Rapid Concept Learning for Mobile Robots.
Concept learning in robotics is an extremely challenging problem: sensory data is often high-dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce features such as opening or wall. The second task is recycling, where the robot has to learn to recognize objects, such as a trash can. We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.
Introduction
Programming mobile robots to successfully operate in unstructured environments,
including offices and homes, is tedious and difficult. Easing this programming burden
seems necessary to realize many of the possible applications of mobile robot
technology [7]. One promising avenue towards smarter and easier-to-program robots
is to equip them with the ability to learn new concepts and behaviors. In partic-
ular, robots that have the capability of learning concepts could be programmed
or instructed more readily than their non-learning counterparts. For example, a
robot that could be trained to recognize landmarks, such as "doors" and "intersec-
tions", would enable a more flexible navigation system. Similarly, a recycling robot,
which could be trained to find objects such as "trash cans" or "soda cans", could
be adapted to new circumstances much more easily than non-learning robots (for
example, new objects or containers could be easily accommodated by additional
training).
Robot learning is currently an active area of research (e.g. see [5], [6], [9], [16]).
Many different approaches to this problem are being investigated, ranging from
supervised learning of concepts and behaviors, to learning behaviors from scalar
feedback. While a detailed comparison of the different approaches to robot learning
is beyond the scope of this paper (see [17]), it is arguable that in the short term,
robots are going to be dependent on human trainers for much of their learning.
S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI
Specifically, a pragmatic approach to robot learning is one where a human designer
provides the basic ingredients of the solution (e.g. the overall control architec-
ture), with the missing components being filled in by additional training. Also,
approaches involving considerable trial-and-error, such as reinforcement learning
[25], are difficult to use in many circumstances, because they require long training
times, or because they expose the robot to dangerous situations. For these reasons,
we adopt the framework of supervised learning, where a human trainer provides
the robot with labeled examples of the desired concept.
Supervised concept learning from labeled examples is probably the most well-studied
form of learning [20]. Among the most successful approaches are decision
trees [23] and neural networks [19]. Concept learning in robotics is an extremely
challenging problem, for several reasons. Sensory data is often very high-dimensional
(e.g. even a coarsely subsampled image can contain millions of pixels),
noisy due to specularities and other irregularities, and typically data collection
requires the robot to move to different parts of its environment. Under these con-
ditions, it seems clear that some form of a priori knowledge or bias is necessary for
robots to be able to successfully learn interesting concepts.
In this paper, we investigate two general approaches to bias sensory concept
learning for mobile robots. The first is based on spatial decomposition of the sensor
representation. The idea here is to partition a high-dimensional sensor represen-
tation, such as a local occupancy grid or a visual image, into multiple quadrants,
and learn independently from each quadrant. The second form of bias investigated
here is to learn multiple concepts using a shared representation. We investigate the
effectiveness of these two approaches on two realistic tasks, navigation and recy-
cling. Both these tasks are studied on a real robot called PAVLOV (see Figure 1).
In both problems, we use a standardized function approximator, in the form of a
feedforward neural net, to represent concepts, although we believe the bias strategies
studied here would be applicable to other approximators (e.g. decision trees
or instance-based methods).
In the navigation task, PAVLOV is required to traverse across an entire floor of
the engineering building (see Figure 10). The navigational system uses a hybrid
two-layered architecture, combining a probabilistic planning and execution layer
with a reactive behavior-based layer. The planning layer requires the robot to map
sensory values into high-level features, such as "doors" and "openings". These
observations are used in state estimation to localize the robot, and are critical to
successful navigation despite noisy sensing and actions. We study how PAVLOV
can be trained to recognize these features from local occupancy grid data. We also
show that spatial decomposition and multiple category learning provide a relatively
rapid training phase.
In the recycling task, PAVLOV is required to find items of trash (e.g. soda cans
and other litter) and deposit them in a specified trash receptacle. The trash receptacles
are color coded, to make recognition easier. Here, we study how PAVLOV
can be trained to recognize and find trash receptacles from color images. The data
RAPID CONCEPT LEARNING FOR MOBILE ROBOTS 3
is very high dimensional, but once again, spatial decomposition and multi-category
learning are able to sufficiently constrain the hypothesis space to yield fast learning.
The rest of the paper is organized as follows. We begin in Section 2 by describing
the two robotics tasks where we investigated sensory concept learning. Section 3
describes the two general forms of bias, decomposition and sharing, used to make
the concept learning problem tractable. Section 4 describes the experimental results
obtained on a real robot platform. Section 5 discusses the limitations of our
approach and proposes some directions for further work. Section 6 discusses some
related work. Finally, Section 7 summarizes the paper.
2. Two Example Tasks
We begin by describing the real robot testbed, followed by a discussion of two
tasks involving learning sensory concepts from high-dimensional sensor data. The
philosophy adopted in this work is that the human designer specifies most of the
control architecture for solving the task, and the purpose of sensory concept learning
is to fill in some details of the controller.
2.1. PAVLOV: A Real Robot

Figure

shows our robot PAVLOV 1 , a Nomad 200 mobile robot base, which was
used in the experiments described below. The sensors used on PAVLOV include
ultrasound sonar and infra-red (IR) sensors, arranged radially in a ring. Two sets
of bumper switches are also provided. In addition, PAVLOV has a color camera
and frame-grabber. Communication is provided using a wireless Ethernet system,
although most of the experiments reported in this paper were run onboard the
robot's Pentium processor.

Figure

1. The experiments were carried out on PAVLOV, a Nomad 200 platform.
4 S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI
2.2. Navigation
Robot navigation is a very well-studied topic [1]. However, it continues to be
an active topic for research since there is much room for improvement in current
systems. Navigation is challenging because it requires dealing with significant sensor
and actuator errors (e.g. sonar is prone to numerous specular errors, and odometry
is also unreliable due to wheel slippage, uneven floors, etc.
We will be using a navigation system based on a probabilistic framework, formally
called partially-observable Markov decision processes (POMDP's) [4], [13], [21].
This framework uses an explicit probabilistic model of actuator and sensor uncer-
tainty, which allows a robot to maintain belief estimates of its location in its envi-
ronment. The POMDP approach uses a state estimation procedure that takes into
account both sensor and actuator uncertainty to determine the approximate location
of the robot. This state estimation procedure is more powerful than traditional
state estimators, such as Kalman filters [14], because it can represent discontinuous
distributions, such as when the robot believes it could be in either a north-south
corridor or an adjacent east-west corridor.
For state estimation using POMDP's, the robot must map the current sensor
values into a few high level observations. In particular, in our system, the robot
generates 4 observations (one for each direction). Each observation can be one
of four possibilities: door, wall, opening, or undefined. These observations are
generated from a local occupancy grid representation computed by integrating over
multiple sonar readings.

Figure

2 illustrates the navigation system onboard PAVLOV, which combines a
high level planner with a reactive layer. The route planner and execution system
used is novel in that it uses a discrete-event probabilistic model, unlike previous
approaches which use a discrete-time model. However, as the focus of this paper is
on learning the feature detectors, we restrict the presentation here to explaining the
use of feature detectors in state estimation, and refer the reader to other sources
for details of the navigation system [11], [18].
The robot maintains at every step a belief state, which is a discrete probability
distribution on the underlying state space (e.g. in our environment, the belief state
is a 1200-dimensional vector). If the current state distribution is ff prior , the state
distribution ff post , after the execution of an abstract action a, is given by 2
ff post
scale
This updated state distribution now serves as ff prior when the state distribution
is updated to ff post , after an abstract observation
ff post
scale
O(ojs)ff prior (s); 8s 2 S (2)
In both updates, scale is a normalization constant that ensures that
RAPID CONCEPT LEARNING FOR MOBILE ROBOTS 5
ff post
This is necessary since not every action is defined in every state (for example, the
action go-forward is not defined in states where the robot is facing a wall).
2.3.

Abstract

Observations
In each state, the robot is able to make an abstract observation. This is facilitated
through the modeling of four virtual sensors that can perceive features in the nominal
directions front, left, back and right. Each sensor is capable of determining if
a percept is a wall, an opening, a door or if it is undefined. An abstract observation
is a combination of the percepts in each direction, and thus there are 256 possible
abstract observations. The observation model specifies, for each state and action,
the probability that a particular observation will be made.
Denote the set of virtual sensors by I and the set of features that sensor i 2 I
can report on by Q(i). The sensor model is specified by the probabilities v i (f js)
for all i 2 I , f 2 Q(i), and s 2 S, encoding the sensor uncertainty. v i is the
probability with which sensor i reports feature f in state s. An observation o is the
aggregate of the reports from each sensor. This is not explicitly represented. We
calculate only the observation probability. Thus, if sensor i reports feature f , then
Y
i2I
Given the state, this assumes sensor reports from different sensors are independent.0000000000000000000000000000000000000000000000000000000000000000111111111111111111111111111111111111111111111111111111111111111111111111
Sensor Reports
Grids
Raw Sensor
Values
Motor
Commands
Action
Reports
Action
Commands
Neural Net
Feature
Detectors Layer
Behavior-based
Layer
Planning

Figure

2. A hybrid declarative-reactive architecture for robot navigation. The neural net feature
detectors (shaded box) are trained using spatial decomposition and multi-task learning.
6 S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI
2.4. Recycling
The second task we study is one where the robot has to find and pick up litter
lying on the floor (e.g. soda cans and other junk) and deposit it in a colored trash
receptacle (see Figure 3). This task involves several component abilities, such as
locating and picking up the trash, and also subservient behaviors (such as avoiding
obstacles etc. However, for the purposes of this paper, we will mainly focus on
the task of detecting a trash can from the current camera image, and moving the
robot till it is located adjacent to the trash can.

Figure

3. Image of a trash can, which is color coded to facilitate recognition (this can is colored
yellow).
Avoid
Motors/Turret
Turn
Camera
Sensors
Avoid
Bump

Figure

4. Behavior-based architecture for recycling task. The focus of sensory concept learning
here is to improve "camera turn" behavior by learning how to detect and move towards trash
cans.
RAPID CONCEPT LEARNING FOR MOBILE ROBOTS 7
The recycling task is accomplished using a behavior-based architecture [2], as
illustrated in Figure 4. Only one of the behaviors, "camera turn" is improved by
the sensory concept learning methods described here, in particular, by learning
how to detect and move towards the trash can. The other behaviors implement a
collection of obstacle avoidance algorithms, which are not learned.
3. Accelerating sensory concept learning
Learning sensory concepts is difficult because the data is often very high-dimensional
and noisy. The number of instances is often also limited, since data collection requires
running the robot around. In order to learn useful concepts, under these
conditions, requires using some appropriate bias [20] to constrain the set of possible
hypotheses 3 The study of bias is of paramount importance to machine learning,
and some researchers have attempted a taxonomy of different type of bias (e.g. see
[24]). Among the main categories of bias studied in machine learning are hypothesis
space bias (which rules out certain hypotheses), and preference bias which ranks one
hypotheses over another (e.g. prefer shallower decision trees over deeper ones).
In the context of robotics, the ALVINN system [22] for autonomous driving is a
good example of the judicious use of hypotheses bias to speed convergence. Here, for
every human provided example, a dozen or so synthetic examples are constructed
by scaling and rotating the image input to the net, for which the desired output
is computed using a known pursuit steering model. We present below two ways
of accelerating sensory concept learning, which can also be viewed as a type of
hypotheses space biases.
3.1. Spatial Decomposition
The sensory state space in both tasks described above is huge (of the order of
several hundred real-valued inputs). The number of training examples available is
quite limited, e.g. on the order of a few hundred at most. How is it possible to
learn a complex function from such a large state space, with so little data? We use
two general approaches to decompose the overall function learning problem.
The first idea is simple: partition the state into several distinct regions, and learn
subfunctions for each region. The idea is illustrated in Figure 5. This idea is used
in the navigation domain to train four separate feature detectors, one each for the
front and back quadrants of the local occupancy grid, and one each for the left and
right quadrants. There are two advantages of such a decomposition: each image
generates four distinct training examples, and the input size is halved from the
original input (e.g. in the navigation domain, the number of inputs is 512 rather
than 1024).
8 S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI
3.2. Multi-class Learning
The second strategy used in our work to speed sensory concept learning is to learn
multiple categories using a shared structure. This idea is fairly well-known in neural
nets, where the tradeoff between using multiple single output neural nets vs. one
multi-output neural net has been well studied. Work by Caruana [3] shows that even
when the goal is to learn a single concept, it helps to use a multi-output net to learn
related concepts. Figure 6 illustrates the basic idea. In the recycling domain, for
example, the robot learns not just the concept of "trash can", but also whether the
object is "near" or "far", on the "left" or on the "right". Simultaneously learning
these related concepts results in better performance, as we will show below.
4. Experimental Results
The experiments described below were conducted over a period of several months
on our real robot PAVLOV, either inside the laboratory (for recycling) or in the
corridors (for navigation). We first present the results for the navigation task, and
subsequently describe the results for the recycling task.
4.1. Learning Feature Detectors for Navigation
Given that the walls in our environment were fairly smooth, we found that sonars
were prone to specular reflections in a majority of the environment. This made
it difficult to create hard-coded feature detectors for recognizing sonar signatures.
We show below that using an artificial neural network produced more accurate
and consistent results. Not only was it easy to implement and train, but it is also
possible to port it to other environments and add new features. Figure 7 shows
the neural net used in feature detection. The net was trained using the quickprop
method [8], an optimized variant of the backpropagation algorithm.
local occupancy grids were collected by running the robot through the
hallways. Each local occupancy grid was then used to produce 4 training patterns.
The neural net was trained on 872 hand labeled examples. Since all sensors predict
the same set of features, it was only necessary to learn one set of weights. Figure 8
shows the learning curve for the neural net, using batch update. Starting off with
a set of random weights, the total error over all training examples converged to an
acceptable range (! 1) within about training epochs.
A separate set of data, with 380 labeled patterns, was used to test the net. This
would be the approximately the number of examples encountered by the robot,
as it navigated the loop in the Electrical Engineering department (nodes 3-4-5-6
in

Figure

10). Feature prediction is accomplished by using the output with the
maximum value. Out of the 380 test examples, the neural net correctly predicts
features for 322, leading to an accuracy of 85%.
RAPID CONCEPT LEARNING FOR MOBILE ROBOTS 900000000000000000000000000000000011111111111111111111111111111111111111111111000000000000000000000000000000000000111111111111111111111111111111111111
sensor
representation original function
front
back
left
right

Figure

5. Spatial decomposition of the original sensory state helps speed learning sensory concepts.
Here, the original sensory space is decomposed into a pair of two disjoint quadrants.00110011001101010011 001100000000000000000000000001111111111111111111111111000000000000000000000000000000000000000000000000001111111111111111111111111111111111111111111110000011111000000000000000000000000011111111111111111111111110000011111 00000000000000000000000000000011111111111111111111111110000000000000000000000000111111111111111111111111100000000000000000000000001111111111111111111111111000000111111000000111111000000111111
Inputs
Concept1
Concept2
Concept3
Concept4

Figure

6. Learning multiple concepts simultaneously using a shared representation can speed
sensory learning.
S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI
Front
Back
Left Right
Hidden Layer Output Layer
Input Layer
local
occupancy
grid (32x32)
Door
Wall
Opening
Undefined

Figure

7. A local occupancy grid map, which is decomposed into four overlapping quadrants (left,
right, top, bottom), each of which is input to a neural net feature detector. The output of the net
is a multi-class label estimating the likelihood of each possible observation (door, opening, wall,
or undefined). The net is trained on manually labeled real data.20060010000
Total
Epochs
curve for neural net feature detector

Figure

8. Learning curve for training neural net to recognize features. The net is trained on 872
hand labeled examples using quickprop.
RAPID CONCEPT LEARNING FOR MOBILE ROBOTS 11
Opening)
(Right
Opening)
(Back Opening) (Back Opening)
Wall)
(Right
Opening)
(Back Opening)
Wall)
(Right
Wall)
(Back Opening)
Wall)
(Right
Wall)
(Back Opening)
Wall)
(Right
Wall)
(Back Opening)
Wall)
(Right
Wall)
(a)
(c)
(b)
(d)

Figure

9. Sample local occupancy grids generated over an actual run, with observations output
by the trained neural net. Despite significant sensor noise, the net is able to produce fairly reliable
observations.
S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI

Figure

9 illustrates the variation in observation data, generated during an actual
run. In these occupancy grids, free space is represented by white, while black
represents occupied space. Gray areas indicate that occupancy information is un-
known. The figures are labeled with the virtual sensors and corresponding features,
as predicted by the neural net.
Specular reflections occur when a sonar pulse hits a smooth, flat surface angled
obliquely to the transducer. The possibility exists that the sonar pulse will reflect
away from the sensor, and undergo multiple reflections before it is received by the
sensor. As a result, the sensor registers a range that is substantially larger than
the actual range. In the occupancy grids, this results in a physically occupied
region having a low occupancy probability. In Figure 9(a) where the specularities
are relatively insignificant, the neural net does an accurate job of predicting the
features. Effects of the specularities are noticeable in Figure 9(b) and Figure 9(c).
In

Figure

9(b) the neural net is able to predict a wall on the left, although it
has been almost totally obscured by specular reflections. The occupancy grid in

Figure

9(c) shows some bleed-through of the sonars. In both examples, the neural
net correctly predicts the high level features. Figure 9(e) and Figure 9(f) are
examples of occupancy grids where the effects of the specularities become very
noticeable. In these examples specularities dominate, almost totally wiping out
any useful information, yet the neural net is still able to correctly predict features.
From the presented examples, it is apparent that the neural net can robustly
predict features in a highly specular environment. Testing the neural net on an
unseen set of labeled data reveals that it is able to correctly predict 85% of the
features. In addition, although examples have not been presented, the neural net
is able to accurately predict features even when the robot is not approximately
oriented along one of the allowed compass directions.
The navigation system was tested by running the robot over the entire floor of the
engineering building over a period of several months (see Figure 10). The figure also
shows an odometric trace of a particular navigation run, which demonstrates that
despite significant odometric and sensor errors, the robot is still able to complete
the task.
4.2. Learning to Find Trash Cans
We now present the experimental findings from the recycling task. In order to
implement a similar neural network approach, we first took various snapshots of
the trash can from different angles and distances using the on-board camera of
PAVLOV. The images (100x100 color images) were labeled as to the distance and
orientation of the trash can. Six boolean variables were used to label the images
(front, left, right, far, near, very-near).
The inputs to our neural network were pre-processed selected pixels from the
100X100 images, and the outputs were the six boolean variables. The RGB values
of the colored images were transformed into HSI values (Hue, Saturation, Intensity)
which are better representatives of true color value because they are more invariant
RAPID CONCEPT LEARNING FOR MOBILE ROBOTS 13
Electrical Engineering
Department
Main
Office
Engineering
Computing
Computer
Science
Department
3.25 mm
Faculty Office
Robot Lab18
Y
(meters)
Odometric plot of three successive runs on PAVLOV in the EE department
Figure

10. The 3rd floor of the engineering building was used to test the effectiveness of the feature
detectors for navigation. The bottom figure shows an odometric trace of a run on PAVLOV,
showing the robot starting at node 1, doing the loop (3-4-5-6), and returning to node 1. The
robot repeated this task three times, and succeeded despite significant odometric errors.
14 S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI
to light variations [10]. Using an image processing program we identified the HSI
values of the yellow color and based on those values we thresholded the images
into black and white. We then sub-sampled the images into 400 pixels so that we
could have a smaller network with far fewer inputs. The sub-sampling was done by
selecting one pixel in every five.

Figure

11 shows the neural net architecture chosen for the recycling task. Figure
shows some sample images, with the output generated by the trained neural
net. The neural net produces a six element vector as its output, with 3 bits indicating
the direction of the trash can (left, front, or right), and 3 bits indicating the
distance (far, near, very near). The figure shows only the output values that were
close to 1. Note that the net can generate a combination of two categories (e.g.
near and very-near), or even sometimes a contradictory labeling (e.g. far/near). In
such cases, the camera turn behavior simply chooses one of the labels, and proceeds
with capturing subsequent images, which will eventually resolve the situation (this
is shown in the experiments below). Figure 13 shows the learning curve for training
the trash can net.

Figure

14 shows the experimental setup used to test the effectiveness of the trash
can finder. A single yellow colored trash can was placed in the lab at four different
locations. In each case, the robot was started at the same location, and its route
measured until it stopped adjacent to the trash can (and announced that it had
found the trash can). 4 Figure 15 and Figure 16 show several sample trajectories of
the robot as it tried to find the trash can. In all cases, the robot eventually finds
the trash can, although it takes noticeably longer when the trash can is not directly
observable from the starting position.
5. Limitations of the Approach
The results presented above suggest that high-dimensional sensory concepts can
be learned from limited training examples, provided that a human designer carefully
structures the overall learning task. This approach clearly has some definite
strengths, as well as some key limitations.
ffl Need for a teacher: Supervised concept learning depends on a human teacher
for providing labeled examples of the desired target concept. Previous work on
systems such as ALVINN [22] has clearly demonstrated that there are interesting
tasks where examples can be easily collected. Similarly, for the navigation and
recycling task, we have found that collecting and labeling examples to be a fairly
easy (although somewhat tedious) task. Nevertheless, this approach could not
be easily used in domains where it is difficult for a human teacher to find a
sufficiently diverse collection of examples.
ffl Filling in details of a pre-specified architecture: The approach taken in this
paper assumes that the designer has already pre-specified much of the overall
control structure for solving the problem. The purpose of learning is to complete
RAPID CONCEPT LEARNING FOR MOBILE ROBOTS 15
inputs 6 outputs
hidden units
RIGHT
FRONT
FAR
VERY NEAR

Figure

11. A neural net trained to detect trash cans.
a b c
Figure

12. Sample images with the output labels generated by the neural net a: front, near. b:
front, far. c: left, front, far. d: right, far. e: left, near, very-near. f: front, very-near.
S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI20601001401800 20 40
Epochs
Comparing Multi-Output Learning vs. Single Output Learning
'multi-output.err'
'very-near.err'

Figure

13. This graph compares the training time for a multi-output net vs. training a set of
single output nets. Although the multi-output net is slower to converge, it performed better on
the test data.
a few missing pieces of this solution. In the navigation task, for example,
the feature detectors are all that is learned, since the overall planner, reactive
behaviors, and state estimator are pre-programmed. Obviously, this places a
somewhat large burden on the human designer.
ffl Decomposable functions: The sensory concepts being learned in the two tasks
were decomposable in some interesting way (either the input or the output space
could be partitioned). We believe many interesting concepts that robots need
to learn have spatial regularity of some sort that can be exploited to facilitate
learning.
6. Related Work
This research builds on a distinguished history of prior work on concept learning
from examples, both in machine learning [20] and in robot learning [5], [9]. Here,
we focus primarily on the latter work, and contrast some recent neural-net based
approaches with decision-tree based studies.
ALVINN [22] uses a feedforward neural net to learn a steering behavior from
labeled training examples, collected from actual human drivers. As noted earlier,
ALVINN exploits a pursuit model of steering to synthesize new examples to speed
RAPID CONCEPT LEARNING FOR MOBILE ROBOTS 17

Figure

14. Environmental setup for finding trash cans.
learning. ALVINN differs from our work in that it directly learns a policy, whereas
in our case the robot learns only feature detectors and recognizers. We believe
that directly learning an entire policy is quite difficult, in general. In fact, in a
subsequent across-the-country experiment, the direct policy learning approach was
rejected in favor of a simpler feature-based approach similar to our work (except
the templates were 1-dimensional rather than 2-dimensional, as in our work).
Thrun and Mitchell [27] propose a lifelong learning approach, which extends the
supervised neural-net learning framework to handle transfer across related tasks.
Their approach is based on finding invariances across related functions. For ex-
ample, given the task of recognizing many objects using the same camera, invariances
based on scaling, rotation, and image intensity can be exploited to speed up
learning. Their work is complementary to ours, in that we are focusing on rapid
within-task learning, and the invariants approach could be easily combined with
the partioning and multi-class approach described here.
Such studies can be contrasted with those using decision trees. For example,
Tan [26] developed an ID3 decision-tree based algorithm for learning strategies for
picking up objects, based on perceived geometric attributes of the object, such as
its height and shape. Salganicoff et al. [15] extended the decision-tree approach
for learning grasping to an active learning context, where the robotic system could
itself acquire new examples through exploration.
In general, the decision tree approaches seem more applicable when the data is not
high-dimensional (in both the system just cited, the number of input dimensions is
S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI
-1.4
-1.2
-0.4
-0.20.2
South
East
Navigation to lab position A
"pose.a1"
"pose.a2"
"pose.a3"
-0.3
-0.2
South
East
Navigation to lab position B
"pose.b1"
"pose.b2"
"pose.b3"

Figure

15. Three successful traces (starting at 0,0) of the robot navigating to the trash can, placed
in positions A and B. In both positions A and B, the trash can was directly observable from the
robot starting position.
South
East
Navigation to lab position C
"pose.c1"
"pose.c2"
"pose.c3"
South
East
Navigation to lab position D
"pose.d1"

Figure

16. Results for learning with trash can in position C and D. The top figure shows three
successful traces (starting at 0,0) of the robot navigating to lab position C. Note that in pose.c2
trace the robot temporarily loses the trash can but eventually gets back on track. The bottom
figure shows a successful run when the trash can is in position D, which is initially unobservable
to the robot.
S. MAHADEVAN, G. THEOCHAROUS, N. KHALEELI
generally less than 10). By contrast, in our work as well as in the ALVINN system,
the input data has several hundred real-valued input variables, making it difficult
to employ a top decision-tree type approach. The advantage, however, of using
decision trees is that the learned knowledge can be easily converted into symbolic
rules, a process that is much more difficult to do in the case of a neural net.
Symbolic learning methods have also been investigated for sensory concept learn-
ing. Klingspor et al. [12] describe a relational learning algorithm called GRDT,
which infers a symbolic concept description (e.g. the concept thru door) by generalizing
user labeled training instances of a sequence of sensor values. A hypothesis
space bias is specified by the user in the form of a grammar, which restrict possible
generalizations. A strength of the GRDT algorithm is that it can learn hierarchical
concept descriptions. However, a weakness of this approach is that it relies on using
a logical description of the overall control strategy (as opposed to using a procedural
reactive/declarative structure). Logical representations incur a computational
cost in actual use, and their effectiveness in actual real-time robotics applications
has not been encouraging.
7.

Summary

This paper investigates how mobile robots can acquire useful sensory concepts from
high-dimensional and noisy training data. The paper proposes two strategies for
speeding up learning, based on decomposing the sensory input space, and learning
multiple concepts simultaneously using a shared representation. The effectiveness
of these strategies was studied in two tasks: learning feature detectors for probabilistic
navigation and learning to recognize visual objects for recycling. A detailed
experimental study was carried out using a Nomad 200 real robot testbed called
PAVLOV. The results suggest that the strategies provide sufficient bias to make it
feasible to learn high-dimensional concepts from limited training data.

Acknowledgements

This research was supported in part by an NSF CAREER award grant No. IRI-
9501852. The authors wish to acknowledge the support of the University of South
Florida Computer Science and Engineering department, where much of this research
was conducted. We thank Lynn Ryan for her detailed comments on a draft of this
paper.
Notes
1. PAVLOV is an acronym for Programmable Autonomous Vehicle for Learning Optimal Values.
2. In actuality, the state estimation procedure is more complex since we use an event-based semi-Markov
model to represent temporally extended actions. However, for the purposes of this
paper, we are simplifying the presentation.
RAPID CONCEPT LEARNING FOR MOBILE ROBOTS 21
3. Bias is generally defined as any criterion for selecting one generalization over another, other
than strict consistency with the training set. It is easy to show that bias-free learning is
impossible, and would amount to rote learning.
4. Although we do not discuss the details here, the robot employs a further processing phase to
extract the rough geometrical aligment of the trash can opening in order to drop items inside
it.



--R

Navigating Mobile Robots.
A robust layered control system for a mobile robot.
Multitask learning: A knowledge-based source of inductive bias
Acting under uncertainty: Discrete bayesian models for mobile-robot navigation
Robot Learning.
Introduction to the special issue on learning autonomous robots.
Robotics in Service.

Robot Learning.
Fundamentals of Digital Image Processing.
A robust robot navigation architecture using partially observable semi-markov decision processes
Learning concepts from sensor data.
A robot navigation architecture based on partially observable markov decision process models.
Fast vision-guided mobile robot navigation using model-based reasoning and prediction of uncertainties


Machine learning for robots: A comparison of different paradigms.
Mobile robot navigation using discrete-event markov decision process models

Machine Learning.
An office-navigating robot
Neural network based autonomous navigation.
Induction of decision trees.
Inductive learning from preclassified training examples.
Reinforcement Learning: An Introduction.

Learning one more thing.
--TR

--CTR
B. L. Boada , D. Blanco , L. Moreno, Symbolic Place Recognition in Voronoi-Based Maps by Using Hidden Markov Models, Journal of Intelligent and Robotic Systems, v.39 n.2, p.173-197, February 2004
robot learning;neural networks;concept learning