PreprintPDF Available

Merging physical and social interaction for effective human-robot collaboration

Preprints and early-stage research may not have been peer reviewed yet.

Abstract and Figures

For robots to share the environment and cooperate with humans without barriers, we need to guarantee safety to the operator and, simultaneously, to maximize the robot's usability. Safety is typically guaranteed by controlling the robot movements while, possibly, taking into account physical contacts with the operator, objects or tools. If possible, also the safety of the robot must be guaranteed. Not less importantly, as the complexity of the robots and their skills increase, usability becomes a concern. Social interaction technologies can save the day by enabling natural human-robot collaboration. In this paper we show a possible integration of physical and social Human-Robot Interaction methods (pHRI and sHRI respectively). Our reference task is object handover. We test both the case of the robot initiating the action and, vice versa, the robot receiving an object from the operator. Finally, we discuss possible extension with higher-level planning systems for added flexibility and reasoning skills.
Content may be subject to copyright.
Merging physical and social interaction for effective human-robot
Phuong D.H. Nguyen1, Fabrizio Bottarel1, Ugo Pattacini1, Matej Hoffmann2, Lorenzo Natale1, Giorgio Metta1
Abstract— For robots to share the environment and cooperate
with humans without barriers, we need to guarantee safety
to the operator and, simultaneously, to maximize the robot’s
usability. Safety is typically guaranteed by controlling the robot
movements while, possibly, taking into account physical contacts
with the operator, objects or tools. If possible, also the safety
of the robot must be guaranteed. Not less importantly, as the
complexity of the robots and their skills increase, usability
becomes a concern. Social interaction technologies can save the
day by enabling natural human-robot collaboration.
In this paper we show a possible integration of physical
and social Human-Robot Interaction methods (pHRI and sHRI
respectively). Our reference task is object hand-over. We test
both the case of the robot initiating the action and, vice versa,
the robot receiving an object from the operator. Finally, we
discuss possible extension with higher-level planning systems
for added flexibility and reasoning skills.
In the near future, robots will certainly leave their safety
cages to work alongside human workers. Removing the
safety cages requires new capabilities that would eventu-
ally enable robots to analyze and assess both the physical
(e.g. position and movement of humans, the presence of
objects and useful tools) and the social properties of the
environment (e.g. emotions and intentions of humans). These
requirements translate into the development and, occasion-
ally, further improvement of fundamental “skills”, ranging
from accurate perception (including human actions) up to the
representation of the acquired information in the form of a
“shareable knowledge” across different skills. The ultimate
goal is to safely and effectively plan and execute generic
tasks in the factory floor as well as to assist humans in
their daily chores. More pragmatically, given the current
level of technological development, we need to resort to a
variety of computational techniques such as symbolic and
sub-symbolic AI, machine learning, vision, planning and
control. The task complexity that we address in this paper is
still beyond reach of a single technique “end-to-end”, i.e. a
comprehensive approach that connects the raw sensory input
down to the motor control output. Notable exceptions can
be found in the work of Gu et al. [1] albeit deployed in
simulated environments.
Phuong D.H. Nguyen, Fabrizio Bottarel, Ugo Pattacini, Lorenzo
Natale, and Giorgio Metta are with iCub Facility, Istituto
Italiano di Tecnologia, Genova, Italy {phuong.nguyen,
fabrizio.bottarel, ugo.pattacini,
2Department of Cybernetics, Faculty of Electrical Engineering,
Czech Technical University in Prague, Prague, Czech Republic
In this paper, we take the humbler but extremely prac-
tical approach of combining methods from the physical
and social Human-Robot Interaction domains (pHRI and
sHRI respectively). It must be noted that, indeed, technology
is mature enough to deploy markerless perception of the
human body in 3D, to recognize and model generic objects
for grasping, seamlessly integrating visual perception with
whole-body force/torque control, tactile sensing, and speech-
based communication to name a few. In addition, machine
learning provides the ability to teach the robot about new
objects and tools, whose descriptions can be stored and
organized – at least for the scope of these experiments – into
a standard database. To set the stage for our work, we start by
briefly reviewing some recent pHRI and sHRI architectures,
their strengths and limitations.
Most pHRI frameworks focus on the “low-level” inter-
action (e.g., contact detection, avoidance and control) and
consider humans merely as other objects in the workspace
the robot needs to deal with. In their architecture, De
Luca et al. [2] integrate residual-based collision detection
and reaction (gathered from the proprioceptive layer) with
collision avoidance (leveraged on depth information provided
by a Kinect sensor). Since this work models the environment
by considering only obstacle-to-robot distances, we argue
that this approach is not flexible enough to scale up to more
complex collaborative tasks. Haddadin et al. [3] outline a
different solution where the robot simply switches between
different functional modes (e.g. autonomous task execution
with/without human, cooperation with human) based on the
state of the human in the robot’s workspace as detected
through proximity sensors surrounding the robots. Although
this approach combines different control techniques (e.g.
impedance control, residual-based collision detection, and
task relaxation reaction) as well as several sensory modalities
(e.g. laser-based imaging, visual-based edge filtering) to offer
safe HRI, it lacks a knowledge-based communication channel
and thus it is not seamlessly extensible to other applications.
It does a very good job though in implementing flexibility
in the physical interaction layer.
On the other end of the spectrum, sHRI frameworks often
overlook the physical aspects of the interaction (e.g safety
and physical contacts) or simply resort to path planning
methods to deal with static or slowly changing environments.
For example, Lemaignan et al. [4] in their recent work
present a cognitive architecture for service robots that sup-
ports human actions and decisions. However, it only utilizes
path planning based methods [5], [6] to guarantee that the
path of the robotic manipulator is collision free, which is
Preprint version; final version available at
2018 IEEE-RAS 18th International Conference on Humanoid Robots, (Humanoids 2018)
Disparity map
Human pose
estimation Object extractor Object recognition
Right Left
Touch detector
Tactile sensor
Object property collector
pHRI Ctrl
Sensorimotor layer Physical layer
Grasp pose
Object point
Fig. 1. Overview of the overall system comprising perception (right side) and action (left side) pathways. At the physical level, perception includes
vision and touch. The robot’s visual system allows for stereoscopic vision. Low-level motor control allows specifying the position trajectories of the joints
exploiting as feedback also a combination of pressure (from the tactile sensors) and force information (from a number of 6-axis force-torque sensors located
on the robot’s structure). The sensorimotor layer transforms raw sensory data into symbolic tokens (e.g. object identities, posture, 3D shape, human body
posture, etc.) that can be easily stored into the “object property collector” database. This symbolic knowledge is used to control action, as for example to
avoid contacts rather than to grasp objects, through reasoning modules (i.e. PPS, Object point cloud, pHRI Controller, and Grasp pose generator).
difficult to satisfy in highly dynamic environments, and in
particular when interacting with a human partner.
Moulin-Frier et al. [7] and Fischer et al. [8] developed the
so called DAC-h3 cognitive architecture. One of DAC-h3’s
main strengths is its implementation, which is an ensemble of
functional modules. Functional modules can be mapped one-
to-one to software modules of typical middleware systems.
The authors validate DAC-h3 by experimenting with human-
robot and robot-object interaction to acquire and express
procedural knowledge. Although the robot can execute a
wide repertoire of actions such as waving, pointing, pulling,
pushing objects in a table-top setting, DAC-h3 employs
exclusively predefined motor primitives and does not address
the problem of safety (e.g. avoiding human and moving
objects). It is a very good reference implementation for
sHRI. Along the same line, in Moulin-Frier et al. [9],
the authors integrate diverse AI techniques into a single
cognitive architecture that combines symbolic reasoning with
embodied behaviors, yet they do not consider the “low-level”
details of physical interaction. Notably, all the aforemen-
tioned systems employ fiducial markers and/or exteroceptive
sensors to enhance the robot perception. Nonetheless, certain
elements of DAC-h3 are readily combined into our system.
In Section IV, we speculate about a possible integration with
To summarize, the main contribution of this paper is the
design of a complete control system that merges elements of
pHRI and sHRI, namely:
A compact human-centered visual perception system for
humanoid robots;
A visuo-tactile reactive controller that allows the robot
to safely react in both pre- and post-collision phases;
A simple symbolic “storage” of information about hu-
mans, objects, and tools supporting social interaction.
Some parts of the system presented in this paper were de-
veloped and analyzed in our recent publication [10], namely
the human keypoints estimation, the reactive controller and
the Peripersonal Space representation. They are by and large
reused “verbatim” in this work. Here we demonstrate that our
approach can handle effectively different types of interaction.
We develop two main experiments, where the robot is given
an object by the human partner and it is subsequently
asked to grasp another object from a table top to perform
a handover task.
The remainder of the paper is organized as follows: we
present the method in details in Section II, and analyze
performance showing experiments and results in Section III.
Finally, in Section IV, we analyze the possibility of integrat-
ing this work into existing cognitive architectures.
A. General architecture
The underlying architecture of our framework is shown
in Fig. 1, where functional modules are classified into three
different layers described in the following:
The physical layer consists of the low-level systems of
the iCub humanoid [11]: the stereo-vision, the artificial
skin covering the robot body, and the joint actuators.
This layer allows the robot to perceive the surroundings
as well as act on the environment.
The sensorimotor layer encompasses those modules
responsible for processing the raw signals produced by
the physical layer in order to yield meaningful internal
representations: the touch detector, the disparity map,
the human pose estimation, the object extractor, the
object recognition and the skeleton3D for visual input.
Components responsible for the computation of control
signals are also listed here, such as the Peripersonal
Space, the pHRI controller, the Object point cloud and
the Grasp pose generator.
The knowledge layer contains the Object properties
collector module (discussed in Section II-C), whose task
is to store and manage the properties of the entities
perceived from the environment.
B. Environment acquisition and perception:
1) Human detection and tracking: In [10], we proposed a
real-time framework to estimate the 3D pose of humans from
the 6 DoF stereo vision system mounted in the iCub head.
The framework is composed of 2 steps: (1) a 2D human pose
detection given as a set of keypoint pixels [ui, vi] extracted
from the raw images using the DeeperCut model [12]; (2)
a 3D human pose reconstruction from 2D information and
depth map. The latter is performed by averaging the spatial
projection of each 2D keypoint along with its neighbors
through the depth map. The output set of 3D coordinates
[xi, yi, zi] is then refined by applying median filtering.
2) Context-aware object detection and tracking: To pro-
vide the robot with a context-aware ability during the collab-
oration with the human partner, we extend the above human
tracking framework to incorporate object recognition. To this
end, we adopt the method developed by Pasquale et al. [13],
which turns to be simple yet efficient in our setting, where
the system must detect and recognize objects held by the
human. The proposed image recognition system utilizes a
CaffeNet [14] deep neural network (DNN) pre-trained on the
ImageNet dataset to extract features from the input images,
and a Regularized Least Squares method for the classification
stage. The algorithm also allows partners to train the robot
with novel object via verbal annotation.
Unlike [13] employing a heuristic motion detector to
acquire cropped images for the DNN, we propose an accu-
rate and flexible solution specifically designed for the HRI
context. By resorting to the keypoint pixels obtained from
the 2D human pose computation, we precisely estimate in
real-time the bounding boxes containing the human hands
(with or without objects) that in turn are passed on to the
image recognition system for labeling purposes. The size
of the bounding boxes are constantly adapted based on the
distance of the human hands as retrieved from the depth map.
3) Physical collision detection through artificial skin:
The body of iCub is covered by a layer of artificial skin
composed of capacitive tactile sensors [15], termed skin
taxels. The poses of the skin taxels are calibrated with
respect to the kinematic model of the robot , and are kept
updated during robot movements. Thus, physical contacts
with the iCub skin can be sensed and localized accurately.
Notably, this approach differs from other recent methods (e.g.
[16]) that typically rely on proprioceptive inputs instead.
To reduce spiking effects, we aggregate multiple adjacent
tactile contacts firing concurrently over a preset threshold
into one representative super contact whose activation at
corresponds to the highest pressure value measured at the
relative taxels. The super contact is also parameterized in
terms of its location Pt
Cand normal vector nt
C, which
also encodes, to a first approximation, the collision direction
(Fig. 2).
C. Centralized knowledge representation through Object
Properties Collector (OPC):
In order to effectively cooperate with humans, robots do
not only need to perceive the surroundings through their
sensors, but they are asked to convert these representations
into a “common knowledge” that can be shared with the
partners to support reasoning and task planning. For this
purpose, we adopt an ontology based framework [17] for
knowledge representation. We consider these representations
as the centralized working memory of the robot during the
interaction with the environment. Thereby, we partially solve
the grounding problem of pure symbolic cognitive systems,
where environment stimuli are firstly transformed into lower
dimensional representations with machine learning methods,
and then are mapped into symbols (given a priori) in a
In this regard, the working memory makes use of the
Entity and the Relation to identify basic concepts and the
connections among different entities, respectively. Thus, the
perceived objects are denoted as Objects, an abstract type
of Entity, composed of some physical properties such as
position, dimension, valence (further properties like objects
affordances can be added easily). On the contrary, objects
that have self-motion abilities are deemed as Agents: humans
and robots fall under this class. A structured hierarchy of
classes can be constructed along the same line, comprising
e.g. Bodies,Emotions,Beliefs etc., as described in [17].
Leveraging on this knowledge management, the human
partner can be represented within the OPC memory as an
Agent whose Body parts are localized in 3D through vision
(see Section II-B.1). A similar process applies to the visually
recognized objects along with their properties (e.g. location,
color, valence) that are relevant to the task at hand.
D. Learned Peripersonal Space (PPS) as an adaptive em-
bodied perception layer
In Roncone et al. [18], the authors propose a dis-
tributed multisensory representation model, named Periper-
sonal Space (PPS), to associate the visual and tactile inputs
prior to and at collision time. This model is then trained by
allowing approaching objects to contact physically with the
robot skin taxels. In deployment, this representation serves
as a mapping of visual stimuli to body parts of the robot,
parametrized in terms of location Pv
Cand normal vector nv
of magnitude av
An interesting application of this concept can be found in
[10], where Nguyen et al. consider the PPS as a distributed
safety zone around the robot body. They build on the model
in order to modulate (expand or shrink) the spatial extension
of such a zone depending on the involved part, resembling
what is observed in humans (e.g. smaller zone for the
hands, bigger for the head). Formally, the modulated PPS
signal am,i(t)occurring at the i-th taxel w.r.t. the valence–
threatening value θk(t)of the k-th object at time instant tis
given by:
am,i(t) = ai(t)[1 + θk(t)],(1)
where ai(t)represents the original PPS activation.
In this work, we further exploit this adaptable PPS rep-
resentation for different collaboration contexts. Human body
parts that are meant to be contactable during the cooperation
(right hand holding an object) would entail lower activations
than other parts (left hand). This allows the robot to approach
the right hand for a close interaction while handing over
the object and to avoid collisions with other parts (i.e. left
hand, head). This contextual modulating mechanism can be
synthesized as follows:
am,i(t) =
min ai(t), ai(t)[1 + θk(t)]kcontactable
max ai(t), ai(t)[1 + θk(t)]otherwise
E. Controllers:
1) Bio-inspired reactive controller for safe physical inter-
action: Most robot movements in the interaction scenarios
can be formalized as reaching with obstacle avoidance. To
this aim, we proposed earlier in [10] a reactive controller
tasked with solving the following nonlinear constrained
optimization problem:
q= arg min
xEEd ¯
xEE +TS·J(¯
The controller aims to find the optimal joint velocities
˙qat each time instant by minimizing the distance between
the desired end-effector pose ¯xEEd and the one-step pre-
diction of the robot current pose ¯xEE , complying with the
constraints of the feasible joint range [qL,qU]and the joint
velocity limits [˙qL,˙qU].J(¯q)and TSare the Jacobian of
the instantaneous configuration ¯q and the sampling period,
In this setting, visually perceived objects elicit PPS
activations, thus reshaping the movements of the robot’s
parts through the PPS representation (see Section II-D) by
adapting in real-time the joint velocity limits (refer to [10]
for more details). Remarkably, the controller can respond
in a similar manner to tactile stimuli, hence dealing with
post-collision scenarios. Fig. 2 depicts the occurrence of a
physical contact, eliciting the activation of a skin taxel on
a robot body part. As a result, the bounding values of the
velocities of the corresponding joints (e.g. mainly the elbow
Fig. 2. React-control dealing with a tactile stimulus. The diagram shows
the quantities involved when a control point is elicited upon the detection
of a real contact (physical contact triggered by tactile sensors).
joint as visible in Fig. 2) are adapted accordingly. In formal
terms, the joint velocity constraints of Eq. 3 relative to both
visual and tactile inputs are expressed by:
qL,j = max VL,j , sj, sj0
qU,j = min VU,j, sj, sj<0
where Cis a control point attached to a generic robot link,
represented by either a mapped PPS locus or a super contact,
depending on the nature of the input signal ( i.e. visual
or tactile, respectively); JCis the Jacobian associated to
C;VCis a gain factor used for avoidance; VL, VUare a
predefined set of bounding values of joint velocities; aPPS
is either the visual (av
PPS) or the tactile (at
PPS) activation;
Kis a tunable gain. In particular, the gain Kis set to be
higher for tactile events than visual events (i.e. three times
in this implementation), reflecting the notion that a physical
collision detected by the skin system is more critical than a
collision predicted from the visual input.
2) Superquadric-based object grasping controller: Given
the desired object, the robot must be able to compute an
optimal grasping pose in terms of position and orientation.
The one-shot approaches adopted in [19] [20] suggest that
modeling objects with analytical shapes (i.e. superquadrics)
is a good assumption for autonomous grasping of partially
occluded objects. Drawing inspiration from such work we
propose our own grasp planner, relying on superquadric
shapes to model objects and employing geometric, analytical
and kinematic considerations for pose generation and evalu-
ation. The pose synthesis pipeline is integrated with the HRI
framework as outlined in Fig. 1 and consists of the 5 steps
described hereinafter.
a) Point cloud acquisition: The algorithm prompts the
OPC module to acquire the location of the object to grasp
in the field of view. Once found and segmented from the
background, the location of single object pixels in the 3D
space are retrieved from the disparity map and the informa-
tion is stored as a point cloud. Possible outliers are removed
by applying density-based spatial clustering [21].
b) Superquadric modeling: At this stage, we retrieve
the superquadric that best fits the point set PR3. The
mathematical representation of the superquadric has the
following implicit form:
1= 1,(5)
where pi={xi, yi, zi} ∈ P. The vertical axis of the
superquadric is constrained to be orthogonal to the table
surface, therefore the superquadric rotation is defined by the
angle φaround the zaxis. This is a reasonable assumption
since the objects are modeled with a single superquadric,
and constitutes the first branching point of our approach
from [19]. To find λ={xc, yc, zc, sx, sy, sz, 1, 2, φ} ∈ R9
we minimize the distance between each pPand the
superquadric surface. The problem can be cast as the least
squares minimization:
λ= arg min
sxsysz(F(pi, λ)1)2
where F(pi, λ)is the left-hand side of (5). We constrain
some of the parameters in order to obtain a convex shape
and not to extend under the table surface; therefore, such
optimization problem is constrained and nonlinear. Another
difference between this approach and the one described
in [19] is that we use the analytical gradient of (6) during
optimization, instead of finite differences.
c) Pose generation: Feasible hand poses must be gen-
erated in order to perform top and side power grasps. The
robot can use any hand it is commanded to. With respect to
[19], our approach seeks for solutions in a narrowed search
space for position and orientation:
position is constrained to the cardinal points (intersec-
tions between axes and surface) of the superquadric, so
that the palm touches the surface;
hand orientation (shown in Fig. 3(a)) is constrained so
that each pose axis is parallel to one superquadric axis;
side grasps are constrained to having the thumb always
point upwards.
Grasp poses are generated as detailed in Algorithm 1,
and are represented as homogeneous transformations gi=
0 1 )linking the robot palm to the root frame. In
Operation 14, the object is graspable if its cross section fits
in the hand of the robot. In Operation 11, the pose is rotated
around the wrist pitch to avoid collision between the thumb
and the object during approach.
d) Pose ranking: The candidate poses are then ranked
according to the square norm of the position error, calculated
with the inverse kinematics solver for iCub [22]. The poses
that reach at least a degree of positioning accuracy (e.g. error
<1cm) are further ranked according to the cost function
Jiin (7). The parameter wweighs the two components of
Ji, where Ji,1accounts for the orientation accuracy and Ji,2
favors grasps around the smallest side of the superquadric:
Algorithm 1 Grasp generation
Center sc, axes unit vectors {ax, ay, az}, size
{sx, sy, sz}of the superquadric
Grasp candidate set Sg
Grasp pose gi={Ri, Ti} ∈ Sg
1: procedure GENE RATE GRASPS(λ)
2: Sg=
3: Sgx← {ax, ay,ax,ay} gx, gysearch spaces
4: Sgy← {ax, ay, az,ax,ay,az}
5: for gi,x Sgxdo
6: for gi,y Sgydo
7: gi,z gi,x ×gi,y
8: Tiscszgi,z
9: Ri[gi,x gi,y gi,z ]
11: SgOFFS ET(Ri, Ti)
12: return Sg
13: procedure ISGRAS PFEASIBLE(R)
14: if OBJECTISGRA SPAB LE(sx, sy, sz)then
16: return true
17: return false
(a) Hand reference
(b) Computed grasp poses
Fig. 3. Grasp poses computation. In (a) the gxaxis is represented in red,
gyin green and gzin blue. In (b), the acquired point cloud is displayed
together with the modeled superquadric and computed grasp poses expressed
in the root reference frame. The poses are represented according to the hand
axes colors in (a) and the green captioned pose is the optimal given the
Ji,1=||˜oisin ˜
Ji,2= 1 sf inger
Ji=wJi,1+ (1 w)Ji,2, w [0,1]
In (7), {˜oi,˜
θ}is the axis angle representation of Riˆ
and ˆ
Ris the hand orientation that the robot can actually reach
(according to the inverse kinematics solver) with respect to
the root reference frame. smax is the size of the longest
axis of the object superquadric, while sfinger is the size of
the superquadric axis that lies in the direction of the fingers
(hand xaxis).
e) Reaching and grasping: The best pose according to
(7) is selected, and the robot reaches for it with one hand
Fig. 4. Our experimental setup with the human and the iCub sharing the
workspace. The human is sitting next to Table 1 while the iCub is located
near Table 2.
to perform a five fingers power grasp. The grasp is executed
by actuating the proximal and distal finger joints until the
load torque exceeds a contact force threshold. The grasping
pipeline is freely available online1.
In this section, we evaluate our method in two different
hand-over experiments.
Safe human-robot hand-over task, where the robot re-
ceives the object from the human.
Safe robot-human hand-over task, where the robot has
to pick up the object from a table to then perform hand-
Our experimental setup is presented in the Fig. 4, where
there are two tables, denoted as Table 1 and Table 2.
The human partner is next to Table 1 and cannot reach
objects located on Table 2, whilst the robot can reach and
grasp objects lying on Table 2. This setup is designed to
simulate the situation where the robot and the human need
to cooperate to complete a shared task, such as moving an
object from Table 1 to Table 2 or vice versa. Intentionally,
we set the hand-over phase of robot’s action long enough (at
least 15 s) to enable any possible physical interaction with
the user.
A. Safe human-robot hand-over task
In this experiment, when engaged by the human partner,
the robot has to look for the requested object the human
holds in his hand, to then take it over and place it on the
table. During this interaction, the robot movements can be
interfered by the human so that the robot needs to anticipate
possible collisions in order to guarantee a safe cooperation.
The dialogue between the human and the robot can be
scripted as below:
PARTN ER : Hi iCub, help me put the DUCK in the basket!
ICUB : I don’t have the DU CK. You have the DUCK.
Please give it to me!
(PART NER shows the DUC K to ICUB, and ICUB moves
the hand to receive the DUCK from PA RTN ER)
In details, iCub locates the human hand and the object
with its visual system, then moves its hand to approach
the object. As it is evident in the Fig. 5 and Fig. 6, the
valances of the human right hand holding the object as well
1 gen
Fig. 5. Experimental results when iCub is approaching the human right
hand holding the object. Top: the distance from the robot right hand to the
human hands are shown (red for right, blue for left; aggregated PPS visual
activation on the robot right hand are shown by the green shaded areas.
Bottom: distance between the end-effector and the target object.
Fig. 6. Experimental results when iCub is approaching the human’s right
hand holding the object and the human interferes with the robot movements
using his left hand. Relevant quantities are explained in the Caption of
Fig. 5.
as the object itself are reduced (at t9s) from normal to
approachable values, while the value of the human left hand
remains unchanged. As a result, the visual PPS activation
on the robot right hand is almost null (barring the short
time range t[11.3,11.5] s), even though the human right
hand is very close (see Fig. 5-Top). Therefore, the robot can
move directly to reach for the target object as long as it
does not detect any approaching obstacle, as illustrated by a
quickly decreasing distance between the robot end-effector
and the object in Fig. 5 (t[10,12] s). This is not the case
presented in Fig. 6, where the human moves his left hand
to interfere with the robot movements; in fact, the relative
distance (blue profile) becomes very close to 0at t11 s,
causing very high PPS activation. The iCub correctly reacts
by anticipating its planned movement for safety reasons until
the instant the human moves his left hand away (t13 s).
Afterwards, iCub continues to move its hand to approach
the human right hand to receive the object safely. A detailed
analysis of the joint velocities commanded at the robot arm
during the interaction can be found in [10].
Fig. 7. Experimental results when iCub is approaching the human right
hand to hand-over the object. Top: we show only the distance from human
left hand (blue) for sake of simplicity; tactile contacts with the robot right
forearm (yellow) and right hand (pink) are also depicted.
B. Safe robot-human hand-over task
In this experiment, the robot has to find the object lying on
Table 2, grasp it properly (Section II-E.2) to finally perform
hand-over toward the human partner. The cooperation task
is initialized with the following dialogue between the two
PARTN ER : Hi iCub, can you give me the OCTO PU S?
ICUB : I have the OCTO PU S on my table. I will give it
to you.
(ICUB then grasps the OC TOPUS from TABLE 2and
shows the OCT OP US to PART NER)
More specifically, iCub looks for the requested object
lying on Table 2 and calculates the best grasping pose using
the procedure described in Section II-E.2. If a suitable grasp
is found, iCub reaches for the object to perform a power
grasp. With the object in hand, the robot brings the object to
the hand-over location that best suits the estimated human
pose. As displayed in Fig. 7, the iCub movements toward
the desired pose are continuously adapted to accommodate
for any incoming visual obstacle, represented in this case
by the human left hand (t[38,40] s) as well as any
unexpected physical contacts (t[31.2,34.2] s and t
[36.5,39] s). Both visual and tactile events do constrain the
admissible range of joint velocities, generating the avoiding
behavior of the robot, as it is visible in Fig 7-Bottom in
terms of the distance between the end-effector and the hand-
over position (blue profile). This behavior guarantees a safe
physical interaction between the human and the iCub in pre-
and post-collision phases.
C. Quantitative assessment of the interactions
In this section we report on the success rates of the
two hand-over experiments carried out with a set of dif-
ferent objects. We ground our quantitative analysis to the
details level of 4sub-tasks per interaction that need to be
completed in sequence. In particular, we identify the sub-
tasks {Recognize, Localize, Receive, Drop}and the sub-
tasks {Detect, Plan, Grasp, Give}for the human-robot and
the robot-human hand-over sequences, respectively. For each
object, we repeat the whole experiment 10 times. Note
that the sub-task corresponding to the control of the robot
movements is omitted as the reactive controller guarantees
the safety of the action at all times.
Object Sub-tasks
Recognize Localize Receive Drop
Octopus 100% 90% 100% 100%
Duck 100% 100% 100% 90%
Bottle 100% 90% 100% 100%
Object Sub-tasks
Detect Plan Grasp Give
Ladybug 100% 80% 100% 100%
Box 100% 90% 90% 90%
Bottle 100% 90% 80% 100%
The high success rates recorded in the two hand-over
experiments and reported in Table I and Table II demonstrate
the effectiveness of our solution in scenarios where both the
physical and social properties of the interaction are relevant
during the human-robot collaboration.
We introduced a compact, fully integrated and scalable
architecture that fills in the gap between physical and social
HRI with the following key features: (i) a markerless 3D
context-aware visual perception system, (ii) a multi-modal
visuo-tactile reactive controller along with a fast and efficient
grasp planner to enable safe interaction, and (iii) a simple
database for storing symbolic knowledge. We showed the
complete system working in real-time controlling a robot
in the human-robot and robot-human object hand-over tasks
while guaranteeing safety for the human experimenter. More-
over, we believe that it is feasible to adapt our architecture
to different robots equipped with a similar set of sensors (i.e.
stereo-vision, tactile and/or force/torque sensing).
Future work will include integration with a state-of-the-art
cognitive architecture. In particular, safe behaviors generated
through our visuo-tactile component recall and advance the
mechanisms of the Somatic and Reactive layers of the
DAC-h3 architecure as described in [7]–[9]. Likewise, our
visual pipeline does tightly connect to the World and Action
layers available in DAC-h3 at least within the limits of
our simplified task planning. Thereby, in this context, we
can incorporate almost seamlessly further DAC-h3 func-
tional modules such as the Synthetic Sensory Memory, the
Perspective Taking and the Autobiographical Memory in
order to enrich the current repertoire of capabilities as for
example adding action recognition skills. If we compare with
the architecture of Lemaignan et al. [4], we do not share
functional modules as such, however, our overall structure
is similar to [4] at the symbolic layer. This similarity may
pave the way to a future integration of functionalities that
are missing in our design but readily accessible in [4], such
as the human-aware task planning.
In conclusion, we aim to further develop the present sys-
tem with the goal of implementing a general and principled
cognitive architecture, by taking advantages of the integra-
tion with other existing approaches. Paramount for effective
HRI is to improve action planners to tackle fast dynamic
environments [23], while taking into account ergonomics, as
discussed for example in [24].
Phuong D.H. Nguyen was supported by a Marie Curie
Early Stage Researcher Fellowship (H2020-MSCA-ITA, SE-
CURE 642667). M. H. was supported by the Czech Science
Foundation under Project GA17-15697Y. The authors would
like to thank Vadim Tikhanoff and Giulia Pasquale for
their valuable assistance with the integration of the object
recognition system.
[1] S. Gu et al., “Deep reinforcement learning for robotic
manipulation with asynchronous off-policy updates,
in IEEE Int. Conf. on Robotics and Automation, May
2017, pp. 3389–3396.
[2] A. De Luca et al., “Integrated control for pHRI:
Collision avoidance, detection, reaction and collabo-
ration,” in Biomedical Robotics and Biomechatronics
(BioRob), IEEE Int. Conf. on, 2012, pp. 288–295.
[3] S. Haddadin et al., “Towards the robotic co-worker,”
in Robotics Research, Springer Berlin Heidelberg,
2011, pp. 261–282.
[4] S. Lemaignan et al., “Artificial cognition for social hu-
man–robot interaction: An implementation,” Artificial
Intelligence, vol. 247, pp. 45–69, Jun. 2017.
[5] E. A. Sisbot et al., “A human-aware manipulation
planner,IEEE Trans. Robot., vol. 28, no. 5, pp. 1045–
1057, Oct. 2012.
[6] J. Mainprice et al., “Planning human-aware motions
using a sampling-based costmap planner,” in IEEE
Int. Conf. on Robotics and Automation, May 2011,
pp. 5012–5017.
[7] C. Moulin-Frier et al., “DAC-h3: A proactive robot
cognitive architecture to acquire and express knowl-
edge about the world and the self,IEEE Trans. Cogn.
Develop. Syst., no. 99, pp. 1–1, 2017.
[8] T. Fischer et al., “iCub-HRI: A software framework
for complex human–robot interaction scenarios on the
iCub humanoid robot,” Frontiers in Robotics and AI,
vol. 5, p. 22, 2018.
[9] C. Moulin-Frier et al., “Embodied artificial intelli-
gence through distributed adaptive control: An in-
tegrated framework,Joint IEEE Int. Conf. on De-
velopment and Learning and Epigenetic Robotics,
pp. 324–330, 2017.
[10] D. H. P. Nguyen et al., “Compact real-time avoidance
on a humanoid robot for human-robot interaction,” in
ACM/IEEE Int. Conf. on Human-Robot Interaction,
ACM, 2018, pp. 416–424.
[11] G. Metta et al., “The iCub humanoid robot: An open-
systems platform for research in cognitive develop-
ment,” Neural Networks, vol. 23, no. 8, pp. 1125–
1134, Oct. 2010.
[12] E. Insafutdinov et al., “Deepercut: A deeper, stronger,
and faster multi-person pose estimation model,” in
ECCV, Springer, 2016, pp. 34–50.
[13] G. Pasquale et al., “Teaching iCub to recognize
objects using deep convolutional neural networks,
in Machine Learning for Interactive Systems, 2015,
pp. 21–25.
[14] A. Krizhevsky et al., “Imagenet classification with
deep convolutional neural networks,” in Advances
in neural information processing systems, 2012,
pp. 1097–1105.
[15] P. Maiolino et al., “A flexible and robust large scale
capacitive tactile system for robots,IEEE Sensors J.,
vol. 13, no. 10, pp. 3910–3917, 2013.
[16] S. Haddadin et al., “Robot collisions: A survey on
detection, isolation, and identification,” IEEE Trans.
Robot., vol. 33, no. 6, pp. 1292–1312, 2017.
[17] S. Lallee et al., “How? Why? What? Where? When?
Who? Grounding ontology in the actions of a situated
social agent,” Robotics, vol. 4, pp. 169–193, 2015.
[18] A. Roncone et al., “Peripersonal space and margin of
safety around the body: Learning visuo-tactile associ-
ations in a humanoid robot with artificial skin,” PLOS
ONE, vol. 11, no. 10, e0163713, 2016.
[19] G. Vezzani et al., “A grasping approach based on
superquadric models,” IEEE Int. Conf. on Robotics
and Automation, pp. 1579–1586, 2017.
[20] A. Makhal et al., “Grasping unknown objects in clutter
by superquadric representation,” in 2nd IEEE Int.
Conf. on Robotic Computing, 2018, pp. 292–299.
[21] M. Ester et al., “A density-based algorithm for discov-
ering clusters in large spatial databases with noise,
in 2nd Int. Conf. on Knowledge Discovery and Data
Mining, AAAI Press, 1996, pp. 226–231.
[22] U. Pattacini et al., “An experimental evaluation of a
novel minimum-jerk cartesian controller for humanoid
robots,” IEEE/RSJ Int. Conf. on Intelligent Robots and
Systems, pp. 1668–1674, Oct. 2010.
[23] P. D. Nguyen et al., “A fast heuristic Cartesian space
motion planning algorithm for many-DoF robotic ma-
nipulators in dynamic environments,” in Humanoid
Robots, IEEE-RAS Int. Conf. on, 2016, pp. 884–891.
[24] W. Kim et al., “Anticipatory robot assistance for
the prevention of human static joint overloading in
human-robot collaboration,” IEEE Robotics and Au-
tomation Letters, vol. 3, no. 1, pp. 68–75, Jan. 2018.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Generating complex, human-like behavior in a humanoid robot like the iCub requires the integration of a wide range of open source components and a scalable cognitive architecture. Hence, we present the iCub-HRI library which provides convenience wrappers for components related to perception (object recognition, agent tracking, speech recognition, and touch detection), object manipulation (basic and complex motor actions), and social interaction (speech synthesis and joint attention) exposed as a C++ library with bindings for Java (allowing to use iCub-HRI within Matlab) and Python. In addition to previously integrated components, the library allows for simple extension to new components and rapid prototyping by adapting to changes in interfaces between components. We also provide a set of modules which make use of the library, such as a high-level knowledge acquisition module and an action recognition module. The proposed architecture has been successfully employed for a complex human-robot interaction scenario involving the acquisition of language capabilities, execution of goal-oriented behavior and expression of a verbal narrative of the robot's experience in the world. Accompanying this paper is a tutorial which allows a subset of this interaction to be reproduced. The architecture is aimed at researchers familiarizing themselves with the iCub ecosystem, as well as expert users, and we expect the library to be widely used in the iCub community.
Full-text available
With robots leaving factories and entering less controlled domains, possibly sharing the space with humans, safety is paramount and multimodal awareness of the body surface and the surrounding environment is fundamental. Taking inspiration from peripersonal space representations in humans, we present a framework on a humanoid robot that dynamically maintains such a protective safety zone, composed of the following main components: (i) a human 2D keypoints estimation pipeline employing a deep learning based algorithm, extended here into 3D using disparity; (ii) a distributed peripersonal space representation around the robot's body parts; (iii) a reaching controller that incorporates all obstacles entering the robot's safety zone on the fly into the task. Pilot experiments demonstrate that an effective safety margin between the robot's and the human's body parts is kept. The proposed solution is flexible and versatile since the safety zone around individual robot and human body parts can be selectively modulated---here we demonstrate stronger avoidance of the human head compared to rest of the body. Our system works in real time and is self-contained, with no external sensory equipment and use of onboard cameras only.
Full-text available
This paper proposes a novel human-robot collaboration (HRC) control approach to alert and reduce the static joint torque overloading of a human partner while executing shared tasks with a robot. Using a pre-identified statically equivalent serial chain (SESC) model, variations of the centre-of-pressure and ground reaction force are calculated, and the overloading joint torques are evaluated in real-time to initially alert the human about consequent injuries. An on-line optimisation technique is implemented to adjust the robot trajectories facilitating the human to achieve more ergonomic body poses throughout the HRC task. The optimised human configurations are calculated by taking into account the human stability, the robot and the human workspaces, and the task constraints, and illustrated to the human in real-time. The experimental evaluation of the proposed technique is achieved in a human-robot load sharing task as a representative example of co-assembly or transportation scenarios in industrial settings.
Full-text available
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in a human-robot interaction experiment with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario.
Full-text available
In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.
Full-text available
This paper investigates a biologically motivated model of peripersonal space through its implementation on a humanoid robot. Guided by the present understanding of the neurophysiology of the fronto-parietal system, we developed a computational model inspired by the receptive fields of polymodal neurons identified, for example, in brain areas F4 and VIP. The experiments on the iCub humanoid robot show that the peripersonal space representation i) can be learned efficiently and in real-time via a simple interaction with the robot, ii) can lead to the generation of behaviors like avoidance and reaching, and iii) can contribute to the understanding the biological principle of motor equivalence. More specifically, with respect to i) the present model contributes to hypothesizing a learning mechanisms for peripersonal space. In relation to point ii) we show how a relatively simple controller can exploit the learned receptive fields to generate either avoidance or reaching of an incoming stimulus and for iii) we show how the robot can select arbitrary body parts as the controlled end-point of an avoidance or reaching movement.
Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human–robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products.
Conference Paper
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry