Vision-based deep execution monitoring

arXiv:1709.10507v1 [] 29 Sep 2017

Francesco Puja, Simone Grazioso, Antonio Tammaro, Valsmis Ntouskos, Marta Sanzari, Fiora Pirri

Abstract— Execution monitor of high-level robot actions can
be effectively improved by visual monitoring the state of the
world in terms of preconditions and postconditions that hold
before and after the execution of an action. Furthermore a
policy for searching where to look at, either for verifying the
relations that specify the pre and postconditions or to refocus in
case of a failure, can tremendously improve the robot execution
in an uncharted environment. It is now possible to strongly
rely on visual perception in order to make the assumption that
the environment is observable, by the amazing results of deep
learning. In this work we present visual execution monitoring
for a robot executing tasks in an uncharted Lab environment.
The execution monitor interacts with the environment via
a visual stream that uses two DCNN for recognizing the
objects the robot has to deal with and manipulate, and a nonparametric Bayes estimation to discover the relations out of the
DCNN features. To recover from lack of focus and failures due
to missed objects we resort to visual search policies via deep
reinforcement learning.

I. I NTRODUCTION
Robot perception has been amazingly boosted in recent years by deep learning results on object recognition
[1], [2], relations recognition [3], visual question-answering
(VQA)[4], activity recognition [5], image annotation [6], and
navigation [7], [8] among other perceptual based abilities
[9]. The upshot is that robots interaction with the real world
nowadays seems to be in reach. The phenomenal success
of deep learning across various disciplines motivates us to
investigate whether useful representations in the robot task
execution monitoring, in unknown environments, can be
learned as well.
In this paper we address visual execution monitoring
(VExM) in a dynamic uncharted lab environment, for highlevel tasks. In order to monitor the state of execution
we embed real time recognition of objects and relations
which we call the visual stream, within a hybrid planning
model. The visual stream is defined by a hierarchical model
combining two deep convolution neural networks (DCNN)s
[2] for object recognition, and a non-parametric Bayes
model DPM[10] that uses the active features of the two
DCNNs to segment the depth images collected during task
execution. The combination of segmented depth and object
labels allows to infer visual relations from the robot point
of view. The hybrid planner, on the other hand, blends
deterministic planning, with durable actions [11], [12], [13],
with a contextually optimal visual search policy inferred
from current state execution trained with DNN [14], [15], to
cope with both task failures and loss of focus on the task. For
The authors are with ALCOR Lab, DIAG, Sapienza University
of Rome, Italy {puja, grazioso, tammaro, ntouskos,

sanzari, pirri}@diag.uniroma1.it

policies inference the VExM builds states representations of
the scene out of the visual stream results, which we call the
mental maps, see Figure 1. Indeed, mental maps are used for
both training the visual search policies and to recover focus
on the current task, basing on deep reinforcement learning
[14], [15]. An overview of a typical task execution in the
proposed framework is given in Figure 1.
Though visual-based view of robot execution has been
somehow faced previously by [16], [5], [17], [18], [7], [8],
the proposed approach is completely novel in particular in
terms of execution monitoring.
In this work perception is egocentric and we are considering high-level tasks, hence we are not considering motion
control, 3D object pose estimation, nor navigation issues, we
assume that all these problems are tackled by appropriate
algorithms, see e.g. [19], [20], [21], [22], [23], [7].
In summary, we address visual execution monitoring as an
hybrid deterministic/nondeterministic state machine streaming perceptual information, for both monitoring the execution and suitably directing visual perception. The VExM
refocuses and redeems from a failure according to learned
policies. Assuming that the visual stream provides a fully
observable environment the VExM relies on learned policies
[15] for focusing on the important objects involved in the task
execution, and likewise to assess the discrepancy between
the inferred state and the perceived one. Hence in case of
failure the VExM can always resort to a recovery policy that
ensures an optimized visual search to redeem into a state
where execution can be retrieved.
II. R ELATED W ORK
The earliest definitions of execution monitoring in nondeterministic environments have been introduced in [24],
[25]. Since then an extraordinary amount of research has
been done to address the nondeterministic response of the
environment to robot actions. Several definitions of execution
monitoring are reported in [26]. For high level robot tasks a
review of these efforts is given in [27]. The role of perception
in execution monitoring was already foreseen in the work
of [28], likewise recovery from errors that could occur
at execution time was already faced by [29]. Despite this
foresight, the difficulties in dealing with scene recognition
have directed the effort toward models managing the effects
of actions such as [30], [31], allowing to execute actions
in partially observable environments [32]. The integration
of observations in high level monitor has been recently
addressed by several authors, among which [33], [34]. Still,
the breakthrough, is achieved with DCNN and in general

search(robot,spraybottle)

approach(robot,table)

search(robot,spraybottle)

grasp(robot,spraybottle)

TV-monitor

chair

chair

chair
table

table
chair
table

table

On(spraybottle,table)

backpack

On(spraybottle,table)

backpack

backpack

floor

On(spraybottle,table)

On(spraybottle,floor)

spraybottle

backpack
spraybottle

spraybottle
chair

robot
Free(robot,robot
hand
hand)

robot hand

robot hand
table

table

chair

chair

chair
whiteboard

Legend

On(spraybottle,table)
spraybottle

backpack

spraybottle

floor

robot hand

Found(robot,spraybottle)
On(spraybottle,table)

blackboard

Holding(robot,spraybottle)
Free(robot,robot hand)

CloseTo(robot,table)
search(robot,person)

grasp(robot,spraybottle)

backpack

approach(robot,person)

Found(robot,spraybottle)
On(spraybottle,floor)

chair
floor
hand
person
robot hand

handover(spraybottle,person)

spraybottle
table
toolbox
TV-monitor

table

floor

person

floor

toolbox

person

hand

person
hand
spraybottle
Holding(hand,spraybottle)
toolbox

spraybottle

person

On(spraybottle,floor)

spraybottle
Holding(robot,spraybottle)

robot hand

TV-monitor
chair

robot hand

chair

TV-monitor

floor

table

TV-monitor

floor

toolbox
robot
spraybottle
hand
toolbox

hand

robot hand
Free(robot,robot hand)

floor

Holding(robot,spraybottle)

Found(robot,person)

CloseTo(robot,person)

Free(robot,robot hand)
Holding(person,spraybottle)

Fig. 1. Visual execution monitoring (VExM) of the task take the Spraybottle and hand it to Person. Actions written in blue are provided by the planner,
while those in red are generated by the visual search policy once a failure is detected. The post-conditions are shown below the images, those not verified,
leading to a failure, are shown in red. The second and forth rows of images show the bbs of the objects and in red their relations discovered by the visual
stream. The first and third rows of images represent the mental maps, here depth within the colored boxes is not visible. The hand misses the spraybottle
(5th image), postcondition of action grasp Holding(robot, spraybottle) fails while F ree(robot, robotHand) is verified. After a visual search the
spraybottle is found on the floor. The involved relation in the parsed plan is updated from On(spraybottle, table) to On(spraybottle, f loor) and the
failed action is re-executed. A further visual search finds the subject, and finally the spray bottle is handed to her. Note the verification of the relations
CloseT o(robotHand, hand) and F ree(robot, robotHand), this last ensuring task goal success.

with deep learning for perception [35], visual planning [18]
and with deep Reinforcement Learning (RL) [14], [15].
Still for relations recognition, despite there is a great
number of contributions [35], [3], [36] for 2D images, it
is missed an appropriate method for robot execution. Hence
we introduce a form of domain adaptation of the 2D images
taken from ImageNet to segment the 3D images collected by
the robot, basing on a DPM [10] that uses the active features
of the recognized objects involved in the relation.
Another relevant aspect is visual search and the environment representation. Since the work of [14] a wealth
of research has been done to make robust and extend RL
[31], [30] to very large set of actions, in so developing
a new amazing research area, deep RL [15], [37]. The
only difficulty is the need to simulate the environment to
perform the required huge collection of experiments. Hence
a certain amount of research has been devoted to create new
simulation environment such as [38], [8]. Here we contribute
with a quite simple representation of the environment using
the mental maps generated directly during robot experiments,
basing on the depth segmentation and the bounding boxes of
the recognized objects.
III. OVERVIEW OF OUR APPROACH TO VISUAL
EXECUTION MONITORING

The robot execution monitor (VExM) we consider interacts both with the planning environment (described in
Section V) and with the real world environment via the
visual stream (Section IV). It exchanges information about

the current state, the executed action and the action to be
executed in such a state. This information concerns objects,
terms and relations mentioned in the pre and postconditions
of the action, and it is oriented by the visual search policies
(Section V).
The planner environment has a deterministic component
that infers the list of actions, pre and postconditions before
execution starts, and a stochastic meta action which is the
visual search. Since each of the plans forming a task has only
one action affecting the world (see Section V) the beforehand
inference of the specific plan goal is fine, because this is
the only action that can fail, under our hypothesis of not
considering robot control, which would lead to a continuous
domain.
Example The task is illustrated in Figure 1. From
a lexical component not described here, the task is
parsed into a number of goals such as holding(robot, sb),
closeT o(robotHand, hand). For each of these goals there
is a plan issuing a sequence of actions, but for the visual
search, which is a policy sequencing actions like look-Up,
look-Down, turn-Left, turn-Right. Policies are learned basing
on the actor critic paradigm [31], [30], exploiting the deep
learning approach of [15].
At each state of the robot execution of the sequence
of actions, the VExM verifies via the visual stream the
feasibility of the next action visually assessing the relations
mentioned in the action pre and postconditions specified in
the state. The VExM uses this information to recompute
transitions, according to a loss function that averages the

discrepancy between the state (pre and postconditions of
actions) inferred by the planner and the one observed by
the visual stream (see Section VI).
If the objects of interest are out of the field of view some of
the pre or postconditions will fail and the VExM appeals to
the visual search to refocus on the object of interest. Typical
examples are when the robot misses to position itself in front
of a table or when it grasps something which falls down (see
Figure 1). In these cases the visual search has to output the
nice sequence of actions bringing the missed object back in
the visual field.
In summary, with the VExM we do a step toward bridging
the robot language and behavior between symbolic reasoning
(via planning and logical inference) and deep learning with
both the visual stream and the visual search paradigms. As a
final remark the environment in principle it is not required to
be completely static, still vision is egocentric and interaction
with people is limited to few actions. An overview is given
in Figure 1.
IV. T HE VISUAL STREAM
In this section we introduce the visual stream, which is
the visual processing of the execution state. A schema is
presented in Figure 2. Here a state st , t in N, specifies the
precondition of action at , to be executed at state st , and
the postconditions of action at−1 , after its execution. For
each state st of the execution, the VExM opens an inputoutput channel with the visual stream, formed by a triple
Ct = (u, F, V ), where u is a set of objects and relations on
them, expected to hold at execution state st , F is a buffer,
of size L, of images from the video recorded by the robot
and V is the visual stream processing results, obtained with
a hierarchy of deep models shown in Figure 2.
The models. There are essentially three models for the
visual stream, which are called in parallel at execution time.
The first model recognizes elements of the environment, a
second model is trained to recognize those items that the
robot can handle, and that can be occluded by the hand itself,
a third model is trained to recognize the spatial relations,
some of which are listed in Table I.
Step 1: recognizing objects in the scene. Let us consider
the first two models concerning the objects. The first model
is trained by collecting images of objects taken from both the
robot environment, with an RGBD camera and with images
of objects from ImageNet. The second model is trained with
both images of human hands holding objects, and empty,
and with the robot hand holding the objects of interest,
and empty. Both these models are based on region proposal
networks [2] and return in V the objects label, their location
in the image, and a probability for the images in F , given
the N objects in the model. Indeed, we are given, for each
object in u:
P (y = obj |x` , ϕ` ) = arg

max

x` ∈F,`∈L

N
X

h(x` , ϕ` )

(1)

`=1

Here h(x` , ϕ` ) is the softmax function of the region proposal
network [2] applied to each image in F , with ϕ` the

parameters.
Semi-supervised relations estimation. Despite recent
contributions of several authors on 2d images, there are some
difficulties to apply these approaches due to the relevance
of depth to capture spatial relations, from the robot vantage
point. To cope with these difficulties we have defined a model
for relations adapting the active features of the trained DCNN
[2], as described above, to the RGBD images collected by
the robot. For each object obj we estimate a statistics of
the active features with dimension 38 × 50 × 512, taken
before the last pooling layer, say Conv5, at each pixel inside
the recognized object bounding box bb(obj ) (here we are
referring to VGG, though we have considered also ZF, see
[39], [40]).
Step 2: objects segmentation within the bounding boxes.
Let the size of bb(obj ) be n = u × v and let zji =
Pd
k=1 xjik , i = 1, ..n, where xjik is the normalized features
value, with d the feature dimension at pixel i, indexed w.r.t.
the vectorized region of bb(obj ). Let Z = (z1 , . . . zn )> be
sorted in descending order, and let J be the corresponding
pixel location of each value in Z within the image resized
according to Conv5. We consider the vector Ẑobj formed by
the first 25 elements of Z (hence of J), which are the values
exceeding the mean square of Z, turning out to be inside
the object shape, in the RGBD-image resized according to
the size of Conv5. Projecting the pixel locations Jˆ of these
values on the resized image we obtain an observation matrix
of size 25 × 4, as we consider the RGB and depth channels.
This data collection is repeated for about 100 images for
each of the objects. With these features set, for each object
we estimate a Dirichlet process mixture (DPM) [10]. Hence
we have an infinite mixture model for each object, which
amounts to have a set of parameters Θh = (θh,1 , . . . θh,k )
for each object obh with mixture components k = 1, .., q,
with q inferred by the process (for more details about the
DPM and its computation we refer the interested reader to
[41], see also [42], [43]). Let obnew be an object in u mentioned in the current open channel Ct - such that for
some relation Rs ∈ u, obnew belongs to the relation domain
DRs . Then we seek the model Θh ∈ Θ and the component
θhk ∈ Θh , which maximizes the probability of the active
features selected as described above. Namely
M (bb(obnew )|Θ) =
q
N X
X
arg max {
phk f (Ẑ|θh,k )δobnew (D∃Rs )}
θhk ∈Θ

(2)

h=1 k=1

Note that here f (·) is the normal distribution for which
we assumed conjugate priors, phk are the mixtures weights,
N is the number of objects, hence of DP M models, and
δobnew (D∃Rs ) is 1 if obnew ∈ u belongs to the domain of
some Rs ∈ u, and 0 otherwise. The optimal parameters θh,k )
returns the component model that results in probability map
within the bounding box of object obnew , corresponding to
the segmentation for obnew , as shown in Figure 3 for the table
and the screwdriver. Note that the segmentation is always
relative to the region inside the bb. Note also that while the

DPM model estimation can take several hours, its evaluation
during execution, for the objects in u, takes a little more
than a second, being a second the time needed to run the
networks, in order to obtain the active features for all the
objects in u.
Step 3: computing relations. Once the partial segmentations (according to the bbs) are available, we finally
estimate the relations. For each relation Rs we use basic geometric properties of the spatial configurations of
the two bbs specifying it, together with reciprocal depth,
basing on the eight volume-volume relation model of
[44]. Here configurations with the same specification are
considered to be topologically equivalent. Let dmap =
g(M (bb(obi )|Θ), M (bb(obj )|Θ0 ), ID ) be the projection of
the mask of the probability map issued by the DPM on the
depth map of the RGBD image in F . Let pc be the confidence
in the specific bbs configuration according to the 9 volumevolume configurations for dmap, s.t sumpi = 1. Without
going into further details, we introduce a configuration
probability for the bbs of the two objects bb(obi ) and bb(obj )
as νij = βr−1 (Ar Dmap)pc with Ar the area of the two
bbs, r the number of relations and β a further normalization
constant.
Configurations of bounding boxes, even including depth,
do not convey enough information, since they lack a semantic
interpretation of the relations (for example in a specific image
Inside and On can have the same configuration). Hence for
each relation we define a co-occurrence matrix N × N amid
all objects. The co-occurrence matrix reinforces the links
between objects that have strong interactions and weakens
those links that are already weak. We estimate the best
×Λ
and H ∈
decomposition B = W H with W ∈ RN
+
Λ×N
R+ , approaching the non-negative factorization (NMF)
basing on the Bayesian NMF proposed in [45] to infer the
best reducing factor Λ. Let r be the number of relations,
considering the tensor resulting from N × N × r, link values
between obi and obj along the r coordinate are given by
λi,j,r . Given these findings, the probability that the relation
Rs holds for the observed objects obi , obj is given by the
probability that the geometric configuration of bb(obI ) and
bb(obj ) and their depth map (according to (2)) is correct,
and by the likelihood of the relation semantics given by
the co-occurrence matrix refining the relation domain. Let
ν be the vector of the configuration values and let λij be the
vector of link values for obi and obj and denote zs (i, j) the
element wise normalized product of the two vectors. Then
the probability of each relation is:
P (Rs |obi , obj ) = zs (i, j)p(obi |ϕ` )p(obj |ϕ`0 ), s = 1, . . . , r
(3)
We see that z acts as a weight and it takes care of
both the configuration of the bounding boxes according to
the probability map given in (2) and of the domain of the
relations, namely the statistics of their typical appearance
in a scene. The chosen relation is the one maximizing
the probability. Figure 3 show the result for the relation
On(screwdriver, table).

BBoxes, Classes

bb geometric
configuration

bb & depth

Relations

+

+

segmentation

D
P
M
M

classifier

RoI pooling

Faster R-CNN

Proposals

Features
Feature
Maps

co-occurrence
matrix
(for each relation )

V
IV
III
II
I

conv
layers

BBoxes,
Classes

Primary
activation
classifier

RoI pooling

Features
Proposals

Feature
Maps

Activation for relations
involving robot hand

Output:
Objects and Relations

V
IV
III
II
I

Faster R-CNN

conv
layers

Input
On demand
activation

Fig. 2.
The models involved in the visual stream processing for the
recognition of the current state st concerning preconditions of action at
and postconditions of action at−1 .

TABLE

TABLE

SCREWDRIVER

TABLE

SCREWDRIVER

ON

SCREWDRIVER

Fig. 3. The segmentation map: segmentation of table and screwdriver from
the bbs in the first image, according to (2), and relation found, according
to (3).

V. H YBRID PLANNING ENVIRONMENT
In this section we introduce the hybrid planning environment. This is made by a deterministic planner and a visual
search policy. The visual search policy is required either to
focus the robot toward the item of interest, so as to let the
visual stream to deal with the preconditions of the action to
be executed, or to recover from a failure. In our framework
a failure occurs when the robot misses the objects of interest
from the visual field, which is critical since in this case the
execution state cannot be verified by the visual stream.
The deterministic planner The deterministic planner
defines a nominal policy of high level actions to the goal. For
the deterministic planner we consider a domain D formed by
objects, relations and real numbers, which are represented in
the robot language L. A term of L can be an object obi
of the domain D, a variable or a function f : Dn 7→ D,
such as actions. Relations (here assumed to be only binary),
are defined exclusively for the specified set of objects of the
domain, and are interpreted essentially as spatial relations
such as On, CloseTo, Inside, Holding.
A robot task is defined by a list of plans hP1 , . . . , Pm i;
a plan Pi is defined as usual by an initial state and a set
of rules (axioms) specifying the preconditions H(at ) and
postconditions K(at ) of action a(t), where t is discrete
time indexed in N. To simplify we assume that preconditions and postconditions are conjunctions of ground atoms
(binary relations), hence a state st =H(at ) ∪ K(at ). Though

not necessary here, time maintains coherence between the
deterministic part of the execution monitor and the Markov
decision process underlying the visual search policy computation.
In our settings robot actions can be of two types: (1)
supply actions, which are actions changing the state of the
world, in so affecting some object in the world, such as
grasp, hand-over, place, open, close; (2) egocentric actions,
which are actions changing the state of the robot, such as
move-Forward, approach obj along a direction, or look-Up,
look-down.
Egocentric actions, despite belong to the deterministic
planner language, are mostly used within learned visual
search policies, which are inferred according to the current
state given a deep RL model [15]. On the other hand supply
actions can have non-deterministic effects, which then lead
to a failure, handled by a visual search recover policy.
According to these definitions a robot plan specifies at
most one supply-action and this action is a durable action,
defined by an action start and by an action end. Any other
action in the plan is an egocentric action. In particular, given
the initial state, each plan introduces at most a new primal
object the robot can deal with and the relations of this object
with the domain. An example is as follows: robot is holding
an object, as a result of a previous plan, and it has to put it
away on a toolbox. This ensures that recovery from a failure
is circumscribed for each plan to a single supply action.
The task goal is unpacked into an ordered list of goals
G=(G1 , . . . , Gm ) such that each Gi is the goal of plan Pi
in the list of plans forming a task. Given a goal Gi , if a
plan Pi leading from the initial state to the goal exists, then
a sequence of actions leading to the goal is inferred by the
search engine [46]. Here plans are defined in PDDL [47].
We extend [46] parser to inferred plans, so as to obtain
the initial state, the sequence of actions (a0 , . . . , an ) leading
to the goal, and the set of states SPi for each plan Pi . This
list forms the set of deterministic states and transitions.
In summary, the deterministic planner plays the role of an
owner’s manual providing the robot with instructions about
actions, properties, conditions that in principle should hold
to accomplish the task.
The visual search Policy. Given the visual stream and
the definition of a state we make here the assumption that
the environment is fully observable. Hence we introduce
a simple hybrid MDP, with discrete time, formed by the
quintuple (S, A, T, RW, γ)P , for each plan P. Here S, A
(set of states and actions for P) are generated by the plan
parser, T : S × A × S 7→ R is the transition probability
distribution, specifying the probability of moving between
states, namely each Tij =τ (st+1 |st , at ). T has an added
failure state. Initially T (which is relative to a plan Pi
hence should be written TPi ) is generated by the parser as
a right stochastic 0−1 matrix, and further updated by the
VExM (see next section); RW : S 7→ R is the expected
reward and γ ∈ [0, 1] is the discount factor. In particular
T = κ(Tpre + Tpost ), since states transitions are checked
and updated by the VExM before and after action execution.

We recall here some notions from MDP and RL [31], [30].
The value v of a state s isP
the expected return starting from
state st , namely v(s)=E[ k=0 γ k RWt+k |st =s], in matrix
form (for each state) v=RW + γT v. A policy π : S 7→ A
specifies the probability of an action a ∈ A given state s ∈ S,
it is time-independent and has the Markov property. The action value function Qπ (s, a) simply extends v(s) to v(s, a).
The optimal action-value function Q∗ (s, a)= maxπ Qπ (s, a)
is the maximum action-value function over all policies and
an optimal policy is one that achieves the optimal actionvalue function π ? (a, s)= arg maxa Q? (a, s).
We are interested in an optimal visual search policy, which
is a sequence of egocentric actions leading the robot to
observe the scene so that the preconditions of the action to be
executed or the postconditions of the executed action can be
observed and verified. So the search policy drives the visual
stream, which cannot be done by the blind deterministic
planner. As noted above visual search can be composed of
the following actions: look-Left, look-Right, look-Up, lookDown, move-Forward, move-Backward, turn-Left, turn-Right,
with all these actions specified by precise quantities coherent
with the underlying control, not mentioned here.
We approach the problem with a policy-based method
by computing the parametrized policy π(a|s, ψ)
[48], where ψ is the policy weight vector, hence
π(a|s, ψ)=P (At =a|st =s, ψt =ψ), and the parameter
update is:
ψt+1 =ψt +
α (RWt+1 + γv̂(st+1 , w) − v̂(st , w)) ∇ψ log π(at |st , ψ)
(4)
where v̂ is a differentiable state value parametrization, w ∈
Rm are state-value weights, ψ the policy weights, and α > 0
step size parameter [30]. This is an actor-critic model in
which the critic learns the action-value function while the
actor learns the policy π. The goal is to learn a visual search
policy. With A3C [15] have shown that a DNN can be used
to approximate both the policy π(at |st , ψ) (softmax over
actions) and the value function using two cost functions,
the actor Lπ (ψ), which is derived from (4) with an extra
entropy term
P to boost exploration in training, and the critic
Lv (ψ)=( i=0 γ i RWt+i − v(st |ψ)2 . For optimization they
used the standard non-centered RMSProp update: g=αg +
(1 − α)∆ψ 2 , and ψ ← ψ − η∆ψ(g + )−1/2 . Training
has been done with A3C-LSTM [15], though with input
the mental maps, described in the following. Our implementation follows https://github.com/miyosuda/
async_deep_reinforce. A reward of 1 is issued whenever the primal object of the current plan appears in the
mental map, then the robot restart the experiment.
Mental Maps There is an increasing interest in creating
environment for deep RL, taking raw pixels as input, for
example [38], [18], [8]. Here we use as input to the DNN
the automatically generated mental maps, collected during
experiments. The mental map takes as input an image from
F labeled by V with stable bounding boxes bbs according to
(1), the depth of the segmented objects within the bbs and a

and similarly for Tpost , hence T = κ(Tpre + Tpost ). This
include transition τ (sf ailure |st , at ) between state st and the
added failure state sf ailure ,(see Section V), so that the sum
is always one. If the transition value to the failure state
is greater than the transition value to the next state the
visual search policy is called, independently of the planning
requests. The algorithms are shown below.
VII. E XPERIMENTS AND RESULTS

Fig. 4. Mental Map generation for searching a person. The transparency
shows the depth map of the labeled objects

color code for each of the N objects in the language and a
link color, represented by a colored line, between the objects
in a relation. It then forms a colored map which represents
everything the robot knows about the world. In Figure 4 we
show a short sequence of close mental maps generated for
searching a person.
VI. F ULL EXECUTION ALGORITHM
In this section we introduce the VExM algorithm. In the
current implementation, given a task, the VExM is able to
choose the list of goals on the basis of a naive Bayes classifier
taking as input a bag of words, which we have not described
here. Given the goals the choice of a deterministic plan for
each goal is immediate. For each plan the VExM compute
the parser, which in turns infers the ordered list of egocentric
and supply actions A and states S to obtain the goal. We
recall that a state St specifies the set of preconditions and
postconditions of action at , namely H(at ) ∪ K(at ). The
algorithm starts with the ordered list of actions and states
as computed by the parser for a plan Pi in the list of plans,
and ends with the list of executed actions for Pi , where only
one action is a supply action. Note also that several events are
here not taken into account, such as camera malfunctioning
or other events concerning control and navigation that cannot
be currently handled by the high-level VExM.
At each state st the VExM computes via the visual stream
the probability of each relation in u, as described in Section
IV and a loss. Then it updates the transition T , starting in st
with the truth values inferred by the deterministic planner.
Because the monitor evaluates the loss for both preconditions
and postconditions separately we indicate two loss functions,
namely L(st , X(at )), with X ∈ {H, K}. Given at at state
st and for Rs ∈ ut , r the number of relations:
1. L(st , X(at )) =
X
1
k1 − P (Rs (obi , obj ))kδobi (Rs )δobj (Rs )
r
Rs ∈uH(at )

(5)
Here P (Rs (obi , obj )) is given in (3). According to (5) the
transition matrix between states sP
t and st+1 , is updated

0
Tpre
= (L(st , H(at ))Tpre ) / 2 s∈S L(st , H(at ))Tpre

Platform Experiments for the VExM presented here have
been done under different conditions in order to test different
aspects of the model. To begin with, all experiments have
been performed with a custom-made robot. A Pioneer 3 DX
differential-drive robot is used as a compact mobile base. To
interact with the environment we mounted a Kinova Jaco 2
arm with 6 degrees of freedom and a reach of 900mm and
finally, for visual perception, we used an Asus Xtion PRO
live RGB-D camera mounted on a Direct Perception PTU46-17.5 pan-tilt unit.
Tasks We have considered two classes of tasks: (1) bring an
object (spraybottle, screwdriver,hammer,cup) on the table or
inside the shelf to a subject; (2) put-away object (screwdriver,
hammer, spanner) in the toolbox. Each experiment in a class
has been run 35 times manually driving the robot to collect
images of the scene and 20 more times with the planning
environment described in Section V, despite in a number of
circumstances the grasping action has been manually helped,
especially with small objects. From robot experiments we
collected 120000 images, while from ImageNet we collected
25000 images. Table I shows the main relations, objects and
actions considered in the tasks.
Training We train the DCNN models using images taken
from the ImageNet dataset, as well as images collected by
the ASUS Xtion PRO RGB-D camera. We split the set of
images in training and validation sets with a proportion of
80%-20%. We performed 70000 training iterations for each
model on a PC equipped with 8 GPUs. Table II shows the
object detection accuracy achieved by the two DCNN models
dealing with the free and holding settings, respectively.
Failures We examine the number of failures encountered
during the executions of the tasks described above. A failure
is recorded as soon as the state perceived by the visual
stream via the DCNN and DPM models, does not match
the post-conditions of the action executed. The histogram
in Fig. 5 shows the probability that a failure is encounter
while executing a particular action in relation to the object
being involved. We note that, as expected, more complex
actions like grasping and localizing show a higher probability
of failure. Surprisingly, handing over an item shows a low
failure probability, this is mainly attributed to the high
adaptability of the subject involved in the action.
Recovery As explained in Section V, a visual search policy
is employed as soon as a failure is detected in order to find
the primary objects involved in the action that failed. Fig. 6
shows the success rate of the recovery via the use of a visual
search policy for four different types of actions. Localization
and manipulation actions show a higher recovery success

Input: Ordered list LI of actions and states for plan Pi
Output: List LE of experienced actions
while LI 6= [] do
Choose at ∈ LI, t = 0, . . . , n;
Open (u, F, V )t with visual stream;
Compute VH(at ) and update transition Tpre
according to (5) for H;
Compute Mmaps;
if at is visual search and args = obj then
˙ M maps ] =
[b
ut , Fbt , Vbt , π(),
t
V searchπ (at , obj , (u, F, V )t , M maps);
b t , F ← Fbt ,
a~t ← π(st ), u ← u
Vt ← Vbt , M maps = M maps ∪ {M mapst };
Execute a~t involving navigation and control, if
required;
Compute VK(at ) and update transition Tpost
according to (5) for K;
LE = LE ∪ {a~t } ;
else if τ (sf ailure |st , at ) < τ (st+1 |st , at ) or at is
not supply-action then
Execute action at involving navigation and
control, if required;
Compute VK(at ) and update transition Tpost
according to (5) for K;
LE = LE ∪ {a~t }
else if τ (sf ailure |st , at ) > τ (st+1 |st , at ) and at is
supply-action then
˙ M maps ] =
[b
ut , Fbt , Vbt , π(),
t
V searchπ (at , obj , (u, F, V )t , M maps);
b t , F ← Fbt ,
a~t ← π(st ), u ← u
b
Vt ← Vt , M maps = M maps ∪ {M mapst };
Execute a~t involving navigation and control, if
required;
Compute VK(at ) and update transition Tpost
according to (5) for K;
LE = LE ∪ {a~t }
end
T = κ(Tpre + Tpost );
t ← t + 1;
end
if Empty list of actions then
return LE
else
Failure
end
Algorithm 1: Execution Monitor.
V

V

TABLE I
S UBSET OF O BJECTS , RELATIONS , SUPPLY- ACTIONS AND EGOCENTRIC
L

ACTIONS OF THE ROBOT LANGUAGE

Relations
CloseTo
Found
Free
Holding
Inside
On
InFront
Left
Right
Under
Behind
Clear
Empty

Objects
Bottle
Chair
Cup
Floor
Hammer
Person
Spray Bottle
Screwdriver
Shelf
Toolbox
TV-Monitor
Table
Door

Supply actions
Close
Grasp
Open
Hand-over
Place
Lift
Push
Spin

Egocentric actions
Look-down
Look-left
Look-right
Look-up
Move-forward
Move-backward
Turn-left
Turn-right
Localize
Rise-arm
Lower-arm
Close-Hand
Open-Hand

Dispose

TABLE III
R ELATIONS DETECTION ACCURACY

Relations
CloseTo
Found
Free
Holding
Inside
On
] InFront
Left
Right
Under
Behind
Clear
Empty
Average

V

Accuracy
64%
73%
62%
79%
75%
83%
78%
68%
72%
71%
89%
67%
76%
73.6%

V

1

0.9

0.8

0.7

Probability of failure

0.6

0.5

0.4

0.3

0.2

0.1

ze

ze

ali

ali

oc

oc
,L
ble
Ta

ze

ze

ze
r,L
ito
on

ali

ali

ali

oc

oc

oc
,L

lf,L

oc

or,
L

rs
on

Sh
e

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-m
TV

n

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ali
Flo

ze

se
,C
lo
ox

,L
air

olb

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ox

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ox

olb
To

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sp

r

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r

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an

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dri
w

olb

re

dri
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re
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sp

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er

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ttle

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y

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tt
y

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,L

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Sp

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ra

Pla

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er,

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ttle

er,

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er,

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ttle

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ze

0

Object,Action

Fig. 5.

Histogram of action/objects failures

rate, while recovering from a failed grasping seems to be
the most problematic.
ACKNOWLEDGMENT

Input: (at , obj , (u, F, V )t , M maps)
Output: [ût , F̂t , V̂t , πt , M mapst ]
Compute π(st ), for visual search obj , with obj
argument of at , input M maps, with A3C model;
Compute M maps;
b t , Fbt , Vbt , π
return u
Algorithm 2: Visual Search V search
V

V

This research is supported by EU H2020 Project Secondhands 643950.
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TABLE II
OBJECT DETECTI0

Objects
ott e
chair
cup
floor
hammer
per on
pray bottle
crewdriver
helf
toolbox
TV-Monitor
table
door
Av erage

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