AAMAS'10 Workshop on Agents Learning Interactively from Human Teachers (2010)
Impact of Human Communication in a Multi-teacher,
Multi-robot Learning by Demonstration System
Murilo Fernandes Martins
Yiannis Demiris
Dept. of Elec. and Electronic Engineering
Imperial College London
London, UK
Dept. of Elec. and Electronic Engineering
Imperial College London
London, UK
murilo@ieee.org
y.demiris@imperial.ac.uk
ABSTRACT
A wide range of architectures have been proposed within the
areas of learning by demonstration and multi-robot coordination. These areas share a common issue: how humans and
robots share information and knowledge among themselves.
This paper analyses the impact of communication between
human teachers during simultaneous demonstration of task
execution in the novel Multi-robot Learning by Demonstration domain, using the MRLbD architecture. The performance is analysed in terms of time to task completion, as
well as the quality of the multi-robot joint action plans.
Participants with different levels of skills taught real robots
solutions for a furniture moving task through teleoperation.
The experimental results provide evidence that explicit communication between teachers does not necessarily reduce the
time to complete a task, but contributes to the synchronisation of manoeuvres, thus enhancing the quality of the joint
action plans generated by the MRLbD architecture.
Categories and Subject Descriptors
I.2.9 [Artificial Intelligence]: Robotics
General Terms
Algorithms, Design, Experimentation
Keywords
Learning by Demonstration, Multi-robot Systems
1.
INTRODUCTION
The extensively studied field of Multi-robot Systems (MRS)
is well-known for its advantages and remarkable applications
when compared to systems consisted of a single robot. MRS
bring benefits such as redundancy, flexibility, robustness and
so forth (for a review on MRS, refer to [14]) to a variety of
domains, including search and rescue, cleanup of hazardous
waste, space exploration, surveillance and the widely recognised RoboCup domain.
Likewise, a wide range of studies have addressed the challenge of developing Learning by Demonstration (LbD) architectures, which system can learn not from expert designers
or programmers, but from observation and imitation of desired behaviour (for a comprehensive survey, see [1]).
However, very little work has been done attempting to
merge LbD and MRS.
The work of [10] presented a novel approach to LbD in
MRS – the Multi-robot Learning by Demonstration (MRLbD)
Figure 1: The P3-AT mobile robots used in the experiments.
architecture. This architecture enabled multiple robots to
learn multi-robot joint action plans by observing the simultaneous execution of desired behaviour demonstrated by
multiple teachers. This was achieved by firstly learning low
level behaviours at single robot level using the HAMMER
architecture [6], and subsequently applying a spectral clustering algorithm to segment group behaviour [16].
Communication is a classic feature, common to the areas of MRS and LbD, which has been addressed by a wide
variety of studies. The performance impact of communication in MRS is well-known [2], [13]. However, a question
that remains open is whether effects of communication between humans when teaching robots are somehow correlated
with the ones in MRS. To the knowledge of the authors, no
prior work has analysed the effects of communication between teachers in the MRLbD domain. This paper presents
an analysis of performance in time to task completion and
quality of the demonstration in a multiple teacher, multiple
robot learning by demonstration scenario, making use of the
MRLbD architecture.
The remainder of this paper is organised as follows: Section 2 provides background information of how communication has been used within the areas of MRS and LbD. In
Section 3, the teleoperation framework, the MRLbD architecture and the robots (pictured in Fig. 1) are explained, followed by Section 4, which presents the real scenario and the
task specification in which the experiments were performed.
Subsequently, Section 5 analyses the results obtained, and
finally Section 6 presents the conclusions and further work.
2.
BACKGROUND INFORMATION
Several studies discuss properties and the nature of communication among humans and robots. In this paper, the
characteristics of communication is categorised into 2 types,
which definition is hereafter discussed. The impact of com-
AAMAS'10 Workshop on Agents Learning Interactively from Human Teachers (2010)
Human-robot
interface
munication in MRS is also discussed in this section.
2.1
Implicit communication
Implicit communication is the information that can be
gathered just by observing the environment, and relies on
the perceptual capabilities of the observer, often involving
computer vision. Implicit communication does not depend
on communication networks or mechanisms. On the other
hand, a mismatch between observed data and the observer’s
underlying capabilities can potentially happen – a common
issue known as the correspondence problem [11].
LbD techniques often make use of implicit communication,
where the behaviour of a group or a single agent (human or
robot) is observed, and predictions of intention are formulated. The group or single agent is not necessarily aware of
the fact that it is being observed. In some cases, humans
pedagogically demonstrate desired behaviour to a robot by
executing a task (e.g., [6]) or teleoperating robots (e.g., [3],
[10]). In other cases a robot observes humans and hypothesises the actions humans may be executing [9].
2.2
Explicit communication
Explicit communication occurs when robots deliberately
exchange internal states by means other than observation.
This type of communication is largely used in MRS as a
means to gather data in order to maximise the utility function of a group. Thus, it is expected that a group of robots
can improve performance by sharing location, sensor data
and so forth. As an example, the work of [17] presented an
approach in which robots could form coalitions with other
robots, sharing locations of robots and objects, as well as
sensor data. Market-based approaches for multi-robot task
allocation also make use of state communication to define
auctions and distribute resources within robots (for a review, refer to [7]).
Within the LbD area, in [5] a teacher could explicitly teach
2 humanoid robots when information should be communicated, and also select the state features that should be exchanged using inter-robot communication. The approach
presented by [12] allowed the teacher to provide the robot
with additional information beyond the observed state by
using verbal instructions, such as TAKE, DROP, START
and DONE. These verbal instructions were used at the same
time the robot learnt to transport objects by following the
teacher. Similarly, the framework presented in [15] enabled
a robot to established a verbal dialog communication (using a pre-defined vocabulary of sentences) to learn how to
perform a guide tour while following the teacher around.
2.3
Impact of Communication in MRS
Previous studies have analysed the effects of communication in MRS. In [2], performance of a simulated group of
reactive-controlled robots was analysed in 3 distinct tasks.
According to the results obtained in that study, explicit communication is not essential for tasks in which implicit communication is available. On the other hand, if the observable
state of the environment is very little, then explicit communication significantly improves the group performance.
In the work of [13], the performance was analysed in regards to time to task completion the results obtained led
to similar conslusions of [2]. If the necessary information
can be gathered by implicit communication, then explicit
communication does not make significant difference in the
Joystick
WiFi
network
Plan Extraction
Visualisation
Action recognition
(HAMMER)
Robot cognitive capabilities
Robot control
Environment
perception
Player
Server
Logging
Group behaviour
segmentation
Robot
hardware
Multirobot plan
Figure 2: Overview of the MRLbD architecture.
time to complete a task. However, if the cost of executing
redundant actions is high, then explicit communication can
potentially reduce the time to task completion.
The results presented in [10] demonstrated that multiple
robots can learn joint action plans from multiple teacher
demonstrations. For those experiments, no explicit communication between the teachers was permitted. This paper
analyses whether the impact in performance caused by communication between teachers is correlated with the findings
of [2] and [13].
3.
PLATFORM FOR EXPERIMENTATION
This Section briefly describes the teleoperation framework
with which the P3-AT robots were controlled during the
experiments, as well as the relevant details regarding the
MRLbD architecture (which block diagram can be seen in
Fig. 2) employed to extract multi-robot joint action plans.
3.1
Teleoperation framework
The teleoperation framework within the MRLbD architecture consists in a client/server software written in C++
to control the P3-AT robots.
The server software comprises the robot cognitive capabilities and resides on the robot’s onboard computer. The
server is responsible for acquiring the sensor data and sending motor commands to the robot, whereas the client software runs on a remote computer and serves as the interface
between the human operator and the robot.
The P3-AT mobile robots – Each robot is equipped
with onboard computers, and its sensing capabilities comprise a laser range scanner model Sick LMS-200 with field
of view of 180 ◦ and centimetre accuracy, a firewire camera
providing coloured images with 320x240 pixels at 30 frames
per second, and 16 sonar range sensors with field of view
of 30 ◦ installed in a ring configuration around the robot.
In addition, odometry sensors installed on the robot provide feedback of robot pose (2D coordinates and orientation
relative to a starting point).
The server software – Within the Robot cognitive capabilities block (Fig. 2), the server interfaces with the robot
hardware by means of the robot control interface Player [8].
Initially, sensor data is requested to the robot and then
processed. Laser data is combined with robot odometry values to build an incremental 2D map of the environment.
Additionally, the images captured from the camera are used
for object recognition and pose estimation, and also compressed in JPEG format to be sent over the Wi-Fi network
to the client software. Also, the sonar data is used for obstacle detection which disables the motors in case a specific
AAMAS'10 Workshop on Agents Learning Interactively from Human Teachers (2010)
M1
I1
M2
I2
In
Figure 3: A screenshot of the human-robot interface; camera is on top-left, 2D map is on top-right.
motor command would result in crashing into a not recognised object. Besides, joystick inputs representing motor
commands are constantly received from the client.
The data manipulated by the server is incrementally stored
in a log file every 0.5 seconds, comprising a series of observations made by the robot during task execution.
The client software – Within the Human-robot interface block (Fig. 2), the sensor data received from the server is
displayed to the human demonstrator . This data consists in
the robot’s onboard camera, laser and sonar range scanners,
odometry values, battery level and Wi-Fi signal strength. A
screenshot of this interface can be seen in Fig. 3. In order
to aid in teleoperating the robots, thicker line segments are
extracted from laser data, and recognised objects are represented by specific geometric shapes and colours (e.g., when
other robots are recognised, a blue ellipse is displayed on
screen, representing the observed robot pose). In addition,
an incremental 2D map of the environment is built using
laser and odometry data, map which can be substantially
useful for location awareness during navigation.
In the meantime, the robot is teleoperated by the demonstrator through a joystick.
3.2
The MRLbD Architecture
It is worth noticing that the MRLbD architecture does not
necessarily require the aforementioned teleoperation framework; it can be integrated in any MRS in which robots are
capable of generating appropriate observations of the state
of the environment along time.
The learning module of the MRLbD architecture is responsible for extracting a joint action plan for each set of
simultaneous demonstrated data provided by multiple human teachers. Within the Plan Extraction block, 3 distinct
stages, hereafter discussed, process the demonstrated data.
Action recognition at single robot level – When
addressing the problem of recognising observed actions, a
mismatch between observed data and robot internal states
might happen. This is a common issue known as the correspondence problem [11].
The MRLbD architecture embeds a prediction of intent
approach – the HAMMER architecture [6] – to recognise actions from observed data and manoeuvre commands, which
is based upon the concepts of multiple hierarchically connected inverse-forward models. As illustrated in Fig. 4, each
inverse-forward model pair is a hypothesis of a primitive behaviour and the predicted state is compared to the observed
state to compute a confidence value. This value represents
P1
Prediction
verification
(at t+1)
P2
F2
Mn
Prediction
verification
(at t+1)
…
…
state s
(at t)
F1
Fn
Pn
Prediction
verification
(at t+1)
Figure 4: Diagramatic statement of the HAMMER
architecture. In state st , inverse models (I1 to In )
compute motor commands (M1 to Mn ), with which
forward models (F1 to Fn ) form predictions of the
next state st+1 (P1 to Pn ), then verified at st+1 .
how correct that hypothesis is, thus determining the robot
primitive behaviour which would result in the most similar
outcome to the observed action.
As in [10], this paper makes use of five inverse-forward
model pairs, which are: Idle (the robot remains stationary),
Search (the robot searches for a specific object), Approach
(the robot approaches the object once it is found), Push (the
robot moves the object to a destination area) and Dock (the
robot navigates back to its initial location). While the inverse models are hand-coded primitive behaviours, forward
models take into account the differential drive kinematics
model of the robot.
Group behaviour segmentation – A Matlab implementation of the spectral clustering algorithm presented in
[16] is used for spatio-temporal segmentation of group behaviour based on the observations made by the robots during
task demonstration.
The current work makes use of the same parameters described in [10] for the clustering algorithm. These values
were thoroughly defined to preserve the generality of the algorithm when applied to different sets of concurrent demonstrated data.
In addition, the same 2 events used in [10] were used in this
implementation. The event SEESBOX occurs every time
the robot recognises a known object within its field of view.
Also, the event SEESROBOT occurs when the robot recognises another robot.
Multi-robot plan generation – In the MRLbD, the
segmented group behaviour is combined with the recognised
actions at single robot level to build multi-robot action plans.
This plan comprises a sequence of actions at single robot
level, with specific times to start and end action execution,
that each robot should perform along time as an attempt
to achieve the same goals of the task execution formerly
demonstrated.
Given the unlabelled group behaviour segmentation over
time for each robot, the recognised actions at single robot
level are ranked based upon the forward model predictions
and start/end times of that particular segment of group behaviour. For every robot and every cluster, a confidence
level of the inverse-forward model is calculated. The action
with highest confidence level is then selected, and a sequence
of actions at individual level for each robot are generated,
defining the multi-robot joint action plan.
4.
EXPERIMENTAL TESTS
Experiments were performed in a furniture movers realistic scenario. This experiment differs from the one presented
AAMAS'10 Workshop on Agents Learning Interactively from Human Teachers (2010)
Comparison of task time to completion
1100
1000
without communication
with communication
900
time (secs.)
800
700
600
500
400
300
200
100
0
1
2
3
4
5
trial
Figure 5: Time-based comparison of the demonstrated task executions, with (green bars) and without (blue bars) verbal communication allowed between participants.
in [4] in the sense that demonstrators have full control of the
robot through the joystick, opposed to the selection of predefined actions comprising the robots’ underlying capabilities. Moreover, in this work mobile robots P3-AT were used
in a real environment. In comparison with the experiments
performed in [10], the current experiments make use of a
larger box, and robots must move the box through a tighter,
cluttered environment, demanding significantly more difficult coordinated manoeuvres to complete the task.
A total of 10 demonstrations were conducted, involving
7 participants: 3 novices, 2 intermediate and 2 experienced
users of the teleoperation framework1 . The same instructions were thoroughly given to all the 7 demonstrators, including a training session of 10 minutes for familiarisation
with the teleoperation framework. In pairs, the demonstrators were asked to remotely control 2 robots and search for
a particular box, which location was initially unknown, and
bring it to a specified destination, resulting in the task completion. In order to analyse the impact of explicit communication between participants during task execution, the same
pairs of participants performed 2 task executions (comprising one trial): one demonstration with implicit communication only, and another demonstration with verbal communication permitted.
An experienced and an intermediate users performed the
first trial, then 2 experienced users demonstrated the second
and subsequently 2 novices deployed the third trial. The
fourth trial was also executed by 2 novices and lastly an
experienced and an intermediate users carried out the task
demonstration.
5.
RESULTS AND DISCUSSION
This Section analyses the impact of explicit communication between teachers in the joint action plans extracted
by the MRLbD architecture, discussing the results obtained
from the 10 demonstrations of task execution in regards to
time to completion of the task, as well as quality of the
multi-robot joint action plan generated.
5.1
Time to completion of task demonstrations
From the results obtained using the MRLbD architecture,
1
A novice and the 2 advanced participants performed the
experiments twice
Fig. 5 shows the time to completion of the 10 task executions, where each trial represents 2 demonstrations from the
same pair of participants, with and without explicit, namely,
verbal communication allowed between participants.
A comparative analysis of the trials in a time basis provides no evidence of performance gains, which is consistent
with the discussion presented in Section 2.3. In fact, no
significant difference can be observed.
On one hand, explicit communication can cause delays, as
observed in trials 2 (13 secs.) and 4 (22 secs.) in Fig. 5. Delays could presumably be a consequence of participants naturally spending some time communicating with each other
in order to synchronise and coordinate manoeuvres.
On the other hand, trials 1 and 5 presented a slight reduction of time demanded (30 secs. and 19 secs.). Recalling
the discussion in Section 2.3, implicit communication not always provide enough information when the environment is
partially observable. That is, sometimes a robot might lose
track of its robot mate or the object being manipulated,
thus requiring manoeuvres to look around and observe its
surroundings. In which case, one robot can provide the other
robot with the relevant information by explicitly communicating its observation, avoiding unnecessary manoeuvres.
From trial 3, however, an expressive reduction in time to
completion of the task can be observed (4 min. and 11 secs.).
Unexpectedly, this trial provided an excellent example of
how group behaviour performance can benefit from explicit
communication.
In this particular trial, during the demonstration using
implicit communication, both participants decided to navigate towards the direction opposite to where the box was
initially located (Fig. 6(a)), thus demanding more time for
the box to be found by both participants. Searching for the
box does not require collaboration between robots, but it is
a classical example of a redundant action.
Predictably, the participants benefited from the explicit
communication: one participant quickly found the box and
immediately shared that information, telling the other participant the box location (Fig. 6(b)), hence reducing the time
for task completion.
Interestingly, whether the participants demonstrating the
task were experienced users or novices, the impact of allowing explicit communication between teachers did not result
in changes in time to completion of the task whatsoever.
5.2
Quality of Joint Action Plans Generated
It is widely recognised that developing systems to work
in real world scenarios markedly elicits real world issues
which must be taken into account, such as noise and the
non-determinism of the environment. Furthermore, when
developing systems in which robots learn from humans, the
demonstrations of desired behaviour are potentially unlikely
to result in exactly the same data.
Therefore, the multi-robot joint action plans generated by
the MRLbD architecture are expected to present variation,
mainly in the segmentation of group behaviour, where robot
trajectories along time are taken into account.
Fig. 7(a) plots the joint action plan generated by processing the data demonstrated with implicit communication
only, from trial 1. In that particular demonstration, a lack
of coordination, showing the manoeuvres were mostly out
of synchrony, can be observed. While robot 1 was already
pushing the object (denoted by the character “P” within the
AAMAS'10 Workshop on Agents Learning Interactively from Human Teachers (2010)
Spatio−temporal view
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time
Spatio−temporal view
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Robot found
the box
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take wrong
direction
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Robots’ initial location
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occurred
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−15
y coord
(a) No communication allowed: both robots navigated opposite to box location
0
−5
x coord
(b) Communication allowed: box location information communicated to other robot
Figure 6: Robot trajectories along time from trial 3.
Robot actions timeline
Robot actions timeline
A
A DDA
P
D
1
S
P
P
A
D
2 S
A
I
A P
A
P
1S
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robot
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robot
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300
time
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(a) Implicit communication only
0
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Figure 7: Joint action plans generated using demonstrations from trial 1.
Robot actions timeline
A
P
1
S
P
P
P
A
D
P
2
S
P
P
P P
P
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robot
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robot
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Robot actions timeline
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(a) Implicit communication only
1 S
P
P
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Figure 8: Joint action plans generated using demonstrations from trial 2.
second action on the timeline), robot 2 was still approximating (“A”) to the object. Only at the fourth action the robot
1 realised that robot 2 was not in synchrony with its manoeuvres. This lack of coordination caused the HAMMER
architecture to recognise the fourth and fifth individual actions of robot 2 as Dock. At that stage, robot 2 was moving
towards the place it was initially located, and presumably
lost track of the object, leading the confidence level of the
Dock action to be higher.
Conversely, the joint action plan extracted from the demonstration with explicit communication in trial 1 indicates rea-
sonably synchronised actions, as can be seen in Fig. 7(b).
Again, robot 1 started pushing the object first, while robot
2 was still approaching the object (second actions on the
timeline). The participant teleoperating robot 1 was possibly notified by the other participant, which initiated a period
in which the robots remained stationary (Idle action denoted
by “I”). The remainder of the actions were hence coordinative executed until task completion, thus suggesting that
explicit communication can contribute to the MRLbD architecture to generate better coordinated joint action plans.
Fig. 8 also provides evidence that explicit communication
AAMAS'10 Workshop on Agents Learning Interactively from Human Teachers (2010)
contributes to extraction of better joint action plans. In trial
2 both participants were experienced users. Therefore, the
result from the demonstration with implicit communication
only (Fig. 8(a)) already presents a considerably coordinated,
noticeably synchronised joint action plan.
However, this synchrony is lost at the fifth action on the
timeline, which was quickly re-established by the robot 2,
presumably approaching the object rapidly, and then engaging in the object moving. In contrast with Fig. 8(a), the
Fig. 8(b) reveals a plan in which actions are jointly executed,
tightly synchronised in time, when explicit communication
was permitted between the experienced teachers.
These results indicate that the use of explicit communication aids human teachers regardless their level of skills,
improving the quality of the demonstration, quality which
still relies on the level of skills.
6.
CONCLUSION AND FUTURE WORK
This paper presented an analysis of the impact of communication between humans concurrently teaching multiple
robots using the novel MRLbD architecture proposed in [10].
It was found that the impact in the MRLbD system performance when using explicit communication is strongly correlated to the impact in MRS. What is more, the results
noticeably provide evidence that explicit communication affects human teachers at the same extent, regardless the level
of skills of these teachers.
Regarding the time to task completion, explicit communication was found to result in no substantial alteration in
performance. Explicit communication can only be beneficial
to reduce the time to task completion if the cost (e.g., in time
or battery life) of executing redundant actions is high.
However, the quality of the joint action plans was shown to
satisfactorily benefit from explicit communication. It was revealed that those plans presented tighter coordinated, temporally synchronised actions executed by the robots.
Ongoing work is on refining the inverse-forward model
pairs of the HAMMER architecture for action recognition
and investigating the use of other events within the spectral
clustering algorithm.
7.
ACKNOWLEDGEMENTS
The support of the CAPES Foundation, Brazil, under the
project No. 5031/06–0, is gratefully acknowledged.
8.
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