=Paper=
{{Paper
|id=Vol-1484/paper14
|storemode=property
|title=Skill-based Exception Handling and Error Recovery for Collaborative Industrial Robots
|pdfUrl=https://ceur-ws.org/Vol-1484/paper14.pdf
|volume=Vol-1484
|dblpUrl=https://dblp.org/rec/conf/iros/BeckSFNK15
}}
==Skill-based Exception Handling and Error Recovery for Collaborative Industrial Robots==
https://ceur-ws.org/Vol-1484/paper14.pdf
Skill-based Exception Handling and Error Recovery
for Collaborative Industrial Robots
A. B. Beck, A. D. Schwartz, A. R. Fugl, M. Naumann, B. Kahl
Abstract— Moving robots from their carefully designed and recovery approach that allows non-robot expert users to
encapsulated work cells into the open, less structured human operate a robotic system embedded in a human-centric
workspace for collaboration with workers requires robust workspace. We briefly introduce our execution model, detail
error detection and recovery strategies. Foreseeing all possible the Extended Markov Chain based Situation Awareness,
uncertainties and unexpected events and to program in which forms the base for Exception Handling, and the Error
recovery actions at setup time is unfeasible. Online learning of Recovery module employing a Bayesian network and beta-
nominal execution behaviour and automatic detection of binomial inference algorithm. The prosed system has been
anomalies using an Extended Markov Model, combined with implemented in a pick & place and in an assembly work cell,
interactively trained Bayesian networks for mapping which are finally presented.
anomalies to error causes and recovery actions, enables
automatic recovery from previously experienced errors. A II. RELATED WORK
three-layered user-friendly model of errors—causes—
responses and a simple GUI allows non-expert user to define Research in exception handling is related to the area of error
new recovery activities and error causes when not yet handled or fault recovery [17]. Error recovery has been defined as
anomalies occur. “the process by which the system returns to a state where
production can restart after an abnormal and disruptive
I. MOTIVATION
condition has occurred” [23]. For a robot coworker to
Today’s robot systems for industrial applications rely on effectively handle an exception, whether through informing
a structured environment to avoid errors. Parts, fixtures, the human worker or resolving the problem by itself, the
tools and stations have defined positions and the workspace types of faults that typically occur in the manufacturing
is encapsulated to avoid intruders that could possibly robotic assembly cases needs to be understood. Fault
endanger this defined environment. Expected exceptions taxonomies have been presented in other related fields,
from the nominal case that were either foreseen during the including mobile robots [7], computing [3], autonomous
planning of the robot system, or occurred during the setup robots in RoboCup [21], workflow systems [16], service-
phase of the system are coped with by integrating additional oriented architecture [6], and web service [8]. Reports show
sensors, adapting tool-, fixture and part geometries and that many errors in manufacturing systems, including CNC
adding additional branches to the robot program to cope with machines, are hardware related and that approximately 60%
these deviations. Furthermore, as many robotic systems are
of all stoppages are due to tool breakdown [23]. However,
complicated, any exceptions and breakdowns occurring after
system setup often require external technicians or engineers there has been a lack of study on the likelihood of common
to diagnose and solve problems. errors and exceptions occurring during assembly tasks
involving collaborative robots. One of the reasons can be
Such strictly controlled and carefully designed work that robot coworkers have not yet proven to be robust
cells are only economically feasible if the designed robot enough for industry application to be studied and
system will run unobstructed for a long time. Small and mid- generalized based on real assembly cases [14].
sized enterprises (SMEs) are often characterized by a much
more agile production style and consequently rely on human III. SKILL-BASED EXECUTION MODEL
workspaces. Moving robots out of their strictly controlled At the base of the system is a Skill Execution Engine,
and carefully designed spaces into human workspaces, which allows a more goal-oriented task description than
which are by nature unstructured environments with a high strict motion based programming or planning. Without
degree of uncertainty, requires significantly enhanced going into details of the skill-model [1], we assume skills to
robustness towards unforeseen events and geometric or be independent, sensor-based motion or handling primitives
other uncertainties. (The additional need for safety measures that adapt themselves to position uncertainties and other
to protect the human co-worker from injuries is out of scope deviations from an ideal state using build-in sensing and
of this work, see e.g. [22], [12] and many others.) A SME monitoring as well as (limited) internal error recovery.
suitable robot system therefore needs semi automatic Robot tasks are constructed by chaining skills and control
exception handling and error recovery capabilities that allow flow instructions, forming a state machine [2] based on
non-expert users to manage exceptions (internally and SCXML1. While skills detect deviations from their expected
externally triggered) occurring in daily operation. We performance and report these, the skill executor by itself
propose a novel skill-based exception handling and error does not provide any error recovery functionality. Features
* The research leading to these results has been funded by the European N. Naumann is with the Fraunhofer Institute for Production Systems
Union’s seventh framework program (FP7/2007-2013) under grant and Automation. E-mail: Martin.Naumann@ipa.fraunhofer.de
agreements #608604 (LIAA: Lean Intelligent Assembly Automation) and B. Kahl is with the Gesellschaft für Produktionssysteme GmbH
#287787 (SMErobotics: The European Robotics Initiative for Stuttgart. E-mail: bjoern.kahl@gps-stuttgart.de
Strengthening the Competitiveness of SMEs in Manufacturing by
1
integrating aspects of cognitive systems). Apache Commons SCXML executor,
A. B. Beck and A. R. Fugl are with the Danish Technology Institute. E- http://commons.apache.org/proper/commons-scxml/.
mail: anbb@dti.dk and arf@dti.dk
FinE-R 2015 Page 5 IROS 2015, Hamburg - Germany
The path to success: Failures in Real Robots October 2, 2015
of the skill executor that allow the implementation of error A. Situation Model
recovery functionality at higher layers are: Situation Assessment uses a Situation Model as a
The skill executor knows and publishes the current template description to fuse together the different data points
state of the system at any time. This allows an error for learning a Situation. The components of the Situation
recovery module to relate errors on the one hand to Model (𝑑𝑖 in (2)) are real number data, which can come from
specific skill models and on the other hand to any source and have any meaning. In our experience,
specific application steps and therefore to draw combining space and time is critical to the success of
conclusions like “this is an error that is very typical learning a skill. For instance, learning a skill using a 6D F/T
for a pick operation” or “this is an error that sensor, the Situation Model 𝑠 could be defined as in (3).
occurred already in the past at this specific 𝑠𝑎 = [𝑝𝑟𝑖𝑚𝑖𝑡𝑖𝑣𝑒, 𝐹x , 𝐹y , 𝐹𝑧 , 𝑇𝑥 , 𝑇𝑦 , 𝑇𝑧] (3)
execution step of the application”.
The component 𝑝𝑟𝑖𝑚𝑖𝑡𝑖𝑣𝑒 of 𝑠a is a data point that
The skill executor has an interface for an error uniquely identifies the current primitive being executed in
recovery module to stop and later continue the the skill. In this case, the unique primitive ID provides the
execution of the skill based application program understanding of time while the understanding of space is
thereby allowing worker interaction to recover from provided by the F/T data. By using the primitive ID we can
errors detected by an error recovery module. learn a skill time invariantly. This means that SA will only
The skill formalism used by the skill executor is learn the sequence of the events and is invariant towards the
built on the concept of reusable hierarchical skills duration of the execution of specific primitives. We have
that are easy to enhance or adapt. It is therefore found this feature particularly useful when the duration of
easily possible to include additional mechanisms the primitives or skills is stochastic. Should it be necessary
into an existing skill model to cope with errors that to catch anomalies in relation to when events occur (e.g. too
could be detected by the system but just have not early or late), the primitive ID in (3) can be substituted with
been considered yet. a time data point. Throughout our research, we have
successfully applied SA to monitoring digital inputs, such as
The Situation Assessment (SA) constantly monitors the the state of one or more grippers. Through the rest of the
overall situation (robot task execution) using data published paper, we will use the following Situation Model for
by the skills executor as well as by additional sensors implementation and testing:
dedicated for situation assessment. Deviations flagged by
the SA are further examined by the Exception Handling 𝑠𝑏 = [𝑝𝑟𝑖𝑚𝑖𝑡𝑖𝑣𝑒, 𝑔𝑟𝑖𝑝𝑝𝑒𝑟𝑜𝑝en, 𝑔𝑟𝑖𝑝𝑝𝑒𝑟𝑐𝑙𝑜𝑠𝑒𝑑] (4)
(EH), which devises a possible cause and corrective where 𝑔𝑟𝑖𝑝𝑝𝑒𝑟𝑜𝑝𝑒𝑛 and 𝑔𝑟𝑖𝑝𝑝𝑒𝑟𝑐𝑙𝑜𝑠𝑒𝑑 are binary outputs of
measure, potentially involving user interaction. The whole reed switches of the gripper: 𝑔𝑟𝑖𝑝𝑝𝑒𝑟𝑜𝑝𝑒𝑛 is 𝑡𝑟𝑢𝑒 when the
system of skill executer, situation assessment and exception gripper is fully open and 𝑔𝑟𝑖𝑝𝑝𝑒𝑟𝑐𝑙𝑜𝑠𝑒𝑑 is 𝑡𝑟𝑢𝑒 when the
handling is collectively referred to as “Exception Handling gripper is fully closed. Our assumption is that the gripper is
Framework” or “EHF”. grasping an object when both readings are 𝑓𝑎𝑙𝑠𝑒, indicating
IV. SITUATION ASSESSMENT the gripper is neither fully open nor closed.
The role of Situation Assessment (SA) is to learn and B. Data Processing and Clustering
monitor the (correct) skill execution and detect non-nominal The Situation Model serves as a template describing
conditions. Deviations from the learned, nominal behaviour which sensors SA should fuse together into one single state.
are interpreted as Anomalies, which are passed on to the In general, all data points in the Situation Model have to be
Exception Handler (section V). Our implementation of SA real numbers. This allows the computation of one single
is based on prior work by [4] and [5], where SA was applied metric for each 𝑋𝑖 in (1). We have so far used the Jaccard
to mobile robotics. We implemented and expanded SA to similarity coefficient as a method for clustering similar
learn skill based execution in a collaborative robotic system. states. Through experimentation, we have found the
To learn how to perform a skill correctly, SA captures the algorithm to be useful despite its simplicity.
essence of the skill by learning the timing and sequence of
events that make up the skill. Our approach is to generate C. Dynamic Learning in Situation Assessment
one parameterized model that includes parameters in the SA can autonomously learn a skill without the user
space and time domain. SA learns the sequence of events having to manually specify the states of a skill. We have
within a skill execution by learning a set of parameters with implemented a spatiotemporal model that allows for online
a temporal component, recording the transition from one dynamic learning of states over time. For this purpose, we
instantiation of the parameters to the next: are currently using the Extensible Markov Model (EMM) as
it is useful for online learning of sequences of states [10]. An
p(𝑋) = 𝑝(𝑋1, 𝑋2, … , 𝑋𝑛) (1) example of a dynamically learned model using the EMM
where 𝑋, a Situation Model, denotes a set of parameters (a algorithm can be seen in Figure 1. In this example, a robot is
state), 𝑛 denotes a discrete step in time, and 𝑝, a Situation, picking up a nut from a table and placing it on a pipe in a
denotes the complete distribution of all the states within a single nonrecurring operation (therefore an open-ended
skill. Each state 𝑋𝑖 of 𝑋 is parameterized: chain). The EMM is also useful in learning looped tasks.
𝑋 = [𝑑1, 𝑑2, … , 𝑑𝑚] (2)
where 𝑑𝑖 are data components such as sensor values or
robot’s internal state values.
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The path to success: Failures in Real Robots October 2, 2015
an error. The Fault node models the root cause of the
Anomaly and the Response node models the solution to the
Fault. This model resembles the diagnosis model used by
physicians when examining a patient: Based on symptoms
(here: the detected error) an illness is inferred (here the
fault) and a therapy decided (here the response). The
intermediate step of a fault is necessary, since one and the
same observed error (symptom) can have multiple causes.
For example an unexpected gripper state can be due to a
failed grasping operation, a missing object at the pickup
position or a defective gripper itself.
B. Bayesian Network
Figure 1. Example of learning a task. The upper left corner shows the
temporal sequence of states the skill consists of. These states were
learned online while the robot performed the task. A full video is
available at https://www.youtube.com/watch?v=-CKbdQ3ocQo.
D. Anomaly detection
SA has two modes of operation: learning and detection
during execution. In the learning mode, SA monitors the
data points specified in the Situation Model and builds the
Situation for the skill that is being learned. During execution
of the same skill, SA loads the saved Situation and applies
the same clustering process as during learning. However,
should the clustering of the data result in a new state in (1),
then SA will interpret that as an anomalous state has
occurred and issue an Anomaly warning. Processing and
handling the Anomaly is the task of the Exception Handler
Figure. 2. Inference of cause and solution to Gripper Open anomaly.
(EH) module. Both error nodes are dependent on both anomaly nodes, allowing the
V. EXCEPTION HANDLER Bayesian network to further strengthen the belief about the cause of
an anomaly. Simulated in GeNIe1.
The task of EH is to receive an Anomaly from SA and
provide a suggested solution that is most likely to solve the Figure 2 is an example of a Bayesian network with three
problem. For each robotic system, EH maintains a Exception Scenarios for the two Anomaly nodes of the
hierarchical four-layered Bayesian network with all
sensors 𝑔𝑟𝑖𝑝𝑝𝑒𝑟𝑜𝑝𝑒𝑛 and 𝑔𝑟𝑖𝑝𝑝𝑒𝑟𝑐𝑙𝑜𝑠𝑒𝑑 in (4). Both Error
exceptions and solutions relevant to that cell. The
nodes are dependent on both Anomaly nodes, allowing the
hierarchical structure allows EH to reason about the most
Bayesian network to further strengthen the belief about the
suitable solution to a problem. EH provides the suggested
cause of an Anomaly (simulated in GeNIe2). The first two
solution to the user along with all other possible solutions.
scenarios with nodes 𝑠1 = {1,3,5,8} and 𝑠2 = {1,3,6,9}, offer
The user is free to select the suggested solution, any other
two Responses to the Gripper Open Anomaly while the third
solution or to create a new solution. The selection is stored
scenario 𝑠3 = {2,4,7,10}, offers a single Response to a
in EH as a sample of user solution preference. Such samples
Gripper Closed Anomaly. The numbers in curly braces
are used in priming the network for inference with future
indicate the node number in Figure 2. In this example we are
anomalies. With the feedback of user samples, a closed
modelling two faults {5,6} and Responses {8, 9} for the
preference-learning loop is formed to provide suggestions
Gripper Open Anomaly. If a gripper is unexpectedly open
for solutions to future anomalies. In this section, we provide
(Gripper Open = true, Gripper Closed = false), we could
a detailed description of EH and begin with the role of the
interpret that as either a pneumatics failure (e.g. loss of air
Exception Scenario ES in EH.
pressure) that can be solved by checking and replacing the
A. Exception Scenario air supply {5,8}, or an actuator failure (e.g. broken gripper)
The Exception Scenario (ES) is designed as a four- that can be solved by repairing the gripper {6,9}. In the
layered model consisting of Anomaly, Error, Fault and reverse case of a closed gripper, we could interpret the
Response, inspired by work in [18]. The hierarchy is a four- failure as there was no object to grip and the solution is
layered binary Bayesian network that facilitates inferring the simply to replace the missing object. In Figure 2, the Faults
most likely Response (solution) to an Anomaly (a {5,6} are modelled as belonging to the same Error, Gripper
deviation), an example is shown in Figure 2. At the lowest Operations Error {3}. This allows the network to learn user
level of the network, Anomaly nodes model anomalies selections for a specific Fault, Response pair over other pairs
detected by SA. Each Anomaly node corresponds to a data belonging to the same Error node. The network is thereby
component (di of (2)) in the Situation Model. Above able to encode knowledge specific to individual user
Anomaly, the Error node models which kind of error the environments.
Anomaly is and if the Anomaly should even be considered
2
Figure 2 shows a screen capture of GeNIe, a Bayesian modelling
environment developed by the Decision Systems Laboratory of the
University of Pittsburgh. Available at http://genie.sis.pitt.edu
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The path to success: Failures in Real Robots October 2, 2015
confirming and rejecting the selection of the node. The Beta
distribution is a conjugate distribution to the Binomial
C. Inference distribution, thereby offering analytical tractability of the
The process of inferring a Response to an Anomaly in Bayesian inference process. The conjugate property ensures
the Bayesian network, is the inference process of the EH. that when updating the prior Beta distribution (7) with new
This process is an implementation of Bayes’ theorem: evidence following the Binomial distribution, the resulting
posterior distribution is also a Beta distribution (8).
𝑝𝑜𝑠𝑡𝑒𝑟𝑖𝑜𝑟 ∝ 𝑝𝑟𝑖𝑜𝑟 ∙ 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 (5)
We have implemented Bayes’ theorem in three steps: (𝑝 | 𝛼∗, 𝛽∗) =Be(𝛼∗, 𝛽∗) (8)
𝛼∗ and 𝛽∗ is respectively the new number of selections
Calculate prior probabilities
and rejections for the specific node. Thus, obtaining the
Introduce evidence to network posterior distribution in (8) becomes simply a matter of
adding new confirmations to the existing, and then
Infer posterior probabilities calculating the mean (𝜇) and variance (𝑣𝑎𝑟) (9,10).
In the following, we describe each of these steps.
D. Inference
A prerequisite for performing inference is the calculation
of prior probabilities. As described in section IV, a feature
of the EHF is to learn the user-preferred solution of a given In Figure 3, examples of Beta distributions for different
anomaly. When the user selects a specific Exception values of 𝛼 and 𝛽 are shown. Distribution 1: 𝐵e(𝛼 = 1, 𝛽 =
Scenario (i.e. an Error, a Cause and a Solution) to solve a 1) is a uniform distribution offering an uninformative prior
problem, it is fed back to the database as a sample of the user with a mean, 𝜇 = 0.5 and a high variance (uncertainty) due
selection, thus learning the preference of selecting this to the low sample size. In this case, the posterior will largely
Exception Scenario for a specific Anomaly. The sample data be determined by the data. Distribution 4: 𝐵e(𝛼 = 30, 𝛽 = 5)
is used to calculate the prior probability for each node of the has 𝜇 = 0.86 and a smaller variance, thus providing a
network. We treat calculating the node’s prior probability as comparably less uncertain estimate of the user selection
an inference process that adds another layer of Bayesian preference, 𝑝. The sequence of graphs 1-4 in Figure 3, can
inference as described in (5). We introduce the sample data be seen as an example of a continuous learning cycle,
from user selection of Exception Scenarios as the evidence starting with no knowledge of user selection (a uniform
to infer each node’s posterior probability. Each node of the
Bayesian network is a binary random variable modelling an
event that either occurs or not. For instance, if the user
selects the ES {1,3,5,8} in Fig. 2, then the user is confirming
that the specific ES solved the problem (e.g. that a Gripper
Open Anomaly did happen, it was caused by missing air
pressure and the solution was to resupply the air). At the
same time and equally important, the user is also confirming
that alternative events {4,6} to ES {1,3,5,8} did not occur.
Thus, with every selection of an ES, EH registers the
confirmed nodes on all levels of the ES, as well as the
rejected nodes. The process of selecting any node in the
Bayesian network over time, can be viewed as a Bernoulli Figure 3. Four 𝐵𝑒(𝛼, 𝛽) distributions for different values of 𝛼, 𝛽.
process following a binomial distribution as in (6). Note that 𝛼, 𝛽 > 0, thus 𝛼 = 𝛽 = 1 is equal to no samples. 1: 𝐵𝑒(1,1), 𝜇
= 0.50, 𝑣𝑎𝑟 = 0.083. 2: 𝐵𝑒(2,1), 𝜇 = 0.67, 𝑣𝑎𝑟 = 0.056. 3: 𝐵𝑒(15,5), 𝜇
𝑋~Binom(𝑛, 𝑝) (6)
= 0.75, 𝑣𝑎𝑟 = 0.0089. 4: 𝐵𝑒(30,5), 𝜇 = 0.86, 𝑣𝑎𝑟 = 0.0034.
where 𝑋 is the number of times a specific node has been
selected. 𝑛 is the number of samples drawn in the sequence. distribution with no samples) towards more informative
If this process is sampled sufficiently, a distribution distributions 2-4 as the sample size increases. When a new
reflecting the user selection can be inferred from the sample node is created with no samples available (𝛼 = 𝛽 = 1), the
set. However, in many cases it is not possible to provide a Beta distribution is uniform. However, to avoid the
sample set of sufficient size and inference will be subject to uninformative uniform distribution we propose to query the
uncertainty. To model this uncertainty, we model the user user to provide a subjective estimate of the selection (the
selection for each node as a hyper-parameter 𝑝, thereby mean) of this node along with a confidence level (the
modelling the user selection as a random variable itself and variance). Using the equations for the mean (9) and variance
creating a hierarchical Bayesian model for calculating the (10), suitable values for 𝛼 and 𝛽 can then be calculated.
prior probability [11]. This approach uses the samples of
user selection as a likelihood function providing evidence to E. Introducing evidence
the inference process. Given the binomial likelihood, we The Bayesian network described in section V.B and Fig.
have chosen the Beta distribution as the prior distribution 2 receives evidence in the form of Anomaly information
(7). gathered by SA. In the example shown in Figure 2, SA has
detected that the gripper was unexpectedly fully open (thus
(𝑝 | 𝛼, 𝛽) = Be(𝛼, 𝛽) (7) providing evidence that Gripper Open = true, Gripper
In (7), the user selection is modelled as the Closed = false. The evidence is in practice introduced to the
hyperparameter 𝑝, drawing samples from the Beta network by clamping the two nodes to their respective
distribution. 𝛼 and 𝛽 is respectively the number of samples values.
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The path to success: Failures in Real Robots October 2, 2015
F. Posterior probabilities
After introducing evidence, posterior probabilities for all
nodes are calculated. We have used the SMILE reasoning
engine [9] for inference. The Response having the highest
posterior probability is selected as the suggested solution.
For each Response, the tree is descended towards the root
Anomaly nodes, thus mapping out each possible path
towards the root. The resulting list will have the most
probable ES listed first with all other less likely alternative
ES following in descending probability.
VI. USER INTERFACE FOR ERROR RECOVERY
While section V discussed the inner working of the
actual mapping process, we focus on a more user-centric
Figure 5 Adding a new error cause or fault to the system.
view in this section.
Whenever an anomaly is detected, the error layer Anomaly. Hereafter, we introduced the GO Anomaly
classifies it into an error cause. If no cause is found the user repeatedly, selecting the RA Response as the solution each
is inquired and given the option to assign an existing cause, time. This process was repeated until EH started to suggest
dismiss the anomaly as not indicating an error or to create a the RA Response, thus demonstrating EHF’s ability to learn
new cause (including a resolution, if known). Figure 4 shows the user preference of selecting the RA Response over the
the dialog box after successfully mapping an anomaly to an RP.
error and further to a recovery action. The user can accept
Test results are shown in Fig. 6. Initially, EH has five
samples confirming the selection of the RP Response for the
Figure 6. Posterior probabilities for solution nodes Replace
Pneumatics (RP) and Repair Actuator (RA) to a Gripper Open
Anomaly. The solid line represents the posterior probability of the
Figure 4 The system identified an error including a recovery action.
RP and the dotted line represents posterior probability of RA. For
In case of misclassification the user can add a new exception or
one sample of RP, it takes EH two samples of RA to learn the user
dismiss the anomaly as not indication an error (button “Continue
preference of selecting RA.
Learning”).
this solution or add a new solution. Figure 5 shows the GO Anomaly. Thus, when the GO Anomaly is introduced,
corresponding dialog box for adding a new triplet of error, EH suggests RP as the most suitable Response to the GO
error cause and recovery action. The dialog boxes shown in Anomaly with probability ~ 0.553. However, the user
Fig. 4 and 5 are designed for use at system runtime and ignores the EH suggested RP Response and instead selects
therefore as simplistic as possible. A more elaborated RA. Thus, when the GO Anomaly is introduced again, EH
interface for managing the entire network of anomalies, now has six samples (five for RA and one for RP),
errors, causes and recovery actions is also provided and computing the most likely Response to be RP with
targeted at specifically trained users that setup a new robot probability ~ 0.545, and so on. At RA sample 5, EH
application. computes the probability for each Response being identical
(~ 0.526). Again, the GO Anomaly is introduced and this
VII. EXPERIMENTAL EVALUATION time EH suggests the RA Response with posterior
probability ~ 0.535. Thus, with five samples confirming RP,
Within the scope of SMErobotics, this framework has it took six samples of RA for EH to suggest RA.
been intensively evaluated using various experiments. A
detailed example is the failure to grasp as described in the VIII. CONCLUSION AND FUTURE WORK
following section. We have tested the system’s ability to
learn the preference of selecting a solution by manually Through the test results in section VII, we showed that
introducing the Gripper Open (GO) Anomaly, shown in Fig. EH is able to learn the user preference of selecting a solution,
6, during the execution of a skill. In this test, we have tested even when it had learned a different preference earlier. As
the system’s ability to learn the user preference of selecting the user selects a specific solution to an Anomaly, the
the Repair Actuator (RA) Response over the Replace solution becomes more probable for future selection. This is
Pneumatics (RP) Response. For the purpose of the test, the normally helpful, but can be problematic if the user wishes
system had initially no knowledge of user selections the system to select a different solution, since learning a new
(samples), except for five samples confirming the choice of preference can take several iterations, as the test results
the RP Response as the user preferred solution to the GO showed. This is especially true when the sample count for
the prior solution is high. A possible future solution could be
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The path to success: Failures in Real Robots October 2, 2015
to introduce additional information in the Error Layer, e.g. [22] D. Stengel et al. "An Approach for Safe and Efficient Human-
condition the error cause not only on the counting of user Robot Collaboration.", The 6th International Conference on Safety
selections, but also on the state of various system variables of Industrial Automated Systems. 2010.
[23] C. Syan, Y. Mostefai, "Status monitoring and error recovery in
at time of the user selection. flexible manufacturing systems", Integrated Manufacturing
The system is currently being integrated in further Systems, Vol. 6 Issue 4, pp.43 – 48, 1995
demonstrators in the context of the SMErobotics project and
will see more in-depth testing and possibly enhancements in
these demonstrators. Concept videos of these showing the
SMErobotics vision of future industrial robotics are
available at http://video.smerobotics.org; especially the D2
and D3 videos are relevant in the context of this work.
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FinE-R 2015 Page 10 IROS 2015, Hamburg - Germany
The path to success: Failures in Real Robots October 2, 2015