Bridging the Gap between Task Planning and Path Planning
ABSTRACT Autonomous service robots have to recognize and interpret their environment to be able to interact with it. This paper focuses on service tasks such as serving a glass of water where a humanoid two-arm-system has to acquire an object from the scene. A task planner should be able to autonomously discern the necessary actions to solve the task. In the process, a path planner can be used to compute motion sequences to execute these actions. To plan trajectories, the path planner requires a pair of configurations, the start and the goal configuration of the robot, to be provided e.g. by a task planner. This paper proposes a method to autonomously find the goal configurations necessary to acquire objects from the scene and thus makes an attempt to bridge the gap between task planning and path planning. The method determines where to grasp an object by analyzing the scene and the influence of obstacles on the intended grasp location. For the case where the goal object can not be grasped due to obstructing obstacles, a solution is proposed
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ABSTRACT: Autonomous mobile robots such as planetary rover need task and path planning abilities in order to fulfill their assigned missions. Task planning is caracterized by a symbolic reasoning and aims at defining a sequence of actions which will be executed to achieve the goals of the mission. Path planning allows to find some ways in the environment to reach these goals and corresponds to a geometric reasoning. Coupling these two kinds of reasoning presents open issues such as the description of the environment and the consideration of geometrical constraints that must be verified in order to act and move. This paper adresses these issues by studying different ways to couple a symbolic and a geometric reasoning and by proposing a hybrid planning architecture allowing to solve problems in which a mobile robot has to act and move in the environment.
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ABSTRACT: Planning a mission for a mobile robot involves the use of a symbolic and a geometric planner. The gap be- tween their internal representation of the environment is an open issue and current researches are conducted without unified formalisms and algorithms. In this pa- per, we propose to extend the classical planning formal- ism in order to model actions with geometric precon- ditions and we propose, develop and test a constraint satisfaction method based on non-linear programming that aims at defining a destination attitude for motion planning.
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ABSTRACT: Mobile robots such as explorer rovers need task and path planning abilities in order to fulfill their assigned missions: path planning to plan their movements and task planning to plan their actions. The coupling between these two kinds of planning presents open issues such as the description of the environment and the consideration of geometric constraints that must be verified in order to act and move during an action. This paper addresses these issues by proposing an architecture in which a hierarchical task planner sends requests to a path planner in order to check the feasibility of actions. Requirements allowing the path planner to produce an answer are presented as well as the description of planning operators. Finally, we specify the mechanism and the communication language by which the task planner produces requests and takes into account answers.AIP Conference Proceedings. 06/2008; 1019(1):162-167.
Bridging the Gap between Task Planning and Path
Franziska Zacharias, Christoph Borst, Gerd Hirzinger
Institute of Robotics and Mechatronics
German Aerospace Center DLR
Abstract—Autonomous service robots have to recognize and
interpret their environment to be able to interact with it. This
paper will focus on service tasks such as serving a glass of water
where a humanoid two-arm-system has to acquire an object from
the scene. A task planner should be able to autonomously discern
the necessary actions to solve the task. In the process, a path
planner can be used to compute motion sequences to execute
these actions. To plan trajectories, the path planner requires a
pair of configurations, the start and the goal configuration of the
robot, to be provided e.g. by a task planner.
This paper proposes a method to autonomously find the goal
configurations necessary to acquire objects from the scene and
thus makes an attempt to bridge the gap between task planning
and path planning. The method determines where to grasp an
object by analyzing the scene and the influence of obstacles on
the intended grasp location. For the case where the goal object
can not be grasped due to obstructing obstacles, a solution is
A main goal in the development of autonomous service
robots is, that they can be commanded by the human in a
very natural way, like e.g. ”Serve a glass of water!”. Therefore
a robot like the DLR robot ”Justin” (fig. 1) must be able
to decompose the task into a set of subtasks by itself. The
subtasks and their ordering depend on the commandedtask and
on the scene. For serving water e.g. some command dependent
subtasks are: finding a water bottle, grasping it, localizing a
glass and pouring water in the glass. Depending on where the
objects are in the scene, some movements by the two arm
system have to be performed.
This paper will focus on the problem where to grasp an object.
At first glance the problem seems solved by using a state
of the art path planner to get the arm to the desired grasp
position. But where is the desired grasp position? With a
simple device like a parallel jaw gripper, the small number
of possible grasping positions can be brute force sampled .
But for a dexterous hand like the DLR Hand II an almost
infinite number of possible grasps can be generated , .
Hence, it is desireable to parameterized the grasp planner in
a way that kinematic constraints of the arm and geometric
constraints of the scene (obstacle objects) are taken into
account. Unfortunately, information about these constraints
cannot be extracted from most state of the art path planners
although they obviously take them into account.
Two classes of path planners can be distinguished, the
the task planning approach.
The DLR humanoid two-arm system ”Justin”, the target system for
first class plans in configuration space and the second class
plans paths in cartesian space. In configuration space, global
probabilistic path planners as presented by Lavalle et al.
 and Kavraki et al.  are state of the art. They are
probabilistically complete, i.e. guarantee to find a solution
whenever one exists provided enough samples. Local path
planners ,  also working in configuration space can be
a good alternative to using global path planners. While they
are not complete, they work well in scenarios composed of
only few objects. Furthermore, local path planners have the
advantages of being faster  and of generating smoother
paths than global planners.
A planner in cartesian space like the one presented by Iossi-
fidis et al.  plans the tool center point (TCP) movement.
Compared to planners working in configuration space, the
influence of the scene can directly be incorporated e.g. via
an attractor dynamics approach. Incorporating the kinematics
of a redundant robot in cartesian space is difficult, thus TCP
planners in general are not complete. Nevertheless they are
able to plan straight paths in cartesian space to the goal which
are more intuitive for humans to predict than straight paths in
configuration space which result in curved motions in cartesian
Everyone of these approaches needs a collision free, reachable
start and goal configuration of the robot arm to be able to
plan a geometric trajectory. The start configuration in general
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Proceedings of the 2006 IEEE/RSJ
International Conference on Intelligent Robots and Systems
October 9 - 15, 2006, Beijing, China
is known. When intending to grasp an object, there is a huge
number of possibilities where and how to grasp it. Thus there is
an undetermined number of goal configurations, one of which
has to be chosen for use by the path planner. Finding the best
grasp might lead to an unreachable arm configuration while
defining a certain arm configuration might lead to bad grasps.
This paper tries to analyze the problem of computing a TCP
position and orientation that is reachable and can be used to
accomplish a given task (fig. 1).
Lozano-Perez et al.  recognize that to solve pick-and-place
tasks, choosing how to grasp an object is crucial. They state
that the choice of a good target position depends on the en-
vironment. The hand position and resulting arm configuration
have to be free of collision and reachable. In principle, also
the availability of a path between start and goal configuration
should be checked. Given the fact that there can be a huge
number of goal configurations and that a complete path
planner’s worst-case time-complexity is exponential, checking
the availability of a path for each possible goal configuration is
computationally too expensive. The question of the existence
of a path will not be addressed in this paper, but the goal
configuration will be chosen in a manner that is beneficial for
path planners and paths in general are found easily. Lozano-
Perez et al. operate with a parallel jaw gripper only in the
domain of polyhedral models and thus have only a constrained
set of possible grasps for an object. They pick a grasp location
and try to plan a path, repeating the process if no path could
be planned to the grasping location. Using the service robot
”Justin” we have a highly redundant robot with dexterous
hands. Thus the set of grasps for an object is infinite and the
brute force method of iterating over the whole set of grasps
is not feasible.
W¨ osch et al.  use an 8-DOF robot arm and a parallel
jaw gripper. They define by hand a set of approach TCPs,
sample the TCP orientation and check whether there is one
among them that results in a collision free robot configuration.
Depending on the number of TCPs, they report that the
determination of an optimal approach TCP in this brute force
manner takes longer than the actual path planning. Thus a
feasible solution is needed to close this gap.
Iossifidis et al.  try to attain straight line end-effector
trajectories. An attractor dynamics approach to trajectory
formation is used, which influences the velocity as well as
two angles characterizing the TCP orientation. If obstacles
obstruct the direct path to the target object, they try to grasp
the object from above. Obstacles are avoided by choosing an
inverse kinematics solution where the elbow position is high.
Although they report that the trajectories are collision free in
all experiments, this fact is not guaranteed. Using this method
to grasp a bottle for a pouring task, before being able to pour,
the bottle has to be regrasped.
The approach presented by Terasaki et al.  again operates
with a parallel jaw gripper on objects taken from an assembly
scenario. Due to their choice of objects and gripper, the
number of possible grasps is limited. The target object is
analyzed to obtain a set of candidates for grasping positions.
The space around each grasping position is described by a so-
called open space characterization which represents approach
directions by pyramidal slices labeled with a measure of the
available free space in that direction. This characterization is
used to select among the grasp candidates. If no grasp could
be found, the method stops.
Observing the human grasp process it becomes evident that
objects are grasped only in a certain area with respect to the
human’s own position and orientation. The grasp placement is
influenced by the presence of obstacles . If the target object
is obstructed, it is also an option to displace the obstructing
obstacles before applying a safe grasp to the target object. We
integrated these aspects in the methods described in sections
II and III.
To plan trajectories we use a local path planner  since
for scenes of low complexity local path planners tend to be
fast, provide smooth trajectories and can cope with changing
scenes. We assume that we have a model-based object recog-
nition that can label objects. Section II presents a method to
analyze the scene using object recognition and to decide where
to grasp the object in the presence of obstacles. Section III
deals with the issue of how to react if the target object is
inaccessible. Having solved the problem of placing a grasp
and determining a collision free reachable goal configuration,
all prerequisites of a path planner are met and an autonomous
task planning process can schedule the path planner when
composing the action plan to solve a given service task.
Section IV presents some experiments for scenarios with a
varying number of obstacles. Section V concludes the paper
and outlines future work.
II. OBSTACLES INFLUENCE THE TCP PLACEMENT
Human grasps depend on and adapt to a given environment
. In this section we present a method that chooses where to
grasp an object depending on the robot’s kinematic structure,
surrounding obstacles and the position of the robot relative
to the target object. It models a right arm behavior since we
intend to use the method on the two arm system ”Justin”.
Modeling right and left arm behaviors is reasonable in view
of the human model and to avoid interference between the
two arms (fig. 1). To concentrate on the problem at hand
”Where should the robot place a grasp?”, this first version
of our algorithm projects the 3-D scene onto the table surface
and bases its decisions on the resulting representation.
To determine an area of preferred grasping directions two
approaches are proposed. Using a brute force approach, we
sample the circumference of the object to be grasped and
compute the inverse kinematics for each sampled position. We
then search for the largest connected region of valid inverse
kinematics solutions and search this stretch for good grasp
approach directions (fig. 2).
The second method is motivated by the observation that
humans grasp an object usually in a certain area with respect
to themselves. Therefore the heuristic approach we propose
depends on the robot’s position. The object’s coordinate sys-
tem is oriented so that its y-axis is determined by the line
table is analyzed concerning kinematic reachability. The object footprints are
represented as circles. The inverse kinematic was able to find solutions at
those locations where to circle has gaps. The circumference of the object was
sampled with 5 degrees resolution. The object frames (blue) resulting from
the heuristic method are sketched into the plot.
A scenario (see fig. 3) where 6 objects are distributed across a
connecting the position of the robot base and the center of the
target object (fig. 3). Grasps are searched in the 4th quadrant
of the resulting object coordinate system.
Fig. 2 compares both approaches. Six objects, their circum-
ference represented as circles, are distributed across the table
(robot-to-table scenario see fig. 3). The inverse kinematics
found solutions at those locations where the circle has gaps.
The object coordinate systems resulting from the heuristic
approach are sketched by hand into the plot. Since the shown
grasping approach directions and TCP orientations correspond
to a robot arm-hand system configured as a right arm, the
solutions for the inverse kinematics are mostly available on
the right hand side of the objects as seen from the robot point
of view. We observe that the heuristically determined region
covers the biggest connected stretch where inverse kinematic
solutions are available.
The solutions lost through applying the heuristic mostly are
farther away from the robot. If due to obstacles no grasp can
be found in the region specified by the heuristic, in most cases
no collision free solutions for the inverse kinematics will be
found for grasps in regions farther away from the robot. In
those situations the robot arm will collide with the objects
that already obstructed the regions nearer to the robot. The
ability to reach around obstacles in regions far away from the
robot is kinematically limited. The heuristic method is clearly
faster than the brute force method. Furthermore, it is evident
that the brute force method does not scale to 3D.
In the following, we will use the heuristic method to determine
the area to search for grasps. Figure 3 shows the DLR robot
”Justin” and a table set with objects. A bottle is represented
by the green object. The robot tries to grasp it in the area
labeled G, according to the heuristic method presented above.
Once the coordinate system of the target object is oriented
according to the heuristic, the remaining movable obstacles
robot base frame
target object frame depends on the robot position. The heuristic method aims
at placing the grasp in the region outlined by the dashed quarter of a circle.
”Justin” approaches the green target object. The orientation of the
are transformed into the target object frame and their influence
on the grasp placement is determined.
A potential field approach could in principle be used to deflect
the grasp approach direction of the TCP. We refrained from
using potential fields for a number of reasons. A potential
field approach incrementally draws the robot towards the goal
using gradient descent along the current potential. While this
is acceptable for mobile robots which use the potential field to
e.g. generate velocities, we need quick decisions concerning
only the grasp approach direction. The actual motion of
the robot arm is planned using a path planner. Moreover,
Borenstein et al.  reported problems with potential fields
when the clearance between obstacles was small. The resulting
repulsive forces could be such that the robot was pushed away
although it would be able to pass. Potential fields thus would
need to be finely tuned to guarantee geometrically consistent
decisions. Since passing objects in close vicinity is essential
in manipulation tasks, we chose to adapt another approach.
The Borenstein’s vector field histogram method  presents
an alternative to using potential fields for collision avoidance.
A polar histogram H of certainty values is built around the
robot’s momentary position. H is comprised of n angular
sectors of width α. Each sector i corresponds to a direction
i·α, i ∈ [1,n] and represents the polar obstacle density in that
direction. The robot then selects that sector i from all polar
histogram sectors, where the obstacle density is smallest and
drives in that direction. We adapt the approach of Borenstein et
al. to our problem. The polar histogram H is now centered on
the target object, oriented according to its coordinate system
(see fig. 3). All other objects are considered to be obstacles.
Only the region G is sampled with stepwidth α to generate
candidate directions from which to approach the object. To
compute the obstacle density values for a sector, the concept
of obstacle influence is introduced. This replaces Borenstein’s
functions for density computation. Each obstacle has a region
of influence. The region of influence should be such that the
robot tries to grasp the target object from a direction with
as much free space as possible to operate in. Considering
fig. 4 (left), valid grasps for the target object (empty black
Fig. 4. (left) The region of influence (dashed ellipses) of obstacles (filled red
circles) is illustrated. The goal object (empty black circle) is grasped in the
shaded region. (right) The values used to compute the second half of eq. 3 are
visualized. The position of the robot is symbolicly sketched in both figures.
circle) should be placable in region B given enough free
space while region A should be preferred since it is easier
to approach. Easily reachable grasps ensure the existence of a
path to the grasp and are beneficial for a path planner. For these
reasons, we describe regions of influence by bivariate gaussian
functions oriented towards the goal object (fig. 4 (left)). The
influence poiof a single obstacle on a point in sector i
characterized by dkand sk(fig. 4 (right)) is computed using
the standard deviations σskand σddescribing the extension of
the bivariate gaussian, the distance dk from the target object
border to the currently considered obstacle k, and the distance
skin sectors between the center of the obstacle and the current
sector i. Eq. 1 states the gaussian function. The choice of
the standard deviation σdin the direction of the target object
depends on the size of the robot hand and is the same for all
obstacles k. Given that we want to grasp the target object (fig.
4) in region B, the robot hand must fit into the clearance. We
set σd= 0.175 m corresponding to the approximate width of
the hand. The second standard deviation σskcorresponds to
the sectors spanned by an obstacle.
2 · π · σd· σsk
To represent the grasping preference, the influence pg of the
goal object is described as in eq. 2, representing a preference
towards the middle of the shaded region of fig. 4 (left). The
parameter pb describes the base influence value exercised
by a target object and is set to 0.02 in our experiments.
The shaded region is sampled with a granularity α to obtain
candidates for approach directions. The variable i describes the
sector currently examined while the corresponding candidate
approach direction is φ = i · α +α
2with i ∈ [0,90◦
α), i ∈ N.
φ = α · i +α
The influences of all obstacles are mapped onto a one-
dimensional representation of the target object’s environment,
the so-called polar histogram H. Each sector i accumulates
influence values from a set of obstacles O. The influence
values generated by the target object expressing the grasp
preferences and the influence of obstacles are combined to
characterize each sector i (eq. 3). The resulting value p(i)
indicates how accessible sector i and thus the corresponding
approach direction φ is for the robot hand. Equation 3 results
in a smooth mapping of obstacle influences and the grasping
preference to 1D.
p(i) = pg(i) +?
Figure 4 (right) visualizes the variables for accumulating
obstacle influences poifor sector i for two obstacles. The
distances in sectors s1 and s2 are shown from the sector of
the obstacle to the sector currently considered. Furthermorethe
distances d1, d2along the x-axis of the obstacle’s coordinate
frame are drawn into the sketch. The resulting accessibility
value is obtained by inserting all variables in equation 3.
We measure the individual obstacle influence at the points
indicated using the assumption that if the robot hand is
allowed to pass this point where the obstacle has the maximum
blocking extension, it can also pass the remaining part of the
To decide where to grasp the object, we search the approach
direction φ with the minimum total influence value p(i). If the
influence value of sector i is below the accessibility threshold
t, the target object is assumed to be unblocked and we try to
grasp the object in that region. This position should then be
supplied to a grasp shaper which shapes the grasp aperture and
finger configuration to the available free space and the target
Should the influence value of every approach direction φ ex-
ceed the accessibility threshold t, the goal object is considered
to be blocked. Sectors are furthermore considered blocked,
if no solution for the inverse kinematic of the robot arm is
available. In the next section we expand the presented method
to deal with blocked objects.
i ∈ [0,90◦
III. SAFELY APPROACHING BLOCKED OBJECTS
If the target object is blocked by obstacles, especially
when working with uncertain vision information, the safest
way to grasp the target object is to clear some space by
removing obstacles beginning with those nearest to the robot.
Modifying the scene to suite ones needs is generally termed
rearrangement planning .
To determine which object can be removed, the scene is ana-
lyzed with respect to the influence of object removals on the
graspablility of the target object. Each obstacle’s accessibility
is determined by the approach described above. A tree is built
to find a sequence of valid obstacle removals. The tree building
process stops as soon as the target object becomes accessible
by one sequence of object removals. Tree nodes of the class
leaf are inserted when the target object becomes accessible.
They terminate a valid solution path. To determine an optimal
removal sequence that path to a leaf is searched, that results
in a minimum value for the accessibility measure of the target
object. Thus the removal tree fullfills a function similar to the
precedence graph combined with the permutation net proposed
Fig. 5. The target object T is not accessible without removing some objects.
The task planner builds a tree to decide which objects to remove. Leaf nodes
terminating the removal of objects are marked by dotted circles.
by Ben-Shahar et al.  in determining the sequence in which
to remove the objects. Ben-Shahar et al. use the precedence
graph in planning rearrangement tasks for a simple 2D robot.
All objects scheduled for removal appear in the corresponding
manipulation path  that defines a sequence of transfer and
transit movements. In the experiments presented in this paper,
obstacles are removed to an appropriate position and the goal
object is grasped.
The methods presented in the previous section were inte-
grated to form a module we call the task planner. It param-
eterizes the Baginski path planner . For our experiments
we used the DLR humanoid two-arm-system ”Justin”. The
complete system has 43 degrees of freedom (DOF) and
consists of two arms with 7 DOF each, two hands with 12 DOF
each, a PanTilt unit for the head with 2 DOF and a torso with 3
DOF as well as one passively coupled link. ”Justin” is mounted
on a table set with objects. The following experiments were
carried out in simulation. Objects on the table are represented
by bounding volumes. This is necessary to use the setup as
input for the path planner. Objects are assumed to be rotation
symmetric, recognized and labeled.
For the first experiment the scene setup is as shown in fig.
5(1). Obstacles are colored red and labeled 0-2. ”Justin” has
the task to grasp the green object labeled T in the preferred
sector as described in the previous section. As can be seen in
fig. 5(1) the target object T is not accessible as accessibility
was defined in section II. The task planner analyzes the scene
and computes a tree of objects to remove (fig. 5(4)). In the
initial set up, only object 1 is accessible and inserted into
the removal tree. On the second tree layer object 1 has been
removed. Now object 0 and object 2 are accessible but only
the removal of object 2 results in the target object becoming
accessible. Thus the task planner computes that to reach the
target first object 1 and then object 2 have to be removed.
approach angle in deg
before object removal
object 1 removed
object 2 removed
inv. kinematics solution available
the heuristically determined approach angles. The red line represents the
accessibility threshold t = 0.04. The black bars mark the area where an
inverse kinematics solution is available. The dashed lines mark for each valid
inverse kinematics solution, if the robot arm would be in collision. Those
intervals are free of collision where the dashed line falls to zero.
The accessibility values p(i) for experiment 1 are plotted across
For each object the computed approach direction and robot
configuration are visualized in fig. 5(1-3). The polar histogram
H of accessibility values for region G is shown in fig. 6.
The graph shows the accessibility of the target object in the
situation at the beginning of the experiment and after removing
obstacle 1 and obstacle 2. The angles (in deg) in the polar
histogram correspond to angles measured clockwise from the
positive x-axis of the target object coordinate system from 0
to 90 degrees (compare fig. 3). The removal of object 1 and
2 results in a constant lowering of the consecutive curves till
the brown curve falls below the accessibility threshold and
the target object can be grasped. The vertical bars border
the region where an inverse kinematics solution was available
for the target object in the respective sector. Furthermore,
the collision information after an object is removed is illus-
trated by dashed lines. Their color indicates the part of the
experiment they belong to. Those region are free of collision
where the dashed line is zero. It can be seen that only after
the removal of objects 1 and 2 there are collision free goal
configurations. The point where the brown curve falls below
the accessibility threshold is nearly identical to the point
where collision-free configurations become available. Since
the configuration belonging to the approach angle with the
minimum accessibility value is chosen, the presented method
could be used as a fast heuristic alternative to replace the
In the second experiment (fig. 7) the scene is much more
cluttered. The green object to be grasped is surrounded by
obstacles and not accessible. Automatically a valid sequence of
object removals is computed to clear sufficient space to grasp
the target object using the algorithms presented in sections
II and III. Removals begin from the outer skirts and move
inwards. Fig. 8 presents an analysis similar to fig. 6. The
plotted collision information shows that there are collisionfree
configurations when choosing approach angles between 42◦
and 62◦, i.e. between object 3 and 4. While these are valid goal
configurations, they are very hard to reach for path planners
cleared automatically to get to the object.
The green object is to be grasped but is inaccessible. A space is
0 20 40
approach angle in deg
before object removal
object 2 removed
object 5 removed
object 4 removed
inv. kinematics solution available
Fig. 8. Accessibility and collision values for experiment 2.
since they belong to the class of narrow passage problems.
The presented method continues to remove objects and thereby
creates a situation that is benificial for path planners.
In manipulation planning as by Alami et al.  and
rearrangement planning as by Ben-Shahar et al.  a task
is given as a set of start and goal positions of every robot
and object. Manipulation and rearrangement planners compute
valid sequences of transit and transfer tasks to achieve a task
as specified above. A task in the context of this paper is
phrased more general, e.g. grasp bottle x. The main aim of
this paper was to provide a method to automatically find a
goal configuration to achieve the task. Thus we enabled a
task planner to parameterize a path planner. The object to be
grasped and the other scene objects influence the computation.
As a first step we addressed the problem in 2D by allowing the
TCP to be deflected by obstacles on an arc around the z-axis of
the target object coordinate system. If the target object is not
accessible, the scene is modified. The proposed method is used
to determine the precedence of objects should a rearrangement
of the scene be necessary to solve the task. The method is also
able to cope with an increased number of objects (fig. 7).
In future work the method will be expanded to work on the
3D scene directly instead of on a 2D projection. Trivariate
gaussians can be used and a part of a sphere can be sam-
pled. Clearly, with the added dimension the computational
complexity rises. While the extended method could still be
applied to simple scenes, it would probably not be feasible for
very complex scenes as presented in fig. 7. Thus an integrated
analytic solution that is computationally feasible is needed.
This method should determine a desirable region to place
grasps and filter the huge amount of grasps according to
criteria such as the freeness of collision, the possibility of
finding stable grasps and kinematic reachability.
When interacting with humans a service robot should act so
that humans feel safe around it. In the opinion of the authors
it is therefore important that the actions of a service robot do
not seem awkward to users and that users are able to predict
them to some extend. Thus a right arm behavior was shown
in the experiments. In future analysis criteria measuring how
natural grasp and arm positions are should also be taken into
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