IEEE Robotics & Automation Magazine - March 2022 - 32

0.125
0.15
0.175
0.025
0.05
0.075
0.1
(a)
10
15
20
25
5
100
20
40
60
80
100
200
300
(c)
400
500
600
Figure 10. Time performance and feasiblity plots. (a) Time
versus the number of plans input to the ranking stage.
(b) Time and number of feasible plans versus the number
of plans input to the feasiblity stage. (c) Percentage of
feasible plans versus the number of plans input to the
feasiblity filter stage.
100
200
300
(b)
Feasible (%)
400
500
Time (s)
100
20
40
60
80
Output
600
Time (s)
the number of plans that it will analyze. We do this by
removing the least semantically similar plans from .R
W For
example, the a resulting from our bedroom setup translates
into a waiting time for the user of 24.7 s if the size of R
kept the same as W . If we reduce this size to 100 plans, the
W is
waiting time falls to 5.96 s.
However, by reducing the number of plans analyzed by
the planner, we also cut the maximum number of presented
options from 107 to 30 in the previous example [Figure
10(b)]. While this might seem like an important
reduction at first, it does not have a significant effect on the
combination of low-level actions and objects that are output.
In the following experiments, we show that most of
these options result in redundant combinations. Therefore,
independent of the size of the plan set analyzed by the
planning system, our algorithm's solution is optimal in the
sense that it outputs and presents all feasible combinations
of low-level actions and objects in the data set.
In Figure 10(c), we demonstrate the effect of removing the
least semantically similar plans from .RW We expected that
plans toward the end of the set involve objects that are not
present in the environment, hence their lower semantic similarity.
Therefore, they will result in fewer feasible plans when
compared with schemes from the beginning of the set. As
seen, the percentage of feasible plans quickly falls as more
plans from R
W are considered, which is consistent with what
we anticipated.
Our final experiment validates how well our algorithm
can combine low-level actions to provide higher-level options
to the user. For this, we first categorized objects in two general
classes depending on their functionality: 1) receptacles and
2) general objects. Given the low-level robot actions available
for the experiment, we measured which combinations of lowlevel
actions, receptacles, and general objects were output.
Furthermore, we measured the number plans input to the
feasibility filter until an existing combination was output. In
addition, we counted the number of options that resulted in
the same combination. For example, " place a book on a bed "
and " put a book on the bed " are options that resulted from
considering different entries in
W but they are equivalent
R ,
combinations of low-level actions and objects. We provide the
results in Figure 11.
Importantly for the aims of this article, 60% of the existing
interactions in the data set are output after considering only
the first 100 plans in
W Furthermore, 80% are output after
R .
Table 1. The time performance and feasibility
details.
LS
Kitchen
Living room
Bedroom
Bathroom
Total
Total
Plans
440
687
631
522
2,280
Time (s)
4.2
14.3
24.7
3
46.2
Total
Feasible
Plans
Zero
32
107
Zero
139
considering the first 200. Interestingly, the output contains several
redundant combinations (13 for " book " - " desk, " 10 for
" cell_phone " - " desk, " and so on), while options for other combinations
are not produced ( " remote_control " - " arm_chair " ).
This could be explained by the relatively low frequency of
appearance for some words in the data set, which directly
relates to the number of plans in which they are considered.
Furthermore, objects such as " laptop " have more plans that
relate to turning them on/off but not for picking and placing
them, while objects such as " remote_control " have more plans
in other localities, e.g., the living room, which explains why
32 * IEEE ROBOTICS & AUTOMATION MAGAZINE * MARCH 2022
Feasible (%)
Time (s)
Time (s)
Number of
Feasible Plans (Output)
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000

IEEE Robotics & Automation Magazine - March 2022

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