estimate for force-closure analysis.
While force and contact stability could additionally beincorporated, it is not a priori required within our framework.
In fact, a complete fixation of the object may even be
counterproductive, e.g. in the case of using a pair of scissors.
We believe that, if robots are to eventually reach the dexterityof an acrobat catching a rope in mid-flight or swinging with
its hands hooked securely around a ring, methods based on
global rather than local object properties can provide analternative approach to grasp synthesis. The chain forming
grasps presented in this paper exploit a global object property– namely a basis of the first homology group formed byshortest closed loops on the object [13], [14]. As in [14], we
consider these as a basis for interlinking an object and robot
hand or arm for the purpose of grasp synthesis.
The main contributions of this work are:a) We propose a new object representation for generating
hypotheses of chain forming grasps from point clouddata. The method is capable of dealing with unknown
and potentially deformable objects using global topological features of the object’s surface geometry.
b) We evaluate our method on the Schunk, Armar III,DLR 1 and iCub hands with synthetic data, showingthat our approach can be used for a wide range of handkinematics.
c) We provide real-word experiments with a Nao humanoid robot and a KUKA/Schunk setup, and withnoisy partial view Kinect data for several rigid and soft
objects.

Hand height λPoint cloud P Delaunaycomplex Kd
with loopa)

Object model
Hand modelb)

Sampling ofloop positions

Loop-based convex local-volume approximationc)d)Graspable volume filter

Grasp frame computation

Volumecenters

Grasp frameswith approachdirectionse)f)Grasp frames set

Volume threshold ϕFig. 2. System overview: Point cloud data P and its refined Delaunay triangulation Kd are shown in a) and b). A shortest loop and its center is depictedin blue. Selected loop points and loop tangents are marked, and associated slices of P are indicated in red in c). Convex volumes and centers of the slice’s
adjacent connected component are shown in d). Volume centers and approach vectors of selected grasp frames after filtering are indicated in e) and f).

System Outline
Our method starts by building a geometric mesh description directly from 3D point cloud data and applies
a topology-based algorithm [13] to identify shortest noncontractible closed loops in the model. Fig. 2 depicts an
example point cloud, a mesh description and a loop on
the left. Subsequently, a hand model and loop tangentsare used to construct approximations of the local geometry
for potentially graspable handle-like object parts. For eachconsidered loop point, a slice of the point cloud, orthogonal
to the tangent is extracted. The convex hull of the slice’s
connected component that is adjacent to the loop point isdetermined as a local volume approximation. Slices andconvex hulls can be seen in Fig. 2c,d).
Finally, these local volume approximations are filtered byan end effector-determined threshold and grasp parameters
are computed for the remaining volume centers. The last
step is visualized on the right side of Fig. 2. There, thevolume centers are depicted as black plus-signs, while thegrasp frames are described by approach vectors.
Although our approach, based on the detection of handlelike object parts, is limited to objects with holes there
are many natural objects that feature such parts – several
examples will be shown in the experimental evaluation.
In this work, we consider grasping in the broader senseof holding an object with or without the support of external forces such as gravity. Our system plans general
chain forming grasps that maximize the interlinking between
suitable parts of the object with an end effector. The resulting configurations can informally be described as clasping,latching and hooking grasps. For illustration, Fig. 1 shows
clasping or latching on left and hooking on the right side.
The grasps synthesized in this work establish caging grasps
in a generalized practical sense and not necessarily in theanalytical sense. This means that the escaping probability ofthe object is low. For clasping or latching of rigid bodies,
we used a random motion rigid body simulation to test if theobject was caged in this sense. Hooking grasps such as in theleft part of Fig. 1 cage the object only with respect to motions
under task constraints. The basket in Fig. 1 will remain
hooked only under reasonable expected motions and forces
applied e.g. during walking. Note that not all styles of suchcaging grasps afford the same activities or provide similar
flexibility. Hooking grasps could be useful in particular when
two humanoid robots carry a basket together, since they
impose fewer constraints on the two robots’ motion than atight clasp of the object.
II. M OTIVATION AND R ELATED W ORK
Point-contact, physics-based methods depending on the
Coulomb friction model such as force-closure are a popular
approach to grasp synthesis as reviewed in [15]. In [2], grasp
hypotheses are generated by approaching an object fromrandomized starting directions and closing the robot’s hand
until contact occurs. Optimal or viable grasps are then chosen
based on a grasp quality function [1].
Recent work of [16] examines empirical or data-driven
methods for grasping. As stated in the survey, the grasping process depends on many factors such as the physicalproperties of the object, the embodiment of the robot, available sensory information and task constraints. Thus, many
approaches apply some form of heuristic directly in the state
space to simplify aspects of the problem [5]–[7], [10], [16],[17]. Furthermore, a suitable simplification of the robot’s
hand state space representation, such as the notion of posturalsynergies discussed in [18], can be used to simplify thecontrol of the robot’s hand. Recently, several extensions ofthese ideas have been actively pursued [19]–[21].

There are also many approaches that perform a decomposition of objects and generate grasps on individual, decomposed parts. Examples include unions of parametrizedshape primitives from manual matching [2] and automatichierarchical fitting methods [22], [23]. While a superquadricdecomposition tree may provide some flexibility [22], thehigh number of parameters may limit the viable complexityof considered objects. Hence, simple hierarchical box shapebased models have been used to represent objects fromrange data [23]. These methods relate to our approach since
they approximate object volumes. However, they do notrealize nor exploit global structural information such as theconnectivity between parts or topological properties of theobject.
Recently, Morse theory has been applied to determine
topological skeletons such as Reeb graphs for shape decomposition [24]. This approach results in a decomposition
into parts based on surface properties and relies on surface
curvature to define part boundaries. The concept has been
applied to semantic grasp planning [25], where the topological segmentation is annotated by automatic part recognitionand shape retrieval. Based on the same idea, programmingby demonstration for grasping and manipulation has been
proposed [26], [27]. In these approaches, the Reeb graph ishowever only used for segmentation and transfer of grasp
affordances by graph matching. The extracted information
about the global object structure and the relation of the parts
is not utilized to plan the grasps. Our method, in contrast,
focuses on global topological information directly.
The medial axis representation, a volume-based object representation describing maximal inscribing spheres was introduced for the purpose of grasping in [10]. The representationconsists of a union of sphere centers which captures theobject’s geometric connectivity and allows to exploit local
symmetry properties. Based on a tree structure derived fromthe medial axis, a set of heuristics is applied to generate viable grasps. The approach has been extended to a grid-based
volumetric representation that examines symmetry propertiesof the grid-local neighborhood [11]. This method is similar toours since it is volumetric and it directly considers structural
information. However, the approach suffers from inherent
instability under noise [28]. The underlying computational
method for extracting the approximated medial axis [12] isbased on the reconstruction of a watertight surface of thewhole object. This depends heavily on a dense, full-view
point cloud and is thus sensitive to noise. In contrast, we
will show that our method extracts useful global structuralobject information even when the data is sparse, noisy or
obtained from a partial view.
In contrast to force-closed grasps, caging grasps onlybound an object’s mobility to prevent it from escaping arbitrarily far from the manipulator. A caging grasp provides away to control an object without immobilizing it completely.
Early research on caging has focused on a theoretical analysis
and on efficient algorithms for simple hand mechanisms
with few degrees of freedoms [29], [30] and consideredmostly planar objects. Object concavity has been exploitedfor caging with finger position maximally far away fromthe object [31]. Later, the study of three-dimensional caging
by multi-fingered hands led to the definition of sufficientconditions and resulted in a cage planning system [32].
Caging configurations have been considered in relation to
grasping as a waypoint or initial point to immobilizing grasps[33] and as a method to deal with uncertainty in grasping[34]. Applications in manipulation planning have been found,
where rigidity constraints between an object and the robot
can be neglected in planning when the object is caged [35].
To the best of our knowledge, caging research so far has
only considered rigid objects. In this work, we particularly
focus on chain forming grasps which attempt to interlink theobject with the manipulator and result in a caging configuration if the object is rigid. However, in the experimental
evaluation, we will also show that our approach can be used
for caging soft or flexible objects with holes.
In summary, our approach prevents objects from escaping without necessarily creating immobilizing grasps. In
particular, we exploit an object representation based on
global structure information which allows us to apply simple
enveloping grasp policies to grasp rigid and flexible objects.
III. M ETHODOLOGY
In this section, we provide details on the detection ofclosed loops, the employed end effector models, the object
representation and elaborate on our grasping strategy.
A. Detection of Loops
A key concept used in this paper are closed loops on aclosed surface S ⊂ R3 which are non-trivial as elementsof the first homology group H1 (S). When computed overa finite field, H1 (S) is a vector space whose elements
correspond to equivalence classes of closed curves andwhere two loops are considered equivalent if they form aboundary of a part (a 2-cycle) of S. The first homology
group is used in Algebraic Topology [36] and stays invariant
under continuous deformations of S called homotopies. We
are in particular interested in a basis of H1 (S) consistingof curves which are of shortest length and use them asfeatures for grasp synthesis as in [14]. Since we reconstruct
our surface representation from point-cloud data, we will
work with a simplicial complex approximation Kd of S
for which the notion of H1 (Kd ) is also well-defined. We
employ the software ShortLoop [37] to determine a basis ofapproximately shortest curves of H1 (Kd ). An example of theaforementioned shortest loop basis is displayed in Fig. 2b.
For a watertight perfect mesh model, loops in H1 (S) can
be further classified as tunnel-loops and handle-loops [38],with tunnel loops running around holes in an object. Tunnelloops traverse handle-like parts of an object and are hence
most relevant for grasping. We however work with a roughsimplicial complex reconstruction obtained from point-clouddata where this distinction is often impossible to make.
Non-graspable elements of H1 (Kd ) will hence at first be
included in our analysis and are only eliminated during alater collision detection phase.

End effector

height [m]radius [m]

λ
[m]
2

ϕ [m]

Schunk SDH
Armar III
DLR 1
iCub

0.120
0.090
0.130
0.085

0.050
0.035
0.060
0.025

0.060
0.045
0.065
0.425

0.15620.11400.1697
0.0986

Fig. 3. Pre-shapes with GCP frames (top) and grasps on maximal volume
cylinder (middle), see Section III-B. The arrows show ϕ (white) and λ(yellow). Table below: End effector description parameters.

B. End Effector Related Parameters
We define two parameters for a robot hand which define
the shape of the maximal cylinder that the end effector cansecure by closing the fingers from a parallel pre-shape as
seen in Fig. 3. The height of the cylinder, λ, describes thegrasp width, ϕ describes the maximal extent of the cylinder.
Parameter λ is used to build an object model that consistsof hand-sized volumes while ϕ serves as a bound in grasp
parameter generation to reject overly large volumes.
For an end effector, we define a grasp center point frame(GCP frame) that defines the center of the cylinder relative
to the robot hand. All end effectors used in this work can
be described by a set of individual fingers and a static palm.
Our approach requires that at least one of these fingers can
oppose the remaining fingers to form a parallel pre-shape.
The DLR Hand (12 DOF) is a four-fingered articulated
robotic hand that is 50% larger than the average humanhand. The hand of the child-sized iCub humanoid robot has
20 DOF. The five-fingered IAI-HAND-12 (10 DOF) for the
ARMAR III platform is a symmetric three-jaw grasper. The
Schunk SDH (7 DOF) is a 3-fingered fully actuated industrial
robot gripper. We disabled the coupled rotation of two of the
Schunk hand’s fingers, resulting in a 6 DOF manipulator.
C. Object Representation
A loop, as defined in Sec. III-A, represents a topologicaland geometric attribute of an object. Therefore, we model
objects with holes as a set of convex local-volume approximations of their approximated shortest loops. Considering
chain forming grasps – created by closing the end effector
around a segment of an extracted loop – we are interested
in the local shape of the enveloped loop section. We infer
this information from unordered full-view 3D point clouddata. The individual steps of the model building process are
exemplified in Fig. 2a–e).
Assuming that the point cloud P ⊂ R3 is sampled froma closed surface, we start by refining the Delaunay complexof P and denote the result Kd . The refinement consists ofremoving all edges with length d or larger and eliminating
all tetrahedra and faces involving those edges. Hence, thevalue of d limits the distance of two points up to which they

are still joined in Kd by some edge, face or tetrahedron.
We assume that P is dense enough so that the triangulation
results in a descriptive model of the observed object. The
exact value of d can be chosen in an informed way byconsidering additional constraints, e.g. using a persistenthomology analysis of P in the form of barcodes [39]. In theexperiments however, we select the value manually based on
point cloud density and hand extent.
We apply a polynomial time algorithm [37] on the finite
simplical complex Kd that results in a set, L, of cyclesof linear line segments, approximating a basis of shortest
loops for H1 (Kd ). In [14], we give further details of theprocess and its parameters. Considering a loop l ∈ L, weapproximate the loop-tangent, TP , of a point P on l.
Next, we construct an orthogonal plane, OP , containing
P with normal direction TP . Given a point P on a loop, we
describe the object’s local shape by a convex volume, VP . To
this end, we consider an undirected graph, GP , derived fromthe simplical complex Kd . The graph arises from the edges of
Kd , defined on the points of P that are within distance λ2 tothe plane OP (Fig. 2c). Finally, VP is defined as the convex
hull of the connected component closest to P (Fig. 2d).
In the process described above, every point on an approximated shortest loop can be annotated with a hand-size
related convex approximation of the loop-local volume. The
extracted set of loops, loop points, and volumes forms our
object representation.
D. Grasping Process
We parametrize a grasp by prescribing the end effector’s
GCP frame and consider the Z-axis as the approach direction. Given an end effector’s GCP and a convex local-volume
approximation VP at a loop l ∈ L, as defined above, we move
the GCP frame into the volume’s center. Since the center of
VP already defines the position, only the frames orientationis left to choose. The loop center point, Ml , is given as thecenter of gravity of the points in P, supporting the linear
line segments of l.
Beginning with the approximated tangent of the loop
point, TP , and using the vector Z = Ml − P , we construct
an orthonormal coordinate frame located at the center of VP
that is identified with the GCP frame. We reject all sampled
points, P on l that result in a maximal extension of VP larger
than the end effector’s volume measure threshold ϕ. If therobotic hand is not symmetric, we additionally include grasp
parameters rotated around the frame’s Z-axis by 180 degrees
for each accepted volume.
IV. E XPERIMENTAL E VALUATION
We first evaluate the performance of the proposed method
on synthetic data using four robotic hands. After that, we
present results from a real setup using Kinect data, a 6 DOF
Kuka arm with a Schunk SDH and a Nao humanoid robot.
A. Experiments on Synthetic Data
We first demonstrate our approach on five real-sized
objects by executing a simple grasp policy. From 3D polygon

mesh models [40], [41] we sample 4000 points on the visible
outside surfaces by moving a virtual camera around theobject. Thus, the average distance to the closest neighborpoint is about 0.03 m. This point density is acceptable since
we are interested in particularly exposed object features andsince the rough object geometry is sufficient for our method.
When refining the simplical complex Kd as described inSec. III-C, we choose the maximal edge length, d, as 0.06 m.
Noise, randomness in sampling and object concavity can leadto detection of small and negligible loops in Kd . We rejectsmall loops where the maximal distance between any twoloop vertices is below 0.09 m.
When evaluating the method in simulation, we apply astraightforward grasping policy as proposed in [3], [42]. As
in other recent work such as [11], we initialize the end
effector in parallel pre-shape and align its GCP frame withthe grasp frame (GF) derived from the object model. Next,
the end effector is moved backwards along the frame’s Zaxis until no collision occurs. The resulting end effector poseand joint angles define the grasp. If the backwards movement
does not remove all collisions within 0.08 m, we consider
the grasp to be obstructed.
If a grasp is not obstructed, its reliability is tested bysimulating pushing, turning and pulling interactions. While
keeping the object fixed, we perform 10 separate sequencesof 100 interleaved random rotations and translations of theend effector. Each random motion is executed until it leads
to separation of the end effector and object or until contact isachieved. A grasp is considered failed if the end effector andobject are separated at least once. We have found that theresults do not change significantly for sequences with morethan 60 motions.
To describe the quantitative results, we employ the terminology of a classification experiment in the following. In
our approach, we denote non-separated i.e. secure grasps
as true positives, and obstructed or failed grasps as false
positives. Fig. 4 shows for each pair of end effector andobject, the true positive rate (red), and the rates of failed(black), and obstructed (blue) grasps each. The rejection
rate of the filtering step (yellow) describes how much of
the handle-parts cannot be grasped by the particular end
effector. The true positive rate ranges from 72.93% in thecase of the DLR 1 hand with the Bag object (Fig. 5.2b) to
100% in the cases of Armar III and DLR 1 hands with the
Purse, Schunk SDH with the Travel Bag and all cases of
the iCub hand. The overall precision is 94.91%. The largest
false positive rates are caused by the two large hands, DLR 1
and Schunk SDH. On the Bag object, the separability is
predominant (Fig. 5.2b and Fig. 5.3b), while on the Chair
object, most false positives are obstructions and are therefore
caused by the simple grasp policy.
A qualitative analysis is conducted by inspecting Fig. 5,
where we show the locations of successful, failed and obstructed grasps on the objects, next to robot poses of successfully executed grasps. The marker colors correspond to thecolors in Fig. 4. It can be observed that the accepted grasp
frames describe parts that are well suited for enveloping

grasps. Frames inside a large volume are rejected. For the
Purse object, it is clearly visible how our approach respects
the robot hand size by only selecting grasp frames on the top
part of the handle for large hands, while the smaller hands
still grasp from the side. This property can also be observedon the Lawn Mower, where the small hands cannot envelope
the control lever box and therefore grasp frames are only
generated for the large hands. For the Bag, the failed grasps
are located on a narrow part of the belt which prevents thelarge fingers from closing properly.
However, most of the time at least one of the two rotatedcopies of a grasp frame does not lead to a failed grasp.
The structure of the Chair results in obstruction of thelarge hands at the backrest since our approach does notconsider the rest of the object when judging a loop-local
volume approximation. Obstruction also occurs in corners
since, there, the loop tangent used to define the grasp frameorientation is not completely aligned with the object shapeand leads to a non-perpendicular placement by the grasp
policy. Both the Purse and the Travel Bag object showthat our approach can place large hands on relatively small
handle-parts. One loop was detected on the Purse, Bag andWheel Bag, two on the Lawn Mower and seven on the Chair
object.
B. Experiments on Real Robot Platforms
In the following experiments, we relax the assumptionof the full-view point cloud and rely on subsampled noisy
Kinect data to show the applicability of our approach in realworld scenarios. In the first experiment with a Kuka arm anda Schunk SDH hand, the sensor is calibrated relative to therobot frame while in the experiment with a Nao humanoid,
the robot is placed on a visual marker in the scene after thescene has been analyzed, inducing additional uncertainty inthe robot’s pose.
1) Nao Humanoid Robot: The experiments using a Nao
robot consider two different ways of picking up a small
basket: grasping the basket at the handle with chain forming
grasps and hooking the handle employing one whole arm. For
robots with limited workspace – such as a Nao – reachability
and kinematic feasibility become important issues in a nonmobile setting. Therefore, the computed grasping framesneed to be carefully selected. Here, we simplify the problem
by applying a simple distance-based scoring function that
selects the frame closest to the torso. Fig 6 shows the courseof one grasping and one hooking experiment at different time
instants from left to right.
In the grasping experiment, an iterative inverse kinematicsolver is used and initialized at a starting position suitable forthe selected target frame. As a sample application of chain
forming grasps, the robot later rises up, turns its arm andlets the handle slide through the hand to establish the proper
object configuration for carrying. In case of the hooking
experiment, the robot executes a five-waypoint-based motion
template parametrized by the selected target frame to pickup and lift the basket in one action. For these experiments
the Kinect sensor was placed at a distance of about 2 m.

a) Purseb) Bagc) Chaird) Lawn Mowere) Travel Bag

Tp.: 102

Tp.: 66

Tp.: 75

Tp.: 102

Fp.: 0

Fp.: 0

Fp.: 3

Fp.: 0Rj.: 106

Rj.: 138

Rj.: 130

Rj.: 102

Tp.: 335

Tp.: 194

Tp.: 221

Tp.: 344

Fp.: 9Fp.: 72

Fp.: 61

Fp.: 0Rj.: 218

Rj.: 296

Rj.: 278

Rj.: 220

Tp.: 1402

Tp.: 961

Tp.: 1188

Tp.: 1216

Fp.: 24

Fp.: 249

Fp.: 56

Fp.: 4

Rj.: 1502

Rj.: 1722

Rj.: 1696

Rj.: 1624

Tp.: 598

Tp.: 613

Tp.: 623

Tp.: 532

Fp.: 2

Fp.: 3

Fp.: 3

Fp.: 0Rj.: 252

Rj.: 252

Rj.: 232

Rj.: 326

Tp.: 149

Tp.: 137

Tp.: 142

Tp.: 142

Fp.: 1

Fp.: 3

Fp.: 0

Fp.: 0Rj.: 86
1) Armar III (right hand)Rj.: 100
2) DLR 1

Rj.: 98

Rj.: 943) Schunk SDH4) iCub (right hand)Fig. 4. Quantitative results of the synthetic experiment: The bar plots show the true positive rate of the applied heuristic, i.e. secure grasps, as the top
bar (red). The false positives are divided into failed grasps (black), and obstructed grasps (blue). The bottom bar shows the rejection rate of the heuristic
function (yellow). The colors are chosen according to the markers in Fig. 5. The absolute numbers for true positives, false positives, and rejected grasp
frames (Tp., Fp. and Rj.) are given next to each plot.

2) Kuka Arm & Schunk SDH: In contrast to the synthetic data experiments, we employ an approach vector based
grasp policy. We first move the Schunk SDH in pre-shape
to a pose 0.2 m in front of the grasp frame. Then we movethe end effector forward and finally close the fingers until aclosed chain is formed. The experiment consists of executing
this grasp policy on several grasp frames at representative
locations for three different objects with subsequent lifting.
Fig. 7 shows the robot grasping a children’s chair, a basket
and a real-sized backpack. The images indicate that ourapproach results in secure chain forming grasps and the
supplementary video confirms that each object, including thesoft deformable backpack, can successfully be grasped.

V. C ONCLUSION
We have presented work on generating grasps that enableclasping, latching and hooking of everyday objects thatexhibit handle-like parts. The proposed method is topologybased and results in an object representation suitable forobjects with holes. The representation is used to identify
object parts suitable for grasping and incorporates local
volume information about these. A whole grasp synthesis
framework that utilizes such loops has been presented andevaluated on two real robot platforms.
The approach is complementary to a local contact-based
force-closure analysis as it depends on global topologicalfeatures of an object. We have shown that our grasp synthesis
framework leads to caging grasps that are robust under realworld measurement noise. As demonstrated, the approachis also applicable to soft and flexible objects. We have
performed an extensive evaluation with four robotic hands
in simulation as well as using a real setup with a Kinect
sensor, a Schunk dexterous hand and a Nao humanoid robot.
Our current work is exploring the use of different handpre-shapes for grasping and the use of an improved grasp
policy featuring eigengrasps and motion planning. We arealso interested in coupling this work with semantic information that constrains the set of viable grasps based on the
robot’s intended task, as demonstrated in our previous workin [44], [45].
ACKNOWLEDGMENT
This work was supported by FLEXBOT (FP7-ERC279933), the Swedish Research Council and the Swedish
Foundation for Strategic Research. We thank Yasemin
Bekiroglu, Alejandro Marzinotto, and Francisco Vina for
their help with the robot experiments.

Fig. 7. Kinect data is used for the execution of grasp policies for several
generated grasp hypotheses. Details of the execution can be seen in the
supplementary video.

R EFERENCES[1] C. Ferrari and J. Canny, “Planning optimal grasps,” in IEEE ICRA,
vol. 3, 1992, pp. 2290–2295.

a) Purseb) Bagc) Chaird) Lawn Mowere) Travel Bag

1) Armar III (right hand)

2) DLR 1

3) Schunk SDH4) iCub (right hand)Fig. 5. Visualization of the experiments with synthetic data using a modified version of Simox [43]: For each pair consisting of a robot hand and an
object, we display the generated grasp center points of a grasp frame (GF, see Sec. III-D and Sec. IV-A) and selected examples of successful grasps. The
markers indicate positions of rejected GFs (yellow), successful GFs leading to clasping (red), failed GFs leading to separation (black), as well as GFs
leading to positioning failures due to obstruction (blue). The colors here correspond to the bar plots in Fig. 4.

Fig. 6. Examples of the experimental evaluation performed with a Nao humanoid robot. Top row: The robot moves its right hand into approach positionand subsequently grasps the handle. It stands up, lifts its arm and lets the the basket slide into carrying position. Bottom row: The robot hooks and lifts
the basket in one action by performing a grasp frame-parametrized motion template.
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