Towards the Use of Quality-of-Service Metrics in Reinforcement Learning: A Robotics Example

Conference Paper (PDF Available) · October 2018with 307 Reads
Conference: MORSE - 5th International Workshop on Model-driven Robot Software Engineering (in conjunction with MODELS 2018), At Copenhagen (Denmark)
Abstract
Service robots are expected to operate in real-world environments, which are inherently open-ended and show a huge number of potential situations and contingencies. This variability can be addressed applying reinforcement learning, which enables a robot to autonomously discover an optimal behavior through trial-and-error interactions with the environment. The process is carried out by measuring the improvements the robot achieves after executing each action. In this regard, RoQME, an Integrated Technical Project of the EU H2020 RobMoSys Project, aims at providing global robot Quality-of-Service (QoS) metrics in terms of non-functional properties, such as safety, reliability, efficiency or usability. This paper presents a preliminary work in which the estimation of these metrics at runtime (based on the contextual information available) can be used to enrich the reinforcement learning process.
Towards the Use of Quality of Service Metrics in
Reinforcement Learning: A Robotics Example
J. F. Inglés-Romero1, J. M. Espín1, R. Jiménez2, R. Font1 and C. Vicente-Chicote3
1 Biometric Vox S.L., Spain
2 Infomicro Comunicaciones S.L., Spain
3 University of Extremadura, QSEG, Escuela Politécnica de Cáceres, Spain
juanfran.ingles@biometricvox.com
Abstract. Service robots are expected to operate in real-world environments,
which are inherently open-ended and show a huge number of potential situa-
tions and contingencies. This variability can be addressed applying reinforce-
ment learning, which enables a robot to autonomously discover an optimal be-
havior through trial-and-error interactions with the environment. The process is
carried out by measuring the improvements the robot achieves after executing
each action. In this regard, RoQME, an Integrated Technical Project of the EU
H2020 RobMoSys Project, aims at providing global robot Quality-of-Servi ce
(QoS) metrics in terms of non-functional properties, such as safety, reliability,
efficiency or usability. This paper presents a preliminary work in which the es-
timation of these metrics at runtime (based on the contextual information avail-
able) can be used to enrich the reinforcement learning process.
Keywords: Reinforcement Learning, Quality of Service, RoQME.
1 Introduction
With the advance of robotics and its increasingly growing use in all kinds of real-
world applications, service robots are expected to operate (at least safely and with a
reasonable performance) in different environments and situations. In this sense, a
primary goal is to produce autonomous robots, capable of interacting with their envi-
ronments and learning behaviors that allow them to improve their overall performance
over time, e.g., through trial and error. This is the idea behind Reinforcement Learn-
ing (RL) [1], which offers a framework and a set of tools for the design of sophisticat-
ed and hard-to-engineer behaviors.
In RL, an agent observes its environment and interacts with it by performing an ac-
tion. After that, the environment transitions to a new state providing a reward. The
goal is to find a policy that optimizes the long-term sum of rewards. One of the fun-
damental problems of RL is the so-called cursing of the objective specification [1].
Rewards are an essential part of any RL problem, as they implicitly determine the
desired behavior. However, the specification of a good reward function can be highly
complex. This is, at least in part, because it requires to accurately quantify the re-
wards, which does not fit well with the natural way people express objectives.
MORSE 2018, Copenhagen, Denmark J.F. Inglés-Romero et al.
The RoQME Integrated Technical Project (ITP), funded by EU H2020 RobMoSys
Project [2], aims at contributing a model-driven tool-chain for dealing with system-
level non-functional properties, enabling the specification of global Robot Quality of
Service (QoS) metrics. RoQME also aims at generating RobMoSys-compliant com-
ponents, ready to provide other components with QoS metrics. The estimation of
these metrics at runtime, in terms of the contextual information available, can then be
used for different purposes, e.g., as part of a reward function in RL.
Integrating QoS metrics in the rewards can enrich the learning process by extend-
ing the quality criteria considered, for example, with non-functional properties, such
as user satisfaction, safety, power consumption or reliability. Moreover, RoQME
provides a simple modeling language to specify QoS metrics, in which qualitative
descriptions predominate over quantitative ones. As a result, RoQME limits the use of
numbers, promoting a more natural way of expressing problems without introducing
ambiguity in its execution semantics.
This paper presents a work in progress towards the use of QoS metrics in RL. San-
ta Bot, a toy” example in which a robot delivers gifts to children, will help us illus-
trate the problem in simple terms.
The rest of the paper is organized as follows. Section 2 describes the Santa Bot ex-
ample. Section 3 introduces the RoQME modeling language. Section 4 shows some
simulation results for the Santa Bot example. Section 5 reviews related work and,
finally, Section 6 draws some conclusions and outlines future work.
2 Santa Bot: an illustrative example
This section introduces the example that will be used throughout the paper to illustrate
the proposed approach. The goal is to present the reinforcement learning problem in
simple terms to explain the role that the RoQME QoS metrics can play. The example
takes place in a shopping mall where a robot, called Santa Bot, distributes gifts to
children. In the area set up for this purpose, a number of children waits in line to re-
ceive a gift from Santa Bot. Each child has a (finite) list of wishes, containing his/her
most desired toys in order of preference. Unfortunately, these lists were made in se-
cret and are unknown to Santa Bot. Santa Bot will try to guess the best gift for each
child to meet their expectations and thus maximize their joy.
2.1 Formalizing the example scenario
Let us consider a queue of M children waiting to receive a gift, i.e.,
!"#$%&'
!"#$%() !"#$%*
. As the order prevails,
!"#$%+
will receive a gift after
!"#$%+,&
and be-
fore
!"#$%+-&
. Moreover, we identify all the different types of toys with natural num-
bers, i.e., Toys = {1, 2, 3…, K}. At time t, the Santa Bot bag contains a number of
instances of each toy k, denoted by
./
01 23'4' ) ' ./
56
, being
./
5
the initial amount. As
gifts are delivered, the number of instances of a toy decreases, remaining at 0 when
the toy is no longer available, therefore,
./
07 ./
0-&
. In this scenario, the action of
Towards the use of QoS Metrics in Reinforcement Learning J.F. Inglés-Romero et al.
Santa Bot is limited to deciding what gift is given to each child. Listing 1 specifies the
effect of this action.
Being !"#$%+ the child to receive a gift at time t
deliver gift k to childi
[pre-condition] ./
08 3
[post-condition] ./
0-& 9 ./
0: 4
Listing 1. Specification of the delivery action in the example scenario.
Although Santa Bot can adopt different strategies for delivering the gifts in its bag,
the best approach will be the one that maximizes children satisfaction. In this case,
satisfaction is associated with the ability to fulfill the children’s wishes, expressed in
their wish lists. Thus, we consider that each child has a wish list represented by a n-
tuple ;
<&'<(' ) ' <=
>
'
where entries are toy identifiers (
), and show uniqueness
(
C#' D 1
2
4'E' ) ' F
6
'<+9 <GH # 9 D
) and order (
<+
is preferred over
<G
if and only if
# I
D
). Moreover, the function
JK
;
!"#$%+
>
9
;
<&' <(' ) ' <=
> links children to wish lists,
such that
JK;!"#$%&> 9 ;E' L' 4>
indicates that the first child in the queue wants toys 2,
6, and 1, in order of preference.
Equation 1 shows a possible definition of satisfaction for
!"#$%+
receiving a toy j.
This function provides a score that determines the goodness of a decision, so the
higher its value the better.
M
;
!"#$%+' D
>
9
N
O
;
P
>
Q R;D : </>
CST1UV;WX+Y Z[>
(1)
Being
O\
a decreasing positive function and
R
the Kronecker delta function, i.e.,
R
;
]
>
9
4
when x=0, otherwise it is 0. It is worth noting that Equation 1 produces 0 if the deci-
sion does not match any option in the wish list, otherwise it increases as the selected
gift has a higher position in this list. Finally, the result of the problem is the entire
sequence of decisions made for all the children, i.e.,
% 9
;
%&' %(' ) ' %*
>
'
where
%+
indi-
cates the toy delivered to
!"#$%+
. Equation 2 shows the overall satisfaction considering
the complete sequence of decisions d.
MZ9
N
M;!"#$%+' %+>
C+
(2)
2.2 The reinforcement learning problem
Santa Bot poses an optimization problem whose optimal solution would be feasible
using integer linear programming if all wish lists were known. However, since this is
not the case, the robot is expected to autonomously discover the optimal solution
through trial-and-error interactions with its environment. In Reinforcement Learning
(RL) [1], an agent observes its environment and interacts with it by performing an
action. After that, the environment transitions to a new state providing a reward. The
goal of the algorithm is to find a policy that optimizes the long-term sum of rewards.
The main elements of a RL problem (states, transitions, actions and rewards) are usu-
ally modeled as a Markov Decision Process (MDP) [3]. Fig. 1 shows the basic MDP
MORSE 2018, Copenhagen, Denmark J.F. Inglés-Romero et al.
specification for the Santa Bot example. It is worth noting that, for the sake of sim-
plicity, we will not delve into the details of RL and MDP.
The Santa Bot environment considers two sets of states: In-front and Leaving. The
former indicates that a child is in front of Santa Bot waiting for a gift. In this situation,
the state is defined in terms of the gifts available (i.e.,
./
0
) and some observable fea-
tures of the child. In the example, we have supposed that the robot can perceive the
apparent age, the gender and the predominant color of the child’s clothes. Ideally,
these features will include sufficient information to trace preferences and common
tastes among children with similar aspects. Actually, a successful learning process
should be able to detect these tendencies and exploit them by making good decisions.
Once the robot performs the delivery action, the environment transitions from In-
front to Leaving. Note that it returns to In-front when a new child arrives. Leaving
integrates the satisfaction of the child with the gift, which is represented by a QoS
metric. This metric provides a real value in the range [0,1] indicating how much the
child liked the gift (being 1 the highest degree of satisfaction). The reward function
will depend on this value, e.g., see Equation 3, where the reward changes in a linear
way from 0 to
^
according to the satisfaction.
_`a<_% 9 ^ Q B<b#Bc<!b#@F' ^ 1 d-
(3)
The reward function is an essential part of any RL problem, as it implicitly deter-
mines the desired behavior we want to achieve in our system. However, it is very
difficult to establish a good reward mechanism in practice. Note that Equation 3
seems simple because we have moved the complexity to the specification of the QoS
metric, i.e., to how the robot measures satisfaction. The following section illustrates
how RoQME can alleviate the complexity of specifying rewards by supporting the
definition of QoS metrics.
Fig. 1. Simple Markov Decision Process for the Santa Bot example.
3 Modeling robot QoS metrics
RoQME aims at providing robotics engineers with a model-driven tool-chain allow-
ing them to: (1) specify system-level non-functional properties; and (2) generate
RobMoSys-compliant components, ready to provide other components with QoS
metrics defined on the previous non-functional properties. In the following, we use
the Santa Bot example to present the main modeling concepts of RoQME and how
they are translated into QoS metrics at runtime. More information about the RoQME
meta-model and its integration into RobMoSys can be found in [4].
Child features
deliver (gift k)
Gifts available !"
#
$ℎ&'()
IN FRONT
i=i+1
$ℎ&'()
LEAVING
ACTION:
REWARD:
reward (satisfaction)
QoS metric:
Satisfaction
Towards the use of QoS Metrics in Reinforcement Learning J.F. Inglés-Romero et al.
The previous section left open the specification of the QoS metric for measuring the
satisfaction of a child after receiving a gift (hereinafter, simply denoted as satisfac-
tion). It is worth noting that QoS metric is not a modeling concept in RoQME, but
rather, a runtime artifact implicitly bound to a non-functional property. Non-
functional properties, which can be thought of as particular quality aspects of a sys-
tem, are included in the modeling language, thus, in our example, satisfaction is mod-
eled as a non-functional property using the keyword property (see line 4
in Listing 2). Regarding the execution semantics, a RoQME model abstracts a Belief
Network [3], in which properties are represented by unobserved Boolean variables. In
this sense, the variable associated with satisfaction would indicate whether or not
Santa Bot is optimal in terms of this property. The runtime quantification of this belief
results in the corresponding QoS metric value. For example, a resulting value of 0.67
can be understood as the probability of the gift being satisfactory for the child.
The belief of a property (i.e., the QoS metric) fluctuates over time according to the
evidences observed by the robot in its environment (contextual information). RoQME
allows specifying observations (observation) as conditions in terms of context vari-
ables (context), so that the detection of an observation will reinforce (or undermine)
the belief. In the belief network, observations are evidence variables that exhibit a
direct probabilistic dependence with the property.
Lines 5-8 in Listing 2 show four observations for the Santa Bot example. These
observations use the following context variables: (1) face, which indicates the facial
expression of the child after receiving a gift; and (2) age, the apparent age of the child
perceived by the robot. Note that the robot will continuously feed RoQME with this
contextual information. Each observation will reinforce or undermine the child satis-
faction according to the emotion expressed by the child. We have assumed that sur-
prise and anger are stronger emotions than joy and sadness, and thus the first ones
should have a higher influence on satisfaction than the second ones. Moreover, obser-
vations 1 and 4 are conditioned by age, which means that strong reactions tend to be
more or less frequent depending on the age. Note that toddlers may tend to react more
vividly than school-aged children. Therefore, it is used to normalize the effect of the
observation among children of different ages.
1 context face : eventType {JOY, SURPRISE, NEUTRAL, SADNESS, ANGER}
2 context age : enum {TODDLER, PRESCHOOLER, SCHOOL_AGED , ADOLESCENT}
3 context prevSatisfaction : number
4 property satisfaction : nu mber prior prevSatisfaction
5 observation obs1 : SU RPRISE reinforces satisfaction hig hly conditionedBy age
6 observation obs2 : JO Y reinforces satisfaction
7 observation obs3 : SA DNESS undermines satisfaction
8 observation obs4 : AN GER undermines satisfaction highly conditionedBy age
Listing 2. A simple RoQME model for modeling children satisfaction.
Finally, the context variable prevSatisfaction provides the satisfaction of the child
who received the gift just before the current one. We want to use this information to
model possible influences between two consecutive children, e.g., a child expressing
MORSE 2018, Copenhagen, Denmark J.F. Inglés-Romero et al.
anger could have an effect on the behavior of the following child. This is implement-
ed by defining a bias in the prior probability of satisfaction (see line 4).
As we have already mentioned, a RoQME model translates all its semantics into a
belief network. Fig. 2 shows the qualitative specification of the network resulting
from the model in Listing 2. Note that, for the sake of clarity, we have omitted proba-
bilities. This belief network will be the “brain” of a generated RobMoSys-compliant
component aimed at measuring satisfaction. In general, the generated component will
estimate the value of each non-functional property, specified in the RoQME model,
by successively processing the available contextual information, either from internal
(e.g., robot sensors) or external (e.g., web services, other robots, etc.) sources. The
contextual information received by the component will be sequentially processed by:
(1) a context monitor that receives raw contextual data and produces context events;
(2) an event processor that searches for the event patterns specified in the RoQME
model and, when found, produces observations; and, finally (3) a probabilistic rea-
soner that computes a numeric estimation for each metric. This information could
then be used by other components, e.g. the robot task sequencer could integrate the
RL process to adapt the robot behavior according to the provided QoS metrics.
Fig. 2. Belief network resulting from the RoQME model developed in Listing 2.
4 QoS metrics in action
This section presents the simulation results obtained on the Santa Bot example. Be-
fore detailing the results, let us describe the simulation setting.
Queues of children. We have generated random queues of length 50 with children
showing different features (i.e., age, gender and clothes color). In particular, we have
considered four age groups: (1) toddlers, (2) preschoolers, (3) school-aged children
and (4) adolescents, with the following density function [0.4, 0.3, 0.2, 0.1], and uni-
formly distributed gender and clothes colors (5 colors).
Wish lists. For each child, we have produced a 3-item list from 20 different toys.
Preferences were distributed depending on the children features (age, gender and
clothes color). These dependencies create tendencies that are expected to be detected
and exploited by the learning process. The correlation is clearly exposed in Fig. 3,
where the heat map represents favorite toys in relation to children features.
Children reactions. As we described in Section 3, RoQME estimates satisfaction
considering as contextual information the age and the face expression of the children.
Satisfaction
Age
Previous
satisfaction
Obs 1Obs 2Obs 3Obs 4
Towards the use of QoS Metrics in Reinforcement Learning J.F. Inglés-Romero et al.
While the former has already been defined, the children reactions need to be estab-
lished. For that, we have assigned face expressions to each child receiving a particular
gift according to his/her features and wish list. The idea is to introduce inclinations
similar to those described in Section 3. It is obvious that the algorithm will not be able
to learn if the RoQME model for satisfaction does not reflect reality.
Simulations. We have executed the simulation on 1000 episodes, where each episode
consists of a new queue of 50 children. In addition, we have considered that Santa Bot
has an infinite number of gifts available for each type of toy. The left side of Fig. 4
shows the learning process in an initial state (episode 25), in which the exploration of
the states (i.e., choosing a random action) is preferred to acquire new information and
discover the best actions. This can be seen in the upper heat map, where Santa Bot
delivers gifts following a uniform approach. As for the cumulative rewards represent-
ed in the lower heat map, it begins to show the correlations we have introduced in the
data (see Fig. 3). On the other hand, the right side of Fig. 4 shows the learning process
in an advanced state (episode 1000), in which the exploitation (i.e., choosing the best
action according to the information already learned at that moment) is preferred over
exploration. The upper heat map shows how the process seems to prioritize the gifts
that have provided greater reward. As for the lower map, it is similar to Fig. 3, which
means the learning process was successful.
Fig. 3. Heat map of gift probabilities by child type.
Fig. 4. Up, the number of visits to the Q-matrix cell; down the Q-matri x. (Left) Episode 25
of the learning algorithm. (Right) Episode 1000 of the learning algorithm.
MORSE 2018, Copenhagen, Denmark J.F. Inglés-Romero et al.
Fig. 5. (Left) Score evolution over 1000 episodes. (Right) Q-matrix after 1000 episodes, in
which the RoQME model estimates satisfaction wrongly.
The left side of Fig. 5 shows the evolution of the learning process, in which the score
seems to stabilize after 400 episodes. Finally, the system has achieved an average
score of 72.03% and a maximum score of 84.67% with respect to the optimal solution
(the one with known wish lists). Finally, we have modified the RoQME model to
observe the effect of modeling wrongly. The right side of Fig. 5 shows that, in this
case, the process fails to learn.
5 Related work
Reinforcement learning emerged as a combination of optimal control (using dynamic
programming) and trial-and-error learning (inspired by the animal world) [5]. In the
last years, thanks to more powerful computing systems and new deep learning tech-
niques, RL has received an impulse in domains such as video games and simula-
tions [6]. In the field of robotics, optimization problems have a temporal structure,
where RL techniques seem to be suitable. However, there may be cases showing high
dimensionality continuous states and actions, with states not fully observable and
noise-free. Those cases generally result in modeling failures that can be accumulated
over time, so training with physical agents is needed [1].
In the literature, we can find numerous RL techniques applied to diverse robotic
tasks. For example, robot manipulators aimed at learning how to reach a certain posi-
tion or open a door [7-9]; or mobile robots that learn how to move in crowded spac-
es [10]. Recently, RL has been used to teach a car how to drive in a short period of
time with just a camera and feedback of speed and steering angle [11].
Despite the large number of applications, one of the fundamental problems of RL
is the so-called cursing of the objective specification [1]. Rewards are a crucial part of
any RL problem, as they implicitly determine the desired behavior we want to
achieve. However, the specification of a good reward function can be highly complex.
In this sense, domain experts may be required to define the proper reward function.
To alleviate this problem, different techniques have been used to define reward
functions. A perfect scenario would be a human supervising the agent and providing
feedback about the reward that the system should give according to its actions, but
this is expensive and error prone. In [12], authors use EEG-signals of a human user as
reward function, so that the user unconsciously teaches the agent to act as he wants.
Towards the use of QoS Metrics in Reinforcement Learning J.F. Inglés-Romero et al.
Another way is, firstly, to train a learner to discover which people actions are better
than others, and then use it in RL to give reward to the agent simulating a person.
Regarding the perspective applied in [13], it creates a Bayesian model based on feed-
back from experts. On the contrary, in [14], non-expert people are considered to train
the learner with reinforcement learning techniques.
Some rules defined at design time in a lax way can be introduced in the learning
process as a bias to enhance the behavior as in [10], where the robot has to respect
some social rules of circulation.
Regarding QoS, in [15] the system tries to autonomously improve the quality of the
services of the robot by adapting its component-based architecture and applying RL to
meet the user preferences. The system learns how to estimate the non-functional
properties in the process as it has not prior knowledge about them.
6 Conclusions and future work
This paper presents a preliminary work about the integration of RoQME QoS metrics
into the reward strategy of RL problems. Moreover, we have introduced and formal-
ized Santa Bot, an optimization “toy” example inspired by Santa Claus, used to illus-
trate the explanations in simple terms. In the following, we highlight some remarks:
The execution semantics of a QoS metric relies on a belief network, which is a
well-known mathematical abstraction that has been successfully applied to many
domains, such as medical diagnosis and natural language processing. Consequent-
ly, we can benefit from existing tools and techniques that are used for the analysis
and simulation of probabilistic networks.
The RoQME modeling language allows users to transparently specify the qualita-
tive part of the underlying belief network (i.e. nodes and arcs of the directed acy-
clic graph). The RoQME framework is in charge of automatically completing the
quantitative part of the network (i.e., the conditional probability tables). As quanti-
fication is often referred to as a major obstacle in building probabilistic net-
works [2], RoQME eases the modeling process by abstracting probabilities. In this
sense, although there are many probabilistic programming languages [16] that can
be used to specify belief networks, unlike RoQME, they usually need a detailed
specification of probabilities.
Although the specification of RoQME QoS metrics does not need to be addressed
by domain experts, a RoQME model that does not sufficiently represent reality will
have a great impact on the learning process.
We have simulated the Santa Bot example considering an unlimited number of
gifts. Although this relaxation of the problem has not affected the explanations, it
is pending to take more advantage of the example and to apply our approach to
more realistic robotics scenarios.
For the future, we plan to continue exploring the potential of RoQME QoS metrics
applied to RL. We also intend to study ways of improving the QoS modeling process.
MORSE 2018, Copenhagen, Denmark J.F. Inglés-Romero et al.
Acknowledgements
RoQME has received funding from the European Union’s H2020 Research and Inno-
vation Programme under grant agreement No. 732410, in the form of financial sup-
port to third parties of the RobMoSys project.
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  • Conference Paper
    Full-text available
    Non-functional properties play a key role in most software systems. There is a lot of literature on what non-functional properties are but, unfortunately, there is also a lot of disagreement and different points of view on how to deal with them. Non-functional properties , such as safety or dependability, become particularly relevant in the context of robotics. In the EU H2020 RobMoSys Project, non-functional properties are treated as first-class citizens and considered key added-value services. In this vein, the RoQME Integrated Technical Project, funded by RobMoSys, aims at contributing a component-based and model-driven tool-chain for dealing with system-level non-functional properties, enabling the specification of global Quality of Service (QoS) metrics. The estimation of these metrics at runtime, in terms of the contextual information available, can then be used for different purposes, such as robot behavior adaptation or benchmarking.
  • Conference Paper
    Full-text available
    Non-functional properties play a key role in most software systems. There is a lot of literature on what non-functional properties are but, unfortunately, there is also a lot of disagreement and different points of view on how to deal with them. Non-functional properties , such as safety or dependability, become particularly relevant in the context of robotics. In the EU H2020 RobMoSys Project, non-functional properties are treated as first-class citizens and considered key added-value services. In this vein, the RoQME Integrated Technical Project, funded by RobMoSys, aims at contributing a component-based and model-driven tool-chain for dealing with system-level non-functional properties, enabling the specification of global Quality of Service (QoS) metrics. The estimation of these metrics at runtime, in terms of the contextual information available, can then be used for different purposes, such as robot behavior adaptation or benchmarking.
  • We consider the problem of learning control policies via trajectory preference queries to an expert. In particular, the agent presents an expert with short runs of a pair of policies originating from the same state and the expert indicates which trajectory is preferred. The agent's goal is to elicit a latent target policy from the expert with as few queries as possible. To tackle this problem we propose a novel Bayesian model of the querying process and introduce two methods that exploit this model to actively select expert queries. Experimental results on four benchmark problems indicate that our model can effectively learn policies from trajectory preference queries and that active query selection can be substantially more efficient than random selection.
  • Article
    Full-text available
    Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.
  • Conference Paper
    Developing robots capable of fine manipulation skills is of major importance in order to build truly assistive robots. These robots need to be compliant in their actuation and control in order to operate safely in human environments. Manipulation tasks imply complex contact interactions with the external world, and involve reasoning about the forces and torques to be applied. Planning under contact conditions is usually impractical due to computational complexity, and a lack of precise dynamics models of the environment. We present an approach to acquiring manipulation skills on compliant robots through reinforcement learning. The initial position control policy for manipulation is initialized through kinesthetic demonstration. We augment this policy with a force/torque profile to be controlled in combination with the position trajectories. We use the Policy Improvement with Path Integrals (PI2) algorithm to learn these force/torque profiles by optimizing a cost function that measures task success. We demonstrate our approach on the Barrett WAM robot arm equipped with a 6-DOF force/torque sensor on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table. We show that the learnt force control policies allow successful, robust execution of the tasks.
  • Robot reinforcement learning using EEGbased reward signals
    • I Iturrate
    • L Montesano
    • J Minguez
    Iturrate, I., Montesano, L. and Minguez, J.: Robot reinforcement learning using EEGbased reward signals. 2010 IEEE International Conference on Robotics and Automation (2010): 4822-4829.