Conference PaperPDF Available

Simulation Based Selection of Actions for a Humanoid Soccer-Robot

Simulation Based Selection of Actions for a
Humanoid Soccer-Robot
Heinrich Mellmann, Benjamin Schlotter, and Christian Blum
Department of Computer Science, Adaptive Systems Group
Humboldt-Universit¨at zu Berlin, Germany
Abstract. This paper introduces a method for making fast decisions in
a highly dynamic situation, based on forward simulation. This approach
is inspired by the decision problem within the RoboCup domain. In this
environment, selecting the right action is often a challenging task. The
outcome of a particular action may depend on a wide variety of environ-
mental factors, such as the robot’s position on the field or the location of
obstacles. In addition, the perception is often heterogeneous, uncertain,
and incomplete. In this context, we investigate forward simulation as a
versatile and extensible yet simple mechanism for inference of decisions.
The outcome of each possible action is simulated based on the estimated
state of the situation. The simulation of a single action is split into a
number of simple deterministic simulations – samples – based on the
uncertainties of the estimated state and of the action model. Each of the
samples is then evaluated separately, and the evaluations are combined
and compared with those of other actions to inform the overall decision.
This allows us to effectively combine heterogeneous perceptual data, cal-
culate a stable decision, and reason about its uncertainty. This approach
is implemented for the kick selection task in the RoboCup SPL environ-
ment and is actively used in competitions. We present analysis of real
game data showing significant improvement over our previous methods.
1 Introduction
A highly dynamic environment requires a robot to make decisions quickly and
with limited information. In the RoboCup scenario, the robot that is in posses-
sion of the ball needs to take action as quickly as possible before the opponent
players get a chance to interfere. However, the particular situation might be very
complex and many aspects like the robot’s position on the field as well as the
positions of the ball and obstacles need to be taken into account. This makes
inferring a decision a complicated task. In this work we propose an inference
method based on forward simulation to handle this complexity and ensure short
reaction times at the same time. We focus in particular on the RoboCup scenario
where the robot has to choose the best kick from several different possibilities,
which provides the motivation for our approach.
20th RoboCup International Symposium, Leipzig, Germany, July 2016.
In the RoboCup community there have already been several attempts to
implement similar methods. In particular [3, 4] and [1] focus on a very similar
task – the selection of the optimal kick. In [3], a probabilistic approach is used
to describe the kick selection problem which is then solved using Monte Carlo
simulation. In [4], the kick is chosen to maximize a proposed heuristic game sit-
uation score which reflects the goodness of the situation. In [1], the authors use
an instance based representation for the kick actions and employ Markov de-
cision process as an inference method. Internal forward simulation has already
been successfully used as an inference method in robotics. In [2], the authors
investigate navigation of robots in a dynamic environment. They use a simula-
tion approach to envision movements of other agents and pedestrians to enable
avoiding dynamic obstacles while moving towards a goal. In [5] a pancake baking
robot is planning its actions using a full physical simulation of the outcome of
possible actions.
For an effective decision, data from heterogeneous sources (e.g., visual per-
cepts, ultrasound) needs to be combined. Often different filtering/modeling tech-
niques are used for state estimation, which can make inference of decisions a
difficult task. In particular, representation of uncertainty is problematic. As we
will show, the simulation based approach can handle it easily.
The intuition behind a simulation-based approach is to imagine (or simu-
late) what would happen as the result of the execution of a particular action
and then choose the action with the optimal (imagined/simulated) outcome. A
potential issue with this approach is that the quality of the decision depends
on the quality of the simulation, i.e., the model of the environment. For exam-
ple in [5, 6], the robots use complete fine-grained physical simulations for their
decision-making. In contrast, we argue that the simulation itself can be quite
coarse. To compensate for errors in the simulation, it is executed a number of
times with varying initial conditions sampled according to the estimated state of
the situation. Each of these realizations is evaluated individually and the overall
decision for an action is then based on the distribution of the particular evalua-
tions of the simulation. This is repeated for all possible actions (kicks) and the
action with the best outcome distribution is chosen for execution.
We evaluate our approach based on labeled video and log data from real
RoboCup competitions. The results show a significant improvement in compar-
ison to our previous method.
The remainder of the paper is structured as follows. In the next section we
discuss the action selection problem within the RoboCup domain. The main part
of the paper consists of Section 3 and Section 4 where we describe the simulation
and the evaluation-decision processes respectively. Our experimental findings are
discussed in Section 5. Finally we conclude our findings in Section 6.
2 Action Selection Task in Robot Soccer
Consider the situation where a robot approaches the ball and needs to choose
the right action from a fixed number of possibilities. In this study we assume the
Fig. 1. Depictions of three different situations in which the best decision is not clear.
The white robot is the robot having to take a decision on which (kick) action to perform
while the blue robot is an opponent. The ball is depicted in red.
following possible actions: four different kicks, namely kick right, left, forward
short (dribbling) and forward long, and a turn around the ball towards the
opponent goal. The last option is to fall back in case no kick is possible, or if no
kick would improve the situation.
To make an optimal decision different factors need to be taken into account.
In our scenario we include estimated position of the robot on the field, position
of the ball relatively to the robot, and obstacles in direct proximity. Each of
these factors is modeled by a different probabilistic algorithm. We refer to the
collective state estimated by these models as situation state estimation.
We approach this task using forward simulation. The outcome of each of the
five actions is simulated using an estimated state of the situation, evaluated,
and compared. The outcome of an action is described by the resulting position
of the ball. Therefore, we need to model the interaction between the executing
robot and the ball, the dynamics of the ball motion, and its possible interactions
with the environment. In the following section these models will be discussed in
3 Stochastic Forward Simulation
To be able to make decisions the robot needs an estimation of the state of the
situation around it. In our case this state consists of the robot’s position on the
field, position of the ball relative to the robot, positions of the teammates and
obstacles in close proximity. These particular aspects are usually estimated using
various filtering techniques. In our case different independent probabilistic filters
are involved, in particular particle filter for self localization and multi-hypothesis
extended Kalman filter for the ball.
The task of the simulation process is to predict the state of the situation
in case of the execution of a given action, e.g., kick. To do so, we need models
for the effect of the action on the state of the situation, for the dynamics of
particular objects and for interactions between the objects.
In general, an exhaustive physical simulation is a complicated and resource
consuming process. To reduce complexity we make several assumptions. We focus
only on simulating aspects involved in the action, i.e., the motion of the ball and
its potential collision with obstacles and goals. We furthermore assume that all
objects excluding the ball remain static.Though this is obviously not true, the
velocity of the ball is usually much higher than that of the robots, which makes
it a viable assumption in this case. To model collisions with obstacles, especially,
goals we assume a fully nonelastic collision, where the ball’s trajectory ends at
the point of contact. With these assumptions we need to define the dynamic
model of the ball and the model for the effect of the kick on the ball, which we
discuss in the following two sections.
3.1 Ball Dynamics
To describe the dynamics of the ball motion we use a simple rolling resistance
model which leads us to the following motion equation:
d(t) = 1
where d(t) is the distance the ball has rolled after the time t > 0, cRis the rolling
resistance coefficient and v0is the initial velocity of the ball after the kick. By
solving d0(t) = 0 and putting the result in eq. (1) the maximal rolling distance,
i.e., the stopping distance of the ball, can readily be determined as
dmax =v2
The parameters v0and cRof this model have to be determined experimen-
tally. It should be noted that v0depends mainly on the particular kick motion
and cRdepends mainly on the particular carpet of the field, since the ball re-
mains the same. Thus, v0has to be estimated once for each kick motion and cR
once for each particular carpet.
3.2 Kick-Action Model
The result of a kick can be described by the likelihood of the final ball location
after its execution, i.e., positions where the ball is expected to come to a halt
eventually. These positions can be estimated based on the dynamic model of
the ball as described in Section 3.1 and the intended direction of the kick. We
assume the direction of the ball motion αand the initial velocity v0of the kick
behaving according to the Gaussian distribution. With this, the outcome of a
kick action can be described as a tuple of initial velocity v0, direction α, and
corresponding standard deviations σvand σα:
a= (v0, α, σv, σα)R+×[π, π)×R+×[π , π) (3)
We predict the outcome of an action by sampling from the Gaussian distribu-
predict(a) := (dmax(v), α)R+×[π, π) (4)
0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
x [m]
y [m]
Fig. 2. Kick action model: distributions of the possible ball positions after a sidekick
and the long kick forward with the right foot. Blue dots illustrate experimental data.
where vN(v, σv) and αN(α, σα). Note that the function predict(·) is
non-deterministic. Figure 2 illustrates the resulting likelihood for the final ball
positions for a kick forward and a sidekick left. The parameters are estimated
3.3 Simulating the Consequences of an Action
The action is simulated a fixed number of times. The resulting ball position
of one simulation is referred to as a sample. The positions of the samples are
generated according to the model introduced in Section 3.2. The algorithm checks
for possible collisions with the goal box and if there are any, the kick distance
gets shortened appropriately. Collisions with the obstacle model are handled the
same way.
Ahypothesis for the action a∈ A is defined as a set of nNsamples drawn
from the model distribution of an action aas described in Section 3.2.
Ha:= {pi|pi=predict(a), i = 1 . . . n} ⊂ R+×[π, π) (5)
4 Action Selection
The implementation of the simulation algorithm is divided into three main steps:
simulate the consequences for all actions, evaluate the consequences, and decide
the best action. In this section we discuss these components in the case of the
kick selection as described in the Section 3.
4.1 Evaluation
The samples of each hypothesis are individually evaluated by two different sys-
tems. First, each sample h∈ Hais assigned a label
based on where on the field it is, e.g., inside the field, inside the own goal, outside
the field etc. These labels reflect the corresponding discrete rules of the game.
In the second step, all samples labeled I N F IE LD are evaluated by a scalar
potential field encoding the team strategy. An example of a potential field used
in our experiments is described closer in section 4.3.
4.2 Decision
The overall decision has to take into account the trade-off between possible
risks,e.g., ball leaving the field, and possible gains, e.g., scoring a goal, weighted
by the chances of their occurrence. The estimation of those risks and gains can
be done based on the individual ratings of the particular simulation results, i.e.,
samples. The likelihood of the occurrence of an event marked by a label λ∈ L
within a hypothesis Hacan be estimated as
p(λ|a) := |{h∈ Ha|label(h) = λ}|
For instance, the likelihood for scoring a goal with the action acan be written
as p(GOALOPP|a).
In our experiments we use a minimal two step decision process, whereby the
actions that are too risky are discarded in the first step and the one with the
highest gain is selected in the second. More precisely, we call an action too risky
if there is a high chance for kicking the ball out of the field or scoring own goal.
The set of actions with acceptable risk can be defined as:
Aacc := {a∈ A|p(INFIELD GOALOPP|a)T0p(GOALOWN|a)T1}(8)
with fixed thresholds T0and T1(in our experiments we used T0= 0.85 and
T1= 0). Note that the cases indicated by OUT and C OLLISION are treated
the same by this rule. From this set the actions with the highest likelihood of
scoring a goal are selected
Agoal := argmax {p(GOALOPP|a)|a∈ Aacc}.(9)
In case that Agoal is empty the default action is always turn around the ball.
In case Agoal contains more than one possible action, the best action is selected
randomly from the set of actions with the maximal strategic value based on the
potential field
a0argmax{value(a)|a∈ Ag oal}(10)
Fig. 3. Three examples for kick simulations. Each possible kick direction is simulated
with 30 samples (different colors correspond to different kicks). Left: the short and long
kicks are shortened due to collision with an obstacle. Middle: long kick is selected as
the best action since it has the most samples result in a goal. Right: the best action is
sidekick to the right – the other kicks are more likely to end up in a dangerous position
for the own goal according to the potential field.
with strategic values defined as
value(a) := Z
p(x|a)·potential(x) dx=1
potential(xi) (11)
Figure 3 illustrates several situations with the corresponding simulated hypothe-
ses and their evaluations.
4.3 Potential Field
A potential field assigns a value to each position of the ball inside the field. The
values reflect the static strategy of the game and are used to compare possible
ball positions in terms of their strategic value. For instance, the position a meter
away in front of the opponent goal is obviously much better than the one in front
of the own goal. In our experiments we use the following potential field:
P(x) = xT·νopp
| {z }
linear slope
N(x|µopp, Σopp )
| {z }
opponent goal attractor
+N(x|µown, Σown)
| {z }
own goal repulsor
where N(·|µ, Σ) is the normal distribution with mean µand covariance Σ. It
consists of three different parts: the linear slope points from the own goal towards
the opponent goal and is modeling the general direction of attack; the exponential
repulsor N(x|µown, Σown) prevents kicks towards the center in front of own goal;
and N(x|µopp, Σopp ) creates an exponential attractor towards the opponent goal.
Fig. 4. Strategic potential field evaluating ball positions. Own goal is on the left (blue).
The configuration used in our experiments is
νopp = (1/xopp,0)T(13)
with xopp = 4.5 being the x-position of the opponent goal and
µown = (4.5,0) µopp = (4.5,0) (14)
Σown =3.37520
0 1.22Σopp =2.2520
0 1.22(15)
for the repulsor and attractor respectively. All parameters are of unit m. Figure 4
illustrates the resulting potential field.
4.4 Kick Selection Visualization
Figure 5 illustrates the decisions made by the algorithm depending on the robot’s
position on the field with the ball in front of the robot for three different fixed ori-
entations of the robot. Since the simulation it stochastic, the decision is repeated
20 times for each cell on the field.
5 Quantitative Analysis in Real Game Situations
In general evaluation of decision algorithms is difficult because they tend to
behave differently in the isolated environment of the lab than under real con-
ditions, e.g., during a soccer competition. In this section we present analysis of
the simulation based action selection using human labeled combined video and
log data from real games.
long forward short forward turn sidekick left sidekick right
Fig. 5. Resulting decisions based on different positions of the ball on the field and three
different orientations of the robot. Different colors correspond to differnt decisions. The
orientation of the robot is indicated by the arrow. Own goal is at the bottom, opponent
at the top.
5.1 Methodology
Evaluation of algorithms in real robot soccer competition conditions is a chal-
lenging task. This is mainly because in a real game many factors affect the
performance of the robot in a particular situation, e.g., robot executes a wrong
kick because it is not localized correctly. To minimize the influence of side factors
on the evaluation we need to observe what actually happened and the internal
state of the robot at the same time.
For this purpose we recorded videos overlooking the whole field of the games
during RoboCup competitions in 2015 alongside with log files recorded by each
of the robots. Video recordings provide a ground truth of the situation while log
data recorded by the robots provides the corresponding internal state. The log
files contain perceptions and the behavior decision tree for every cognition cycle
(33 ms). This allows us to extract the situations when the robot took a decision
to kick.
The logs have been synchronized with video files and the extracted kick ac-
tions manually labeled. The labeling procedure has been performed with the help
of the interface which had been designed specifically for this purpose. Figure 6
illustrates an example of a labeling session for the first half of the game with the
team NaoDevils at the RoboCup 2015.
The labeling criteria consist of 15 distinct boolean labels in three categories:
technical execution of the kick, e.g., robot did miss the ball; situation model
(was the estimation of robots position on the field and the ball correct?); result
of the action and strategic improvement of the situation (ball left the field, was
moved closer to the opponent goal etc.).
Fig. 6. Illustration of the labeling interface used to collect data regarding the quality
of the kicks. At the bottom are time lines for each of the robots. Different actions are
represented by buttons on the time line with different colors. On the right the robots
estimated state is visualized, i.e., estimation of its position, ball model and obstacles.
On the left are three categories of labels capturing the quality of the action.
5.2 Data Set
For our analysis we took a look at the games our team has played in two differ-
ent competitions in 2015 – the German Open 2015 (GO15) and RoboCup 2015
(RC15). In both competitions our robots performed well – we reached the third
place at the German Open and quarter finals at the RoboCup. At the GO15
we used our previous solution for action selection based on a manually adjusted
heuristic decision tree and a potential field indicating the best direction towards
the goal while at the RC15 the presented simulation based approach had been
From GO15 a total of five game halves have been analyzed with: ZKnipsers
(two halves, preliminaries); HULKS (first half, preliminaries); and Nao Devils
(two halves, game for the 3rd place). And from RC15 we analyzed three complete
games with: RoboCanes (two halves, preliminaries); Nao Devils (two halves,
intermediate round); and HTWK (two halves, quarter finals). The selection of
the games depends largely on the availability of the videos and log data.
5.3 Results
To single out the effect of the kick selection we focus on kicks where the robot
was well localized (so it knew what it was doing) and kicks where executed
successfully, i.e., the ball went in the intended direction and did not collide with
opponent. In short: successful kicks are the ones which comply with our action
Algorithm New Old
Total number of kicks 163 196
Robot was localized 150 (92.02 %) 165 (84.18 %)
Successful execution 93 (57.06 %) 153 (78.06 %)
Failed execution 70 43
Failed: opponent interference 33 (47.14 %) 14 (32.56 %)
Failed: technical failure 37 (52.86 %) 29 (67.44 %)
Successful execution + Localized 86 (52.76 %) 131 (66.84 %)
+1 67 (77.91 %) 88 (67.18 %)
0 15 (17.44 %) 39 (29.77 %)
-1 4 (4.65 %) 4 (3.05 %)
Out at opponent goal line 1 (1.16 %) 8 (6.11 %)
Table 1. Analysis results of video material. The new algorithm shows a higher rate of
strategic improvements (+1) and a lower rate of mediocre kicks (0). It is also about 5
times less likely to kick out at the opponent field line.
model as described in section 3.2. The top part of the table 1 illustrates the
numbers of the successful and failed kicks.
Our analysis has also revealed that a high percentage of the actions fail
due to various reasons. The main reasons appear to be failure in the technical
execution, e.g., the robot trips and doesn’t kick the ball properly, and interference
by opponent players. Both aspects are not part of the simulation and require
further investigation. The table 1 (Failed execution) summarizes the rates of
the failed kicks split in these two cases. The higher opponent interference in the
case of the new approach can be explained by the more challenging opponent
teams at the RC15.
In the lover part of the table 1 we summarize the evaluation of the the kick
results according to the strategic improvement of the ball position as described
in section 5.1. The separation used here is very rough: +1 corresponds to the
cases where the strategic position of the ball was clearly improved by the action,
e.g., it was moved closer towards the opponent goal; -1 was given when the
ball moved towards own goal or away from the opponent goal; and 0 when no
improvement was visible, e.g., ball moved along the middle line. The results show
that the new approach results in a higher rate of improvements (+1) and a lower
rate of mediocre kicks (0), while the rate of cases where the position of the ball
worsened (-1) remained at a comparable level.
Another important factor is the number of times the ball leaves the field
because it results in a tactical disadvantage as the ball is replaced into the field.
The penalty is especially large when the ball leaves on the opponent goal line,
since the ball is then reset to the middle line. In this case we can see a significant
improvement with the new approach as only one kick (1.16%) left the field at the
opponent goal line in contrast to more than 6 % (8 kicks) with the old solution.
In summary, the data shows that the new approach performs more robustly
than our previous solution. The new algorithm is about 5 times less likely to
kick out at the opponent field line (decrease by 81%) and 16 % more likely to
kick towards the opponent goal.
6 Conclusions and Future Work
We presented and discussed an action selection algorithm based on forward sim-
ulation. We discussed its application in the scenario of kick selection for robot
soccer. This kick selection algorithm was successfully implemented and used in
RoboCup competitions. The three main advantages of the presented approach
are easy implementation and extensibility. Experimental data collected in real
RoboCup games has shown that the algorithm performs very well and is an
improvement over the algorithm used by our team up to now.
Our current effort focuses in particular on stepwise extension to simulating
the ball approach and more dynamic evaluation. For instance, the potential field
might reflect the influence regions of the own teammates based on their position,
which would favor the kicks towards these regions and enable emergent passing.
At the present state the implemented method is limited to the selection of
the kicks only. We believe that the true potential of the forward simulation can
only unfold if extended to all areas of decision making like role decision, passing,
positioning etc.
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Conducting games in RoboCup incurs high cost in terms of effort, time, and money. The scientific outcome, however, is quite limited and often not very conclusive. Especially, analyzing and drawing conclusions about the performance of complex processes like decision making of an individual robot or the behavior on the team level poses a considerable challenge. Collecting more data during the competition games will help to analyze the performance of algorithms, identify errors and areas for improvement, and make more significant statements regarding the performance of the robots. In this work we investigate the possibilities for collection of the large scale RoboCup data and its analysis. We present a system for automatic recording of synchronized videos of RoboCup games and an application for exploration and annotation of large sets of RoboCup-related data. We also present data sets collected during the competitions in 2018 and an algorithm for visual detection and tracking of robots in the RoboCup videos. A first empirical evaluation shows promising results and demonstrates how such data can be integrated and used to validate robot’s behavior.
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