Touch-Triggered Withdrawal Reflexes for Safer Robots
Torbjørn S. Dahl, Erick A. R. Swere and Andrew Palmer
torbjorn.dahl|Erick.swere|[email protected]
Cognitive Robotics Research Centre
University of Wales, Newport
Abstract
Covering robots with a skin-like sensor enables a range of skills that will
improve human-robot interaction (HRI) both in terms of safety and
communication. In this chapter we review the current state of robot safety
and current understanding of human reflexes, in particular the human
nociceptive withdrawal reflex (NWR), i.e., the reflex that causes withdrawal
from noxious, including painful, stimuli. We present and analyse the results
of an experiment capturing human NWR motion data and also present a
model developed to use the results to implement a similar reflex on the Nao
humanoid robot. Finally we present the idea of protective reflexes as robot
middleware where we define middleware as a set of generic skills
independent of application area.
1. Withdrawal Reflexes and Robot Safety
The he work presented here is part of a wider research context.
The
ROBOSKIN project is funded by the European Commission’s Framework
Programme 7 (FP7) and its activities are centred on new sensor technologies
that can cover large areas of a robot’s body with a touch sensitive material,
i.e., robot skin. The activities of the ROBOSKIN project span a range of
levels from sensor hardware to application specific skills. The project
studies hardware for sensing not only the force and location of touch, but
also the direction of the force and other non-touch phenomena such as heat
and proximity.
The ROBOSKIN project is also developing a level of
problem domain and robot platform independent middleware such as
protective reflexes and touch recognition and production skills. Finally, the
ROBOSKIN project studies the application of robot skin technology in
several domains, including programming by demonstration (Calinon et al.,
2007) and HRI for therapeutic play (Dautenhahn et al., 2009). This chapter
focuses in particular on the protective reflexes activities of the ROBOSKIN
project.
Touch-triggered withdrawal reflexes are a technology that can contribute to
safer human, robot interaction (HRI). Safety is an area that already has a
long track record in HRI research. In this Section, we review existing work
on robot safety and place touch-triggered withdrawal reflexes within this
larger context.
1.1 The Principle of Separation
Traditionally, in industrial applications, safe HRI is achieved by isolating
the robot from the human (Corke, 1999) (ANSI, 1999) (Gaskill and Went,
1996), in effect prohibiting interaction. As robots move out of isolated
industrial environments and into interactive environments, this approach is
no longer tenable (Corke, 1999). Despite the fact that several manufacturers
of service robots have released their technology onto the commercial
market, we do not yet have worldwide guidelines or regulations concerning
the safety aspects of such robots (Kabe et al., 2009).
Recently, an
industrial assistant robot system designed for close human-robot-interaction
was launching into the market (Oberer et al., 2006). The new international
standard for robot systems ISO 10218-1 (ISO, 2006) covers this situation
and provides regulations for HRI. In this case, the humans in the active
workspace of a robot can be protected by the internal safety control, while
the area surrounding both the robot cells and the operator should be guarded
against entry of unauthorized personnel.
Industrial assistant robots are
typically both heavy and fast requiring comprehensive safety control
systems usually involving high costs. Service robots are typically smaller
than industrial robots and thus have lower kinetic energy. They also have
higher levels of mobility and autonomy than assistant robots in industry.
They are not restricted to static mass production scenarios, but are employed
in a wide range of roles in industrial and home applications. The successful
commercialization of service robots requires a low pricing strategy that
cannot support the comprehensive safety control systems employed by
industry.
1.2 Sensitisation
Consider the example of a compliant robot arm colliding with an object. In
this case, the compliance provides some measure of safety within a range,
but when the range of compliance is exhausted, the force of the impact will
again increase and along with the probability of damage or injury.
Compliance provides a window of time within which it is necessary to sense
the impact and stop or even reverse the underlying motion in order to avoid
the potential damage. Haptic sensors, e.g., motor stress or strain gauges are
commonly applied for this purpose. An early warning system would only
be effective, however, if coupled with correspondingly quick and beneficial
reflex motions. This is achieved in humans and animals through dedicated
neural pathways called reflex arcs, and evolved reflex motions. In Section 2
we review what is known about these biological mechanisms. In Section 3
we discuss the capture of human reflex motions and present an experiment
designed for this purpose. In Section 4 we present initial results from these
experiments.
In Section 5 we discuss technologies and previous work
related to protective robot reflexes and in Section 6 we discuss the potential
roles of generic protective reflexes in a wider robot control architecture and
also mechanisms for the integration of generic reflexes and domain specific
robot behaviours.
2. Human Nociceptive Withdrawal Reflexes
A reflex is an almost instantaneous movement that the body performs in
response to a stimulus without having to think about it (Zehr and Stein,
1999). For example, a human being does not decide to extend the leg when
the tendon below the knee is hit. Instead it happens automatically. Healthy
human beings posses many types of reflexes and many of these reflexes are
present from birth. Reflexes allow an animal to quickly adjust its behaviour
to sudden environmental changes so as to protect itself. Analyses of the
nervous system show that reflexes are essentially direct connections
between the sensory neurons, recognising the sensory data, and the motor
neurons responsible for the response.
2.1 The Anatomy of a Reflex
The simplest form of animal behaviour is the reflexive behaviour in which
some fast, stereotyped responses are triggered by a particular class of
environmental stimuli (Beer et al., 1990) (Ghez, 1990). A reflexive response
occurs, for example, when one steps on a sharp object or when one touches
a hot surface. Reflexes allow an animal to quickly adjust its behaviour to
sudden environmental changes so as to protect itself. Reflex actions are
realised by the reflex arc (Stűttgen, 1999). The reflex arc is the simplest and
most primitive nerve pathway in the human body (Kandel et al., 1995). A
single connection can contain as little as a single synapse. All reflex arcs
have a minimum of five functional elements:
1. The receptor; this host the dendrite of the sensory neuron and reacts to a
stimulus.
2. The sensory neuron; this conducts the afferent impulses to the central
nervous system (CNS).
3. The integration centre; this consists of one or more synapses in the CNS.
4. The motor neuron; this conducts the efferent impulses from the
integration centre to an effector.
5. The effector; this is muscle fibres or glands, respond to the efferent
impulses by contraction or secretion a product, respectively.
Reflexes protect the body from things that can harm it, for example, if the
human hand is about to touch a hot object, as soon as the body sense the
heat from that hot object, a reflex will cause the human to immediately
remove the hand before the warning message of the danger from that hot
object gets to the human brain. Reflexes exist in different complexity stages,
native, learned or trained. The automatic, stereotyped reaction to
disturbances is a basis to survive in a mainly consistent environment. A
biological reflex action can be divided into three fundamental phases:
1. The reflex is triggered by an adequate stimulus that is recognized by a
receptor cell.
2. The generated neural activation is passed from the sensory nerves to the
respective processing centre in the spinal cord.
3. In the spinal cord the signal is transmitted through excitatory or
inhibitory synapses to the motor nerves, which are connected to the
descending nerves of higher control structures.
2.2 Types of Reflexes
Reflexes are classified as either autonomic or somatic. Autonomic reflexes
are not subject to conscious control. These reflexes activate smooth, i.e.,
involuntary, muscles, including the heart. These reflexes also activate the
glands of the body and regulate body functions such as digestion and blood
pressure. Somatic reflexes include all reflexes that stimulate skeletal, i.e.,
voluntary, muscles.
These reflexes can be modulated by conscious
processes.
Autonomic Reflexes Autonomic or visceral reflexes, refer to reflexes such
as those that effect gland secretion or cause the smooth muscle of the
cardiac system to contract. The main purpose of the visceral reflexes is to
ensure that the involuntary process of the body are in full operating
condition and can be tailored to react to a given situation. Heart rate,
respiratory rate, blood flow, and digestion are examples of the types of
visceral reflexes that require constant monitoring by the internal body
systems. Reflexes like coughing, swallowing, sneezing or vomiting are also
considered to be autonomic. However, these reflexes require at least some
participation of the skeletal muscles. The autonomic reflexes relate mainly
to parts of the human physiology that are typically not replicated on
humanoid robots such as the heart and the lungs. In general, they relate to
basic operation, keeping energy flowing through the body. As such these
reflexes are not currently relevant for implementation on robots, but one can
imagine that similar reflexes could be implemented on robots to, e.g., reduce
unnecessary activity when battery levels are low or to facilitate recharging.
The Somatic Reflexes: The somatic, or voluntary, reflexes enable humans
to react to environmental changes. They involve 31 pairs of spinal nerves,
i.e., nerves running through the spinal cord, and 12 pairs of cranial nerves,
i.e. nerves emerging directly from the brain without going through the spine.
This system controls movements of skeletal muscles. Somatic reflexes
include the flexor, stretch, crossed extensor, superficial cord, corneal, and
gag reflexes. The first four reflexes are controlled by spinal nerves while
the corneal, i.e. related to the transparent part of the eye, and the gag
reflexes are controlled by cranial nerves.
Spinal Reflexes:
Reflexes mediated by spinal nerves include stretch,
flexor, crossed extensor and superficial cord reflexes: The flexor, or
withdrawal, reflex is reviewed in detail in Section 2.3. The remaining
reflexes are reviewed briefly here.
Stretch reflexes are important postural reflexes that act to maintain posture,
balance, and locomotion. Stretch reflexes are produced by tapping a tendon,
which stretches the attached muscle. This stimulates muscle spindles, i.e.
specialized sensory receptors in the muscle, and causes reflex contraction of
the stretched muscle also resisting further stretching. Stretch reflexes are
segregated into different types including the patellar reflex, the Achilles
reflex and the biceps reflex. The patellar or knee-jerk reflex occurs when
the tendon just below the kneecap is struck. The reflex is processed at spinal
cord and does not involve the brain.
This reflex is known to be a
monosynaptic reflex, i.e., only one synapse needs to be crossed to complete
the circuit which triggers the reflex. The Achilles reflex or ankle-jerk reflex
is produced when the Achilles tendon at the back of the ankle is firmly
tapped. This reflex is also a monosynaptic. The biceps-jerk reflex is a
polysynaptic reflex, i.e., its arc contains more than one synapse. The biceps
reflex causes sudden contraction of a biceps and brings the forearm up
sharply when the tendon of the biceps muscle is firmly tapped. The origin
of the stretch reflexes is balance and posture maintenance. As such they
could provide a way of improving balance and posture on humanoid robots
and their potential benefits should be explored further.
The crossed extensor reflex is activated when numerous spinal cord
segments are activated via impulses coming from the receptor organs, and
the release of multiple motor neurons is required, this is known as an intersegmental reflex arc. This means that multiple effector organs are initiated
and multiple receptor organs sent signals. The required sensory impulses
from a receptor organ cross the spinal cord in order to stimulate an effector
organ on the opposite side of the body. This is known as a contra-lateral
action. This reflex, like the stretch reflexes, help maintain balance and
posture.
The superficial cord reflexes, or abdominal and plantar reflexes, are initiated
by the stimulation of receptors in the skin and mucous membranes. The
superficial cord reflexes depend both on brain participation and on the cordlevel reflex arc. The abdominal reflex contracts the muscles in the abdomen
on cutaneous stimulation, i.e., stimulation of the skin. The purpose of this
reflex is to protect the abdomen.
The plantar reflex is elicited by
stimulating the cutaneous receptors in the sole of the foot. In adults,
stimulation of these receptors causes the toes to flex and move closer
together. This is effectively a withdrawal reflex working to protect the foot,
but unlike the nociceptive withdrawal reflexes described below, the plantar
reflex is activated without noxious, e.g., painful, stimuli. The superficial
chord reflexes in general protect particularly vulnerable areas of the human
body.
This principle of lowering the reflex activation threshold for
particularly vulnerable areas could potentially be transferred to robots. The
principle has already been employed in developmental robotics, where the
palmar reflex, i.e., the reflex that closes a baby’s hand when something
touches its palm, has already been used as inspiration for the
implementation of an anthropomorphic finger with multi-point tactile
sensation (Banks, 2001).
Cranial reflexes: Two reflexes mediated by cranial nerves are the corneal
reflex and the gag reflex. The corneal reflex, or blink reflex, is mediated
through the trigeminal nerve. This reflex is activated by touching the cornea
and causes the eye to move. A similar reflex in robots could be beneficial to
protect cameras, however it would require sensitisation of the camera area
and a related protective mechanism, i.e., a robotic eyelid. The gag reflex is
activated by touching the back of the throat. As previously noted, the
absence of air and digestive channels in robots makes this reflex irrelevant
for robots at the moment.
2.3 The Nociceptive Withdrawal Reflex
A withdrawal reflex, also known as a flexor reflex, is a polysynaptic reflex
arc (Sherrington, 1910). Sensory and motor neurons are part of this reflex,
as are association neurons. Pain is the most obvious stimuli that initiates the
flexor reflex, as painful stimuli sets the whole reflex into action. The
receptor responds and sends the information via sensory neurons. It reaches
the spinal cord, and there, the association neurons take over the situation.
Motor neurons receive the information and immediately the muscles
involved contract to produce a withdrawing snap action. Additionally and
simultaneously, the antagonistic muscles relax, otherwise they may inhibit
the body's ability to snap the muscles into withdraw. It is not uncommon for
the body to initiate additional reflexes along with the flexor reflex. The
nociceptive flexion reflex (NFR) was first described in detail at the
beginning of the 20th Century by Sherrington (Sherrington, 1910) as a
general excitatory response of flexor muscles and inhibitory response of
extensor muscles to stimulation of the cutaneous afferents and deep nerves.
Nociception is the sense of noxious, potentially tissue damaging, stimuli
including pain. Sherrington also define reflex receptive field (RRF) as the
assemblage of receptive points that might evoke a particular reflex
movement when suitably stimulated. A receptive field is the region of the
sensor where an adequate stimulus elicits a response (Churchland and
Sejnowski, 1996). The first study of human NFR was carried out in 1960s
and found indications of a more complex organization of the human
withdrawal reflex (Hagbarth, 1960) (Kugelberg et al., 1960). The study,
carried out by Grimby (1963), observed ankle dorsiflexion as a result of
stimulation of the medial, distal sole of the foot, ankle extension after
stimulation of the plantar surface of the heel, inversion following
stimulation of the medial side of the foot sole, and eversion after stimulation
of the lateral side of the foot sole. These findings reveal a complex response
tending to withdraw the stimulated area from the noxious source.
In
humans, a reflex withdrawal reaction can be elicited by transcutaneous
electrical
stimulation
of
a
sensory
peripheral
nerve
and
the
electromyographic response recorded from the flexor and extensor muscles.
This nociceptive withdrawal reflex (NWR) is a polysynaptic spinal
nociceptive reflex and represents the mechanism for withdrawing an
extremity from injury (Sherrington, 1910).
The NWR is reproducible,
stimulus-dependent and is closely correlated with the intensity of subjective
pain perception (Willer, 1977) (Willer, 1984) (Chan and Dallaire, 1989).
Therefore the NWR and its modulation have been widely used in
experimental (deBroucker et al., 1989) (Arendt-Nielsen et al., 2000)
(Andersen, 2007) and pharmacologic studies (Petersen-Felix et al., 1998)
(Piguet et al., 1998) (Escher et al., 2007) as a non-invasive
neurophysiologic tool to objectively assess spinal nociceptive processing.
The repetition of reflex stimulations applied at the same spot of a subject
can result in a gradual decrease in the NWR amplitude. This spinal
phenomenon is termed habituation. Habituation is intensity and frequency
dependent, occurring more frequently at low intensities and at high
stimulation frequencies (Shahani & Young, 1971) (Dimitrijević et al.,
1972). Different studies have found that the background activities can also
affect the response of withdrawal reflexes. In stationary conditions, a
modulation of the nociceptive reflex in tibialis anterior, a muscle in the
lower leg, depends on which limb is supporting the body while standing
(Rossi & Decchi, 1994). Reflexes were seen to be increasing when standing
on the contralateral leg, and were also seen decreasing when supporting on
the ipsilateral leg compared with standing on both legs. The study of
comparing tibialis anterior responses during running and walking was
carried out by Duysens et al. (1991) and found that the cutaneously evoked
reflex sizes, using non-nociceptive intensities, are generally large during
running when the background activity is also largest.
Another study
(Duysens et al., 1993) also observed larger reflexes were during running
than during standing. Finally, research on infant humans and monkeys has
shown that the ability to inhibit reflexes increases with age, allowing
increased cortical control as a person matures (Sherrington, 1910).
2.4 Reflex Receptive Fields
Research on the NWR has shown that a reflex has a related reflex receptive
field (RRF) and that each RRF controls only a single or small group of
muscles (Andersen, 2007). The effectiveness of an RRF decays as the
stimuli moves from the field’s centre towards its edge. Further research on
RRFs has shown that such fields are also clearly observable in humans
(Andersen et al., 1999). Two RRFs on the sole of the human foot are
illustrated in Figure 1.
Figure 1: The reflex receptive fields (RRFs) on the sole of the foot for the
Tibialis anterior (TA) and Peroneus longus (PL) muscles in the lower leg
The two RRFs and their related muscles are illustrated in Figure 3 control
two muscles in the leg, the tibialis anterior and the peroneus longus. The
locations of these two muscles in the human leg are illustrated in Figure 1.
3. Capturing the Human Nociceptive Withdrawal Reflex
Modelling human reflexes is a two-part process. First the reflexes must be
produced, i.e., provoked. Second, they must be captured. In the work
presented here, the first point produced unexpected difficulties, as described
below.
3.1 Provoking the NWR
A range of methods have been used to elicit withdrawal reflexes in humans.
These include the application of pin-pricks or heat (Willer, 1984), reaching
with a transparent barrier (Sherrington, 1910), direct electronic stimulation
of the sensory nerve (Arendt-Nielsen et al., 1990) and electronic stimulation
of the sole of the foot (Willer, 1985). To capture the reflexes, a goniometer
or angle-meter, is commonly used (Andersen et al., 2004), measuring the
reflex for a single joint. Our work will use the Gypsy-6 mechanical fullbody motion capture suit from Animazoo Ltd. to capture protective reflex
responses. The Gypsy-6 suit is shown in Figure 5.
Figure 2: The Gypsy-6 mechanial motion capture from Animazoo Ltd. used
in our withdrawal reflex experiments
From the literature there were strong indications that the NWR habituates
rapidly.
We therefore arrange a schedule where our volunteers were
subjected to only three stimuli and where the order of the stimuli would
change.
This setup allowed us to test statistically for any habituation
effects.
3.2 The Withdrawal Reflex Capture Experiments
In order to get a good quality model of a withdrawal reflex and avoid any
possible anomalies in a single person, base it on reflexes from a range of
subjects in. To ensure consistent reflexes we formalised an experimental
setup where five different points on the subjects arm were stimulated during
different but similar reaching actions. The formalisation included the initial
position of the subject’s arm, the position a target to be reached for relative
to the subject, and an approach angle and stimulation point for a stimulation
tool consisting of a rod with a tip capable of delivering a mild (<200V)
shock to the subject. The procedure also involves dampening the skin on
the subject’s arm with a sponge. During early trials, a significant number of
subjects did not produce any significant responses to the stimuli presented.
These subjects reported that the stimuli were so weak that they were
difficult to detect. We discovered that different skin moisture levels had a
critical effect on the reflexes motions produced.
A procedure for each trial was also formalised which made it impossible for
the subject to predict the contact point by blindfolding the subjects and
covering their ears.
To enable the subjects to reach the target while
blindfolded, they were first allowed three attempts at reaching without the
blindfold. The blindfold was then introduced and the subjects were asked to
attempt to reach the target between two or three times, decided at random,
before the stimulus was introduced.
4
Results
In order to describe the reflex motion, it was necessary to isolate it from the
interrupted reaching motion. These isolated reflex motions could then be
aggregated and compared to give provide a good understanding of typical
reflex motions.
4.1 Identifying the Reflex Motion
Shoulder Rotation
40
Z_roll
X_pitch
Y_yaw
-40
-60
19.99
19.57
19.16
18.74
18.32
17.91
17.49
17.07
-20
16.66
0
16.24
Degree
20
Time (s)
Figure 3 presents a plot of the rotations of each of the three degrees of
freedom (DOFs) in a subject’s shoulder joint over the time of the final
reaching movement when stimulation took place.
Shoulder Rotation
40
Z_roll
19.99
19.57
X_pitch
Y_yaw
-40
-60
19.16
18.74
18.32
17.91
17.49
17.07
-20
16.66
0
16.24
Degree
20
Time (s)
Figure 3: The rotations of the 3 DOFs in a subject's shoulder joint during a
trial when stimulation took place. The rapidly changing values between
18.32 and 19.16 seconds reflect the relatively high speed of the reflex
motion compared to the reaching motion.
The withdrawal reflex can be easily identified from the plots in
Figure 3 and Error! Reference source not found.Figure 3 as the reflex
motion is much quicker and produces a more rapid change in the rotation
values than the interrupted reaching motion. It is also clear that the start and
end time of the reflex are consistent over the different DOFs. This allowed
us to extract the duration of the reflex motion and the corresponding
changes in rotation and translation values.
We performed the experiment described above on a group of 10 volunteers,
all males aged between 18 and 25 years of age. This gave us six samples for
each of the five stimulation points. The raw data describing the rotation of
each joint for the first stimulation point, the upper arm, bicep are given in
Table 1. As an exception, we got seven data points for this trial. The first
column gives a unique number of the subject columns two to six give the
rotation angle for each DOF of each joint, three for the shoulder and two for
the elbow. The last column gives the duration of the reflex motion.
Subject
Joint Rotation (degrees)
θ0x
θ0y
θ0z
θ1x
θ1y
Duration
(seconds)
1
0.92 0.23 0.16 39.80 33.99 0.19
2
0.03 0.53 0.58 35.25 34.37 0.40
3
0.06 0.44 0.02 13.94 15.64 0.19
4
1.07 0.08 0.53 4.83
0.31
0.67
5
0.31 0.05 0.01 8.50
7.74
0.34
6
0.03 0.07 0.04 3.71
1.43
0.91
7
0.03 0.07 0.01 14.23 2.53
0.27
x
0.35 0.21 0.19 17.18 13.72 0.42
s
0.45 0.20 0.25 14.53 14.90 0.27
Table 1: The joint rotations for the shoulder and elbow joints for each
subject for stimulation point 1, the bicep, with mean and standard deviation.
Data equivalent to that presented in Table 1 was also produced for the other
four stimulation points.
There was large variance in the data and
subjectively we have classified the responses into three classes: ‘noresponse’, ‘very small response’ and ‘normal response’. For modelling
purposes we plan to use only the data from the responses classified as
‘normal response’.
5
A Robotic Withdrawal Reflex
The concepts of robotic skin and robotic reflexes have both been explored
previously by multiple research groups. In this Section we review that work
and explain how our research differ from that work.
5.1 Robot Skin
Our work will provide study protective reflexes. These can be triggered by
both thermal receptors and mechanoreceptors. However, the first generation
robot skin prototype will only have pressure sensory capabilities.
Historically a wide range of sensory technologies has been studied in the
context of robot tactile sensing including microelectromechanical systems
(Gouaillier & Blazevic, 2006), force-sensitive resistors (FSRs) (Liu et al.,
1995) and a quantum tunnelling compound (Sandini et al., 2006). Dahiya et
al. (2010) have published a comprehensive review of human sensory
capabilities and potential technologies for robot skin. It is an explicit
objective for the ROBOSKIN project to develop robot skin technology
further, including an integration of multiple sensory modalities into a single
sensor. The initial work, however, will be focused on a new sensor
technology that provides purely tactile feedback from large areas of the
body. A picture of the initial prototype skin sensor that will be used for this
work work is given in Figure 4.
Each of the golden disks on the
interconnected triangular base plates is an individual touch sensor. Cannatta
et al. (2008) have presented this technology in detail.
Figure 4: A robot skin prototype fitted on a human hand, picture provided
by the Italian Institute of Technology
5.2 Related Work
Many contemporary robots are designed to imitate the functional skeletal
motions of man or animal in either locomotion or manipulation (Bekey and
Tomovic, 1986). The structures of the robots are anthropomorphic and the
joints of manipulators are commonly termed shoulder, elbow and wrist even
though these joints possess fewer degrees of freedom than the
corresponding human joints.
However, while the joints have some
similarity to human or animal anatomy, such is not the case with robot
control systems. Rather than using biological prototypes for robot control,
current systems are based largely on the application of either classical or
modern control theory to the robots. Yet, functional motions such as
locomotion or manipulation are under the control of complex hierarchical
systems which receive a variety of sensory inputs, perform signal processing
and pattern recognition, infer the desired actions on the basis of the
available knowledge, plan the motion and execute it. The implementation
of robot reflexes has been devised by analogy with the way in which
biological systems process sensory information in producing simple motions
of the extremities which called artificial reflex control (ARC). The ARC
principle was first implemented in the control of multifunctional grasping
devices by Tomovic and Boni (1962). A shape-adaptive artificial hand was
designed and constructed. The sensory input was provided by pressure
sensitive pads on the fingers. Unequal pressure on the pads caused the
fingers to begin closing until the distribution was equalized, thus adapting to
the shape of the object being grasped. A later version of the "Belgrade hand"
was reported in 1975 (Tomovic & Stojiljkovic, 1975). The principle of nonnumerical control has also been applied to the design of assistive devices for
both upper and lower extremity disability (Tomovic and Rabishong, 1975)
(McGhee et al., 1978). The view that locomotion is basically an
asynchronous process, triggered by sensory information and leading to
specific joint states, was proposed by Tomovic and McGhee (1966) and
applied to the construction of a quadruped walking machine by Frank
(1968). Bekey et al. (1977) used a finite state model for the mapping of
muscle states to various pathological gaits in human subjects. The
electromyogram from each of eight muscles in the leg was quantized into
two levels and the resulting binary vector used as an input to a pattern
recognition algorithm.
Brooks et al. (1999) took inspiration from the human vestibule-ocular reflex
(VOR) and opto-kinetic nystigmus (OKN) in the development of the stereo
vision head of the Cog humanoid robot. VOR stabilises the eyes during
rapid head movements by counter rotating the eyes as a response to head
velocity as sensed by the otolith organs in the inner ear. A similar reflex
was implemented on the robot, driving the eyes as a response to rotation
measurements from a gyro.
OKN compensates for slow, smooth head
motions as a response to the optical flow sensed by the retina. A similar
mechanism was implemented using the optical flow on the background
image from Cog’s cameras.
5.3 The Fixed Mean Response Model
To implementing a withdrawal reflex on the Nao humanoid robot we will
sensitise the areas on the robot corresponding to the stimulus points and use
these sensitised areas to trigger the corresponding reflex as represented by
the model used to represent the human capture data. The simplest model we
have developed is based on calculating the mean joint angle difference in
the shoulder (3 DOF), elbow (2DOF) and the mean duration of the
withdrawal reflex.
As a result of individual differences in the underlying reaching motion
produced by the human subjects, the captured human reflexes were
produced from a range of similar, but slightly different poses. Reflexes
based on the fixed mean response model can be expressed in any arm
configuration, but being a simple average of human reflexes produced in
different poses, they are not likely to be realistic as they will not reflect any
differences in motion correlated to the differences in the pose at the time of
stimulation.
5.4 A Gaussian Mixture Model
The fixed response model of reflexes does not take into account differences
in start and end position and related differences in the reflex motion. To
capture these features of the reflex motions, we will also produce a Gaussian
mixture model (GMM) of the reflexes, using the spatially and temporally
normalised human reflex movements.
Compared to the fixed mean
response model, a GMM will provide a more sophisticated reflex model
able to express differences in movement correlated to differences in pose at
the time of stimulation.
5.5 A Hierarchical Self-Organising Map Model
In addition to the Fixed Mean model and the Gaussian mixture model we
will develop a connectionist model based on the Compressed Sparse Code
Hierarchical Self-Organising Map (CoSCo-HSOM) algorithm (Pierris &
Dahl, 2010). The CoSCo-HSOM model has been shown to be more robust
in terms of handling motions of varying duration, not requiring an initial
standardisation. By reusing sparse encodings of the principal components
of observed motions in a hierarchy of increasingly abstract sequences, the
CoSCo-HSOM algorithm achieves highly compressed representations of
motion data, though not as compressed as GMM.
6
Reflexes as Robot Middleware
Modularisation and reuse are two key principles of good software
engineering. We are dedicated to follow these principles and produce selfcontained and reusable functionality to avoid repetition of effort and thus
increase the rate at which robotic research progresses. In this Section we
discuss these concepts in a humanoid robot behaviour context.
6.1 Hardware Independent Generic Skills
In order to support modularisation of humanoid robot behaviours we use the
term middleware. In an operating system context this is software that sits
between the operating system and the user applications. In the humanoid
robotics context we define middleware as the software that exists between
the hardware specific software and the application specific software. An
example of hardware specific software is the software that calculates the
distance to an object based on vergence between the robots eyes, something
that is again dependent of the distance between the robots eyes (Ruesch,
2008). Correspondingly, we can consider the ability to estimate the distance
to an object through vision as generic middleware function.
A typical
example of application specific behaviour is the ability to dynamically
change interactions style, based on tactile interaction patterns, in order to
facilitate communication with an autistic child (Dautenhahn et al., 2009).
Protective reflexes can form a software layer that can potentially be ported
between humanoid platforms, i.e., it can be relatively hardware independent
and provide a generally useful, i.e., domain independent, capability. In
order to realise these ambitions, it is necessary to develop usable interfaces
both downwards to the specific platforms and upwards to as of yet
unspecified applications.
6.2 Humanoid Robot Hardware Abstraction
The iCub project has already done a lot of work on making the iCub
software reusable. Using YARP (yet another robotics platform) (Metta et.
al., 2006) they have separated the hardware from higher level application by
presenting a well defined interface to the hardware in terms on YARP
modules for the head, arms, legs, etc. This work will abstract the iCub
interface further to create a standard humanoid interface. In particular we
aim to provide this interface for the iCub, Nao and Kaspar platforms. Each
of these platforms share a basic physiology but they also have unique
features.
Figure 5: The iCub, Nao and Kaspar humanoid robots, all targets for the
same set of generic withdrawal reflexes
6.3 Integrating Generic and Domain Specific Skills
On the other side of the middleware is the interface it presents to domain
specific applications. Currently, a wide range of behaviour representations
are in use. Many systems use ad-hoc representations built on top of popular
low-level platform standards such as Player/Stage (Vaughan et al., 2003) or
YARP (Metta et al., 2006). There are also efforts at standardising higher
level behaviour representations using frameworks such as behaviour-based
AI (Arkin, 1997) (Proetzsch et al., 2010). On top of these frameworks,
there are a wide range of representations used to learn robot behaviours.
These include artificial neural networks (Yamashita & Tani, 2008),
Gaussian mixture models (Calinon & Billard, 2009) and reinforcement
learning approaches (Hester et al., 2010). While these technologies provide
representations for individual behaviours, they do not provide mechanisms
for integrating skills on different levels.
Our approach will embed the
generic skills in relevant hardware modules, hiding their presence from
developers who, as a result, will be able to use the underlying hardware as
normal with the additional assumption that the robot will keep itself safe by
expressing protective reflexes whenever necessary. For this purpose we will
develop a range of alternative YARP modules, mirroring the existing YARP
representation of the humanoid iCub robot using abstract modules such as
‘arm’, ‘leg’ and ‘head’.
The new modules will, to the developer look
exactly like the existing ones, but they will require the presence of tactile
sensors and they will, in given circumstances, override the commands from
the domain specific applications and instead express a set of built-in
protective reflexes.
7
Conclusions
Our next step is to use the results from our experiments to implement
withdrawal reflexes on the Nao and then to generalise this implementation
so that it can be ported to different platforms and be used beneficially in a
wide range of problem domains. At the same time, we will start capturing
reflex motion for a new scenario where the subject stands still and receives a
stimuli in the hip/lower torso area to see how reflex motions interact with
other crucial capabilities such as balance maintenance that are likely in their
turn to rely on a separate set of reflexes. Finally, we also plan to approach
the reflex modelling problem from an optimisation angle, automatically
adapting robot reflex movements to optimise a set of metrics such as the
minimisation of stimuli strength and duration.
Acknowledgements
This research was partially funded by the European Commission’s Seventh
Framework Programme FP7/2007-2013) under grant agreement number
231500.
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