Mechanical and Mechatronics Engineering

Human motor control has always acted as an inspiration in both robotic manipulator design and control. In this paper, a modeling approach of anthropomorphism in human arm movements during every-day life tasks is proposed. The approach is not limited to describing static postures of the human arm but is able to model posture transitions, in other words, dynamic arm movements. The method is based on a novel structure of a Dynamic Bayesian Network (DBN) that is constructed using motion capture data. The structure and parameters of the model are learnt from the motion capture data used for training. Once trained, the proposed model can generate new anthropomorphic arm motions. These motions
are then used for controlling an anthropomorphic robot arm, while a measure of anthropomorphism is defined and utilized for assessing resulted motion profiles.

Coupling the human upper limbs with robotic devices is gaining increasing attention in the last decade, due to the emerging applications in orthotics, prosthetics and rehabilitation devices. In the cases of every-day life tasks, force exertion and generally interaction with the environment is absolutely critical. Therefore, the decoding of the user’s force exertion intention is important for the robust control of orthotic robots (e.g. arm exoskeletons). Nevertheless, humans tend to perform force tasks in a robust manner, making use of the inherent redundancy of the arm in kinematic as well as in musculoskeletal level. The analysis of these strategies can benefit the control of coupled human-robot systems in force exertion tasks. In this paper, the human arm manipulability is analyzed and its effect on the recruitment of the musculo-skeletal system is explored. It was found that the recruitment and activation of muscles is strongly affected by arm manipulability. Based on this finding, a decoding method was built in order to estimate force exerted in the three-dimensional (3D) task space from surface ElectroMyoGraphic (EMG) signals, recorded from muscles of the arm. The method is using the manipulability information for the given force task. Experimental results were verified in various arm configurations with two subjects.

Reaching and grasping of objects in an everyday-
life environment seems so simple for humans, though so
complicated from an engineering point of view. Humans use
a variety of strategies for reaching and grasping anything
from the simplest to the most complicated objects,
achieving high dexterity and efficiency. This seemingly
simple process of reach-to-grasp relies on the complex
coordination of the musculoskeletal system of the upper
limbs. In this paper, we study the muscular co-activation
patterns during a variety of reach-to-grasp motions, and we
introduce a learning scheme that can discriminate between
different strategies. This scheme can then classify
reach-to-grasp strategies based on the muscular
co-activations. We consider the arm and hand
as a whole system, therefore we use surface
ElectroMyoGraphic (sEMG) recordings from muscles of both the upper arm and the forearm. The proposed scheme is tested in extensive paradigms proving its efficiency, while it can be used as a switching mechanism for EMG-based decoders of arm-hand
motion, towards the control of advanced upper limb prosthesis.

A learning scheme based on Random Forests is used to decode the EMG activity of 16 muscles of the human arm and hand to a continuous representation of arm-hand kinematics in reach-to-grasp movements in 3D space. Classification methods are used to discriminate between significantly different reach to grasp strategies, formulating a switching mechanism that may trigger the use of position and object-specific decoding models (task-specificity). These task-specific models can achieve
better estimation results than the general models for the kinematics of different reach-to-grasp movements. The proposed
methodology aims to overcome well known difficulties of the
EMG-based control systems such as, the nonlinear relationship
between the myoelectric activity and the arm-hand motion. The
efficacy of the proposed methodology is assessed through a
strict validation procedure, based on everyday life reach-tograsp
scenarios and data not previously seen during training.
Finally, for demonstration purposes, the authors teleoperate
an arm-hand model in the OpenRave simulation environment
using the estimated from the EMG signals human motion.

In this paper we propose a generic methodology for human to robot motion mapping for the case of a robotic arm hand system, allowing anthropomorphism. For doing so we discriminate between Functional Anthropomorphism and Perceptional Anthropomorphism, focusing on the first to achieve anthropomorphic solutions of the inverse kinematics for a redundant robot arm. Regarding hand motion mapping, a “wrist” (end-effector) offset to compensate for differences between human and robot hand dimensions is applied and the fingertips mapping methodology is used. Two different mapping scenarios are also examined: mapping for teleoperation and mapping for
autonomous operation. The proposed methodology can be
applied to a variety of human robot interaction applications, that require a special focus on anthropomorphism.

"In this paper a comparative analysis between
the human and three robotic hands is conducted. A series
of metrics are introduced to quantify anthropomorphism
and assess robot’s ability to mimic the human hand.
In order to quantify anthropomorphism we choose to
compare human and robot hands in two different levels:
comparing finger phalanges workspaces and comparing
workspaces of the fingers base frames. The final score
of anthropomorphism uses a set of weighting factors
that can be adjusted according to the specifications of
each study, providing always a normalized score between
0 (non-anthropomorphic) and 1 (human-identical). The
proposed methodology can be used in order to grade
the human-likeness of existing and new robotic hands,
as well as to provide specifications for the design of the
next generation of anthropomorphic hands. Those hands
can be used for human robot interaction applications,
humanoids or even prostheses."

"A learning scheme based on Random Forests is used
to discriminate between different reach to grasp movements in
3D space based on the myoelectric activity of human muscles
of the upper arm and the forearm. Task specificity for motion
decoding is introduced in two different levels: subspace to move
towards and object to be grasped. The discrimination between the
different reach to grasp strategies is accomplished with machine
learning techniques for classification. The classification decision is
then used in order to trigger an EMG-based task-specific motion
decoding model. Task specific models manage to outperform
“general” models providing better estimation accuracy. Thus the
proposed scheme takes advantage of a framework incorporating
both a classifier and a regressor that cooperate advantageously
in order to split the task space. The proposed learning scheme
can be easily used to a series of EMG-based interfaces that must
operate in real time, providing data driven capabilities for multiclass problems, that occur in everyday life complex environments."

"In this paper a series of teleoperation and
manipulation tasks are performed with the five fingered
robot hand DLR/HIT II. Two different everyday life
objects are used for the manipulation tasks; a small
ball and a rectangular object. The joint-to-joint mapping
methodology is used to map human to robot hand motion, taking into account existing kinematic constraints
such as synergistic characteristics and joint couplings.
The Cyberglove II motion capture dataglove is used to
measure human hand kinematics. A robot hand specific
fast calibration procedure is used to map raw dataglove
sensor values to human joint angles and subsequently
through the mapping procedure, to DLR/HIT II joint
angles. A novel low cost force feedback device is developed, in order for the user to be able to detect
contact and perceive the forces exerted by the robot
fingertips, during manipulation tasks. The design of the
force feedback device is based on RGB LEDs that provide
visual feedback and vibration motors that provide vibrotactile feedback."

""A learning scheme based on Random Forests
is used to discriminate the task to be executed using
only myoelectric activity from the upper limb. Three
different task features can be discriminated: subspace
to move towards, object to be grasped and task to be
executed (with the object). The discrimination between
the different reach to grasp movements is accomplished
with a random forests classifier, which is able to perform
efficient features selection, helping us to reduce the number of EMG channels required for task discrimination.
The proposed scheme can take advantage of both a classifier and a regressor that cooperate advantageously to
split the task space, providing better estimation accuracy
with task-specific EMG-based motion decoding models,
as reported in [1] and [2]. The whole learning scheme can
be used by a series of EMG-based interfaces, that can
be found in rehabilitation cases and neural prostheses.""

"In this paper teleoperation and telemanipulation
with a robot arm (Mitsubishi PA-10) and a robot hand
(DLR/HIT 2) is performed, using a human to robot motion
mapping scheme that guarantees anthropomorphism. Two position trackers are used to capture position and orientation of
human end-effector (wrist) and human elbow in 3D space and a
dataglove to capture human hand kinematics. Then the inverse
kinematics (IK) of the Mitsubishi PA-10 7-DoF robot arm are
solved in an analytical manner, in order for the human’s and
the robot artifact’s end-effectors to achieve same position and
orientation in 3D space (functional constraint). Redundancy is
handled in the solution space of the robot arm’s IK, selecting
the most anthropomorphic solution computed, with a criterion
of “Functional Anthropomorphism”. Human hand motion is
transformed to robot hand motion using the joint-to-joint
mapping methodology. Finally in order for the user to be able
to detect contact and “perceive” the forces exerted by the robot
hand, a low-cost force feedback device, that provides a mixture
of sensory information (visual and vibrotactile), was developed"

In this paper we provide directions, methods and
metrics that can be used to synthesize a complete framework for
mapping human to robot motion with Functional Anthropomorphism.
Such a mapping can be used in order to perform skill
transfer from humans to robot arm hand systems with arbitrary
kinematics. The mapping schemes proposed, first guarantees the
execution of specific functionalities by the robotic artifact in
3D space (task space functional constraints), and then optimizes
anthropomorphism of robot motion. Human-likeness of robot motion is achieved through minimization of structural dissimilarity
between human and robot arm hand system configurations. The
proposed methodology is suitable for a wide range of applications
ranging from learn by demonstration for autonomous grasp
planning, to real time teleoperation and telemanipulation with
robot arm hand systems in remote or dangerous environments.

The majority of the works on grasping consider
both object as well as robot hand parameters to be accurately
known and do not take into account the constraints imposed
by the robot hand. In this paper, a complete methodology is
proposed that handles the grasping problem under a wide range
of uncertainties. Initially, we search for an acceptable posture
that provides robustness against positioning inaccuracies and
maximizes the ability of the robot hand to exert forces on the
object. Subsequently, in order to secure the grasp stability, we
also deal with the determination of sufficient contact forces.
Finally, an appropriate tactile sensor setup, mounted on the
robot hand, allow us to reduce the magnitude of uncertainty
regarding the grasping parameters. The efficiency of our approach
is validated through extensive experimental paradigms
using a 15 DoF DLR/HIT II robotic hand attached at the end
effector of a 7 DoF Mitsubishi PA10 robotic manipulator.

In this paper, we propose an optimization scheme
for deriving task-specific force closure grasps for underactuated
robot hands. Motivated by recent neuroscientific studies on
the human grasping behavior, a novel grasp strategy is built
upon past analysis regarding the task-specificity of human
grasps, that also complies with the recent soft synergy model
of underactuated hands. Our scheme determines an efficient
force closure grasp (i.e., configuration and contact points/forces)
with a posture compatible with the desired task, taking into
consideration the mechanical and geometric limitations imposed
by the design of the hand and the object shape. The efficiency
of the algorithm is verified through simulated paradigms on a
hypothetical underactuated hand with the kinematic model of
the DLR/HIT II five fingered robot hand.

In this paper we provide directions, methods and metrics that can be used to synthesize a complete framework for mapping human to robot motion with Functional Anthropomorphism. Such a mapping can be used in order to perform skill transfer from humans to robot arm hand systems with arbitrary kinematics. The mapping schemes proposed, first guarantees the execution of specific functionalities by the robotic artifact in 3D space (task space functional constraints), and then optimizes anthropomorphism of robot motion. Human-likeness of robot motion is achieved through minimization of structural dissimilarity between human and robot arm hand system configurations. The proposed methodology is suitable for a wide range of applications ranging from learn by demonstration for autonomous grasp planning, to real time teleoperation and telemanipulation with robot arm hand systems in remote or dangerous environments.

In this paper, we propose a complete methodology
for deriving task-specific force closure grasps for multifingered
robot hands under a wide range of uncertainties. Given a
finite set of external disturbances representing the task to be
executed, the concept of Q distance is introduced in a novel way
to determine an efficient grasp with a task compatible hand
posture (i.e., configuration and contact points). Our approach
takes, also, into consideration the mechanical and geometric
limitations imposed by the robotic hand design and the object
to be grasped. In addition, incorporating our recent results on
grasping [1], the ability of the robot hand to exert the required
contact forces is maximized and robustness against positioning
inaccuracies and object uncertainties is established. Finally, the
efficiency of our approach is verified through an experimental
study on the 15 DoF DLR/HIT II robotic hand attached at the
end effector of the 7 DoF Mitsubishi PA10 robotic manipulator.

In this paper we present a series of design directions
for the development of affordable, compliant, modular,
underactuated robot fingers, that can be used as prostheses by
amputees that suffer from various partial hand amputations
(index to pinky fingers are considered). Our design is based
on parametric models that have been derived from hand
anthropometry studies. Various interfaces have been considered
in order to control the prosthesis, depending on the type and
level of amputation. More precisely: 1) An Electromyography
(EMG) based interface is used to control the robot fingers
employing the EMG signals of the human forearm muscles 2)
A flex sensors based interface is used to record the motion of
the intact finger/fingers and predict the motion of the prosthesis
implementing a synergistic behavior in an efficient manner, 3)
A body powered interface is used for those that want to achieve
even lower cost, with robust intuitive operation. Following the
proposed design directions, an amputee will be able to replicate
our fingers and develop personalized, affordable, light-weight
but yet efficient prostheses.

In this paper we present a series of design directions
for the development of affordable, modular, light-weight,
intrinsically-compliant, underactuated robot hands, that can be
easily reproduced using off-the-shelf materials. The proposed
robot hands, efficiently grasp a series of everyday life objects
and are considered to be general purpose, as they can be
used for various applications. The efficiency of the proposed
robot hands has been experimentally validated through a series
of experimental paradigms, involving: grasping of multiple
everyday life objects with different geometries, myoelectric
(EMG) control of the robot hands in grasping tasks, preliminary
results on a grasping capable quadrotor and autonomous grasp
planning under object position and shape uncertainties.

In this paper, we propose a robust model free
control scheme of minimal complexity (it is a static scheme
involving very few and simple calculations to output the
control signal) for robotic manipulators, capable of achieving
prescribed transient and steady state performance. No information
regarding the robot dynamic model is employed in
the design procedure. Moreover, the tracking performance of
the developed scheme (i.e., convergence rate and steady state
error) is a priori and explicitly imposed by a designer-specified
performance function, and is fully decoupled by both the control
gains selection and the robot dynamic model. In that respect,
the selection of the control gains is only confined to adopting
those values that lead to reasonable control effort. Finally, two
experimental studies in the joint and the Cartesian workspace
clarify the design procedure and verify its performance and
robustness against external disturbances.