Neural Architectures for Robot Intelligence

by   H. Ritter, et al.

We argue that the direct experimental approaches to elucidate the architecture of higher brains may benefit from insights gained from exploring the possibilities and limits of artificial control architectures for robot systems. We present some of our recent work that has been motivated by that view and that is centered around the study of various aspects of hand actions since these are intimately linked with many higher cognitive abilities. As examples, we report on the development of a modular system for the recognition of continuous hand postures based on neural nets, the use of vision and tactile sensing for guiding prehensile movements of a multifingered hand, and the recognition and use of hand gestures for robot teaching. Regarding the issue of learning, we propose to view real-world learning from the perspective of data mining and to focus more strongly on the imitation of observed actions instead of purely reinforcement-based exploration. As a concrete example of such an effort we report on the status of an ongoing project in our lab in which a robot equipped with an attention system with a neurally inspired architecture is taught actions by using hand gestures in conjunction with speech commands. We point out some of the lessons learnt from this system, and discuss how systems of this kind can contribute to the study of issues at the junction between natural and artificial cognitive systems.



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1 The challenge of brain architecture

Much of brain research in the past and in the present has – often dictated by experimental constraints – emphasized a bottom up approach in which the properties of individual neurons or even synapses are in the center. Recent years have seen the advent of methods that can significantly help to complement this with a more top-down approach.

We may argue that the work of recent years and decades has now accumulated to the point where we may form quite reasonable guesses about the computational contribution of quite a substantial number of brain areas. While a more detailed elucidation of the exact nature of these computations may still need a long time of further research, we may nevertheless have the hope that many of these details are not decisive for the operation of the overall system.

In support of this view are the results of recent studies of brain organization that have revealed a rather tight interconnectivity of the majority of brain centers [49, 32, 50], making it likely that it is the structure at this level that shapes system properties much more than the details in the modules themselves. Fig. 1 from [50] depicts a view of the intermodule-connectivity in cat brain. Nodes indicate major processing centers and each line represents a major interconnection bundle. The technique of multidimensional scaling has been used to depict processing centers at positions in such a way that their spatial distance reflect their degree of their interconnectedness. Diagrams like this can summarize huge amounts of anatomical data and give us a ”bird’s eye view” on brain architecture.

Figure 1: Multidimensional scaling plot of connectivity between functional areas in the cat brain (from [50]): functional areas are indicated by labelled nodes, major connectivity bundles by lines. The technique of multidimensional scaling has been used to map brain centers to node positions in such a way that spatial proximity between nodes reflects a high degree of mutual interconnectivity.

In fact, from technical control systems it is well-known that heavy feedback connections can almost entirely determine the dynamic system behavior, effectively “disguising” almost all dynamical details (and imperfections!) that may be exhibited if the modules would operate in open loop mode.

Therefore, even rather crude approximations to the different brain module functions may give us a chance to gain significant insights into the functioning of the overall system, provided we get the architectural level right. If this view is correct, a significant part of the challenge will be the exploration of possible architectural patterns that could organize the computational contributions of a collection of sensor-, motor- and memory modules into a coherent sensori-motor-processing activity.

While controlled experimental alterations of actual brain architectures are a very difficult – and at least in higher animals ethically highly objectionable – task, the domain of robotics offers much wider experimental possibilities: current technology has matured to the point where we can (admittedly only very coarsely in many cases) approximate a reasonable spectrum of isolated perceptual, memory, and motor capabilities, allowing us now to explore architectures for the integration of these functions into artificial cognitive systems.

Behind such an approach is the expectation that the computational architecture of brains is to a large extent shaped by the computational demands of the tasks that they solve (certainly, there will be other significant factors, such as the available kind of “hardware”; however, at least in the technical domain the experience is that software can compensate for a wide range of different hardware characteristics).

If this is indeed the case, it will be important to explore computational architectures primarily for those tasks that are likely to be typical for what brains have to solve. From an evolutionary perspective, one likely candidate is navigation, which is one of the most basic behaviors that contributes to the capabilities of an animal. However, the example of insect navigation also shows that the evolution of cognition apparently has been driven by demands that are additional to those that gave rise to the ability of navigation alone.

2 Hands and Actions

It has been argued that the development of cognition is closely linked with the capability to purposively act on one’s environment and to cause changes to it [44]. Therefore, we may expect that the need to control sophisticated manipulators, particularly in the form of arms and hands (for many animals, also the mouth will play the role of an important “mani”pulator) will be a major driving force and shaping factor for the cognitive processing architecture of a brain. In fact, a closer look reveals that in particular the control of hands in the human is connected with a large number of highly demanding and in many ways generic information processing tasks [48] whose coordination already forms a major base for intelligent behavior.

Regarding perception, a first major issue is the visual recognition of hands and hand actions. Since hands are a major focus of action, we – but also other higher primates – are very good at the visual recognition of hands, their motions as well as their highly variable postures and – at an even higher level – their actions. Most interestingly, such recognition tasks were found to be correlated with the activity of neurons (located in the superior temporal sulcus) that show rather selective responses to visually perceived hand actions [35], see Fig. 2. Therefore, the task of recognition of hands and their actions is a very suitable starting place to investigate a minimal set of demands on the architecture of a visual system, and in Sec. 3 we will report on ongoing work in our lab towards the goal of visually based recognition of hand actions.

Figure 2: Selective neuron response to visually perceived hand actions (from [35]). The bottom row shows the associated activity trace of a neuron (the same for all trials) in the STS (superior temporal sulcus) of a macaque when the animal observes the action sequence (1 s duration, indicated by calibration bar at the bottom) depicted in the corresponding column above. Apparently, the recorded neuron responds very selectively to the interaction between hand and fur (leftmost column) and is silent in the remaining, visually very similar trials.

Touch is another modality closely linked with our hands. The haptic perception of objects is highly developed in humans and it is known that some of the initial processing steps in somatosensory cortex (edge filtering etc.) resembles visual processing in V1 [22]. However, there are also significant differences: haptic perception is usually closely coupled with finger movements during which – unlike the retinal surface in our eyes – the skin of our fingers undergoes complex changes in its three-dimensional arrangement in space and relative to the object surfaces. From a computational point of view, this poses a formidable challenge which no doubt can only be addressed at the level of an architecture that is suitable to coordinate the operation of tactile sensors and active finger control. In recent years, we have developed finger tip force sensors [21] that provide at least some rudimentary touch force information for the finger tips of a three fingered hand and in Sec. 4 we will briefly report how this research can help to approach issues such as how to control finger synergies for grasping and fusion of touch with other sensory modalities, such as vision.

Finally, there is the issue of carrying out hand actions themselves. This increases the demands on the underlying architecture even further: besides the integration of perception and action we have as additional elements the need for some state and context memory as well as planning. The neural structures involved in these stages appear to be farther removed from direct sensory input or motor output and include areas such as the parietal cortex, the premotor and the supplementory motor area. To carry out their task, these systems must operate on highly preprocessed representations that reflect important invariants at the task level, such as holding an object, aligning it with another one or controlling a constrained movement, such as screwing a nut onto a screw.

At the same time, the operation of these structures exhibits an enormous flexibility and it is only the ability of learning that can make possible and account for the enormous range of human manual activities. Therefore, a major challenge at this architectural level is to understand how neural adaptivity at the lower system levels can manifest itself as a progressive shaping of the interactions among neural modules in the large. In other words, we have to understand what principles are required to realize a “learning architecture” in which a substantial number of coarsely adapted skills can become coordinated in different ways, such as to allow goal-directed sequences of manual actions.

Research on unsupervised reinforcement learning algorithms has tried to address this issue, however, with limited success. While there have been some remarkable demonstrations of unsupervised skill learning on simple tasks, the learning of more complex tasks usually leads to exponentially growing learning times and quickly becomes unrealistic even for tasks of moderate complexity

[24]. Additionally, the success of these demonstrations usually relies on often rather ingeniously encoded input data. Typical real world learning situations differ markedly from this: they are usually characterized by extremely high-dimensional sensory input, such as vision or touch, and it is part of the challenge for the learning system to find out which regularities in the input can guide successful execution of the intended task.

The task of detecting such regularities shares many issues with the still rather young field of datamining: in datamining, the goal is to detect patterns and regularities in often huge amounts of data in order to support decisions, make predictions or improve the control of some industrial process [11, 1]. In a way, modern datamining systems can be seen as the analogous endeavor carried through by nature when evolving brains: to endow a company, similar to an organism, with sensors and perceptual capabilities to exploit useful regularities in its surround in order to improve its fitness. While brains have been evolved for the processing of sensory signals we are largely familiar with, datamining techniques attempt to imitate similar capabilities – although at a currently much lower level – for data from artificial, man-made domains, such as economic systems, medical databases, industrial processes, or the internet.

Therefore, it is not too surprising that many methods in the field of datamining are closely related to the mathematics that underlie many models of brain operation. To exploit these analogies may be fruitful for both sides: brain modeling may benefit from the rapid progress of current datamining methods, and insight into brain mechanisms may inspire new datamining techniques.

However, it seems doubtful that this alone is sufficient to realize a high-level learning architecture. Even the most sophisticated machine learning methods are unlikely to remove the “curse of dimensionality” that limits the possibilities of unsupervised learning in high-dimensional spaces. Therefore, it appears to us that the only promising way towards high-level learning is to detect the required high-level structures in the input data themselves, instead of trying to completely re-construct them by search in a high-dimensional search space.

An attractive approach along these lines – and again intimately connected with the observation and interpretation of hand actions – is offered by the paradigm of imitation learning [25, 2]

. Watching an action sequence offers a learner the chance to strongly reduce the search space he or she is confronted with and to focus exploration to a more or less narrow neighborhood of a successful example of the to-be-learned skill. To make this feasible, however, requires to identify essential elements of the action in the sensory stream and to remap them into the context of the observer. The first step requires a highly developed vision capability (possibly supported by acoustic perception), with the ability to detect relations in image sequences. The second step requires a matching of the observed situation to one’s own context; this goes well beyond a simple geometric transformation, since the involved objects and relationships between them usually will only be analogous but not identical to the situation in which the learner wants to operate. Therefore, the approach of imitation learning can only be implemented on top of a rather highly developed base of sensory preprocessing that “opens the door” to detect useful regularities in a complex world. It may turn out that this is the price to pay for making learning feasible and that approaches that try to solve “the” learning problem by “more intelligent” search strategies alone are doomed to fail when it comes to the complexity of real world tasks, since they address the much harder problem of “invention” as compared to “imitation”.

3 Neural net-based hand posture recognition

Recognition of 3D hand postures constitutes one of the complex tasks that are routinely and effortlessly carried out by our brains. In contrast to most everyday objects, our hands are deformable objects, characterized by about 20 continuous degrees of freedom (33 muscles actuating 20 joints). The associated, very large configuration space makes the recognition of continuous hand postures a much harder problem as, e.g., classification of only a set of discrete hand configurations. As a result, robust and accurate recognition of hand postures requires to address many issues of more general vision systems. Still, this task is sufficiently circumscribed to make the implementation of a complete recognition architecture feasible. In the following, we will describe the architecture of a hierarchical recognition system that employs several artificial neural nets in order to extract from monocular video images of a human hand an explicit representation of the three-dimensional hand posture so that a computer-rendered articulated model of the hand can follow the posture of the viewed human hand (Fig.


Figure 3: Task of artificial neural recognition system: extract from 2D monocular video image of a human hand (left) its 3D shape in order to permit tracking of the ”seen” hand posture by a computer rendered articulated hand model (right).

In line with our previous remarks, the described recognition system (for more details, see [33]) does not attempt to follow any biological detail. Instead, it is meant to explore a particular processing architecture whose modules coarsely mimic some of the principles that are thought to underlie processing in the visual system. This allows then to study the significance of these principles for the processing properties at the system level, such as the contribution of multiple processing streams to achieve better robustness, or the use of a hierarchical coarse-to-fine strategy to focus on important parts of the image.

Figure 4: Schematic arrangement of gabor jets in image plane (left) and spatial profile of two different gabor functions (right).

As a first step, the pixel image is transformed into a lower-dimensional feature vector of “neural activities”. We use a discrete grid of image positions and position at each such location a number of “receptive fields” of formal neurons that are chosen as Gabor functions with different resolution and orientation (see Fig.


The choice of these functions is motivated by the observation of Gabor-like response characteristics in visual neurons ([7]) and also by favorable mathematical properties of Gabor functions to capture important local image information.

The resulting activity pattern provides an initial, “holistic” input representation of the image. While we have studied the extraction of hand posture directly from such holistic activity patterns by means of neural learning algorithms [31], we found that such a simple “single-stage architecture” is rather limited in the achievable accuracy of hand posture identification.

Figure 5: Two-level processing hierarchy for determining finger tip location: for each finger, a lower level network determines a “focus region” in which an upper level network attempts to determine the finger tip location.

Experience with that approach has led us to the introduction of two additional ingredients into our vision architecture: instead of a direct computation of the final hand posture, we attempt to first extract from the holistic input representation a set of meaningful and stable object features which we chose in the present task to be the 2D finger tip locations in the image.

the solution of this subtask is not attempted in a single step; instead, we use a processing hierarchy in which a neural network operating on the holistic input representation first computes a coarse estimate of the centers of up to five image subregions (one per finger) where the finger tips should be located. Subsequently, and on the second level of the processing hierarchy, we provide for each finger subregion an extra network, applying an analogous processing as on the first stage, but entirely focused on the selected finger subregion that was identified by the first, “global” network (i.e., any visual input outside this region is clipped), and using Gabor functions at a correspondingly reduced length scale (Fig.


Each network is itself modular and combines characteristics of a fully localized representation (feature selective cells sharply tuned to different pattern prototypes: “grandmother cells”) and a fully distributed representation (broadly tuned feature selective cells such that only the combination of many activities is meaningful).

Figure 6: Left:

Local Linear Map (LLM) networks for supervised learning. Each locally valid linear map is implemented as a linear perceptron (upper layer) that is activated only for a subregion of the input space. The subregions are defined by a layer (bottom) of competitive “gating neurons”.


PCA analysers for unsupervised classification. Each PCA analyser is valid only for a subregion of the input space and orients its axes along the directions of maximal variance of the data distribution in its subregion.

Figure 7: Accuracy of finger tip detection without use of hierarchy (global nets only: rows 1 and 3) and improvement through two-level hierarchy (+local nets: rows 2 and 4).

The underlying mathematical principle of these networks is the representation of an input-output mapping through a combination of local linear maps in the case of supervised learning (Fig. 6 (left)), or the representation of a stimulus density by a collection of local principal component analyzers in the case of unsupervised learning (Fig. 6 (right)).

Both situations share the idea of using a layer of “competitive” neurons (the “grandmother” cells) to tessellate the feature space into a number of smaller and thus more manageable subregions and to employ within each subregion a locally linear representation (local perceptron-type or PCA-type mapping, employing a local subnetwork of formal neurons). This type of approach has become very popular during recent years and we have considered suitable training algorithms in more detail elsewhere [29]. For the present situation, the required training data consist of a sufficient number (several hundred) of monochrome hand images for which the finger tip -locations must be explicitly provided (e.g., labeled by a human). The first (“global”) network is directly trained with these data; subsequently, each network of the second stage is then trained with the image subregions that are identified by the trained global network of the first stage (again using the associated

-pairs as target output values). Fig.

7 shows for six typical hand postures the extracted finger tip locations obtained from the global network alone (rows 1 and 3) and the improved locations after including the corrections by the local networks (rows 2 and 4).

Figure 8: Pixelwise multiplication of different filter results to integrate different processing streams (for details see text).
Figure 9:

PSOM-based mapping from fingertip positions to hand postures. The bottom rectangle coincides with the viewing area enclosing hand shapes, the vertical axis denotes image depth. Each fan-like grid structure belongs to one finger tip and indicates a set of 4x4 finger tip positions traversed when independently varying the two joint parameters (per finger) over 4 discrete values. The resulting point set is used as training data for the PSOM network that achieves a smooth interpolation through the depicted fan-like structures, thereby yielding a smooth relationship between (observable) finger tip positions in the viewing (=bottom) plane and (unobservable) finger tip depth (vertical axis) as well as associated joint parameters ( interpolated fan-grid coordinates).

To further increase the accuracy of the obtained finger tip locations we employ another architectural principle known to exist in the brain: the fusion of multiple processing channels [45]. For the present task, we fuse three different processing “streams”: the first stream is obtained by a Gaussian activity profile that is centered at the finger tip location determined by the 2nd stage detection network for that finger. This activity profile represents a confidence measure for the presence of the corresponding finger tip that achieves its maximum at the estimated position, but decays smoothly as one moves away from that point.

The second processing stream is motivated by the observation that (for the considered hand orientation) finger tips tend to be located at intensity edges. Thus, we compute the second processing stream as the edge image of the input distribution. Fig. 8, 2nd column shows the product of the first two processing streams, which is an intensity modulated edge with maximum modulation at the point of closest approach to the finger tip position as estimated from the network.

We convolve the input image with a simple “finger tip filter” that consists of a 5x5 pixel template of a typical “finger tip edge”, i.e., a short vertical line with a two-sided (to take care of right and left pointing directions) rounding at both ends (i.e., the template resembles a ”)(”-figure, with the middle parts of the brackets coalesced into a common vertical edge) to obtain a 3rd processing stream (Fig. 8, 3rd column).

All three processing streams are normalized to yield images with confidence values in between 0 and 1 and are then pixel-wise multiplied to obtain the final result (Fig. 8

, rightmost column). The two highest values in the result image are then used as the most probable position candidate and an alternative position (large and small squares in Fig.


In the final step, the obtained 2D features (finger tip locations) are used to identify the 3D hand posture. There is a large debate to what extent our vision system actually achieves a 3D reconstruction of perceived objects (see, e.g., [36] for a survey). While there are many arguments that the available intensity pattern of most real world images is subject to too many degradations to allow a full and general 3D shape reconstruction by local algorithms (“shape from X” approaches), it seems that the visual system can very efficiently use the available 2D views as an “index” into a 3D shape memory that then provides access to 3D object properties. While little is known about the precise nature of such a shape memory, on a more general level it can be viewed as providing a set of shape models that provide sufficient constraints to make the available 2D information interpretable in 3D.

Within our architecture, the available 2D information consists of five coordinate pairs , , one for each finger. Obviously, this is insufficient for a 3D-reconstruction even of the finger tip positions alone (15 degrees of freedom), let alone of the entire hand posture. However, in real hand postures finger joints are highly correlated so that the available degrees of freedom are by far not fully used. For instance, the angles of the last two joints in each finger fulfill for most hand postures approximately the relation since they are driven by the same tendon. Another simplification is to equate the joint angles and that determine the flexion of the fingers. While this constraint does not exactly correspond to the situation of the human hand it is a good approximation. With these constraints, we can represent each hand posture with 10 parameters, which is the same number of parameters as available with the five 2D coordinate pairs and which, therefore, can be identified from the 2D data alone.

While the required mapping from finger joints to image locations is rather straightforward and can be analytically computed, our system requires the inverse transformation, which is much harder and cannot be given in closed form. Therefore, we again use a learning approach at this processing stage. For each finger, we train a Parameterized Self-organizing Map

(PSOM), using data from the analytically computable forward transform. The PSOM is a generalization of the well-known Self-organizing map (SOM). It replaces the discrete lattice of the SOM with a continuous manifold, combined with the very useful property that it makes available with each learned mapping automatically also the associated inverse (for details, cf.

[47]). Fig. 9 illustrates the PSOM-generated mapping from the 2D-Fingertip positions to the joint-angle space.

The development of this system provided us with many insights into the usefulness of biologically motivated processing principles at the architectural level of a (still small) vision system. Fig. 10 gives an impression of the system’s accuracy. In contrast to most other systems with a similar objective, the present approach can work without special hand markers and makes extensive use of learning at several processing levels.

Figure 10: Reconstruction results for hand postures with the described system.

4 Tactile sensing for robot hand control

Touch is another very important sensory cue that informs us what our hands do. Although the initial processing steps in the somatosensory cortex seem to resemble cortical processing in the visual areas (extraction of local features, such as edges and their motion)[22], one major difference is that the acquisition of a “tactile perception” of an object is a much more interactive process than its visual counterpart. In the case of our hands, it is intimately connected with a sophisticated control of the mechanical interaction between their shape-variable sensory surface and the grasped object. This makes the establishment of computational models an even bigger challenge than in vision. At the same time, the gulf between the tactile sensing of our hands and current technical solutions is much larger than in the case of vision, where rather good camera sensors are available.

Figure 11: Three-fingered hydraulically operated hand for dextrous grasping.

However, its active nature makes the understanding of haptic perception a key issue for insights into how the brain controls action. One way to approach this issue with technical robot systems is to study the control of multifingered robot hands for haptic discrimination and manipulation of objects. Fig. 11 shows a three-fingered robot hand developed at the Technical University of Munich [30] that we have used to explore some of the issues involved. Each finger is approximately of the size of a human finger and can be moved about four joints with a total of three degrees of freedom (two distal bending joints are coupled, as described in Sec. 3 for the virtual hand model employed there). The joints are actuated hydraulically, which allows a fast and forceful movement control but at the price of hysteresis effects introduced by friction within the hydraulic cylinders of the actuators. This prevents the implementation of a reliable joint angle control purely based on oil pressure data from the actuators and makes additional tactile and force sensing an even more important necessity.

Figure 12: Fingertip sensor for detection of pressure and slipping (from [21])

To equip this hand with some basic tactile sensing, we have developed miniaturized finger tip sensors that allow to measure for each finger tip the force vector caused by an object contact. An early prototype of the sensors [21] used two types of pressure sensitive foils to mimic the tonic and phasic responses of the skin that are used to control a static force and to react to rapid force changes indicating, e.g., object slipping (Fig.12). Based on experiences with this prototype we succeeded in a subsequent, simplified sensor design (visible in Fig.11) that was much easier to fabricate and to maintain and that allowed to obtain both tonic and phasic signals with the use of a single sensor material only [20].

Obviously, the information gained from these sensors is much more limited than that provided by a tactile skin covering the entire hand. However, we have seen in the case of the artificial vision system that the information of finger tip locations in the visual image carries already a surprisingly large part of the information about the 3D hand posture. Similarly, we may hope that the knowledge of the finger tip forces can provide us with an important part of the information necessary to control grasping actions.

Figure 13: Control architecture for finger control of the TUM Hand.

Following this assumption we have implemented a control architecture for basic finger synergies that are involved in dextrous grasping (Fig. 13). The control is based on the minimization of an error function that is formed from a weighted sum of error signals in different sensory modalities: a first set of inputs is provided by the force measurements at the finger tips. A second set of inputs is provided from the oil pressures in the hydraulic actuators. Finally, a third set of inputs encodes the oil piston positions in the actuator unit. By specifying suitable sets of weighting coefficients for the error contributions we can endow the fingers with different types of reactive behavior. Switching between such behaviors is achieved with a finite state automaton whose state transitions become triggered either by sensory events or by top-down signals and whose states activate the weighting coefficients for a particular finger behavior.

With this scheme we have implemented several simple grasp primitives, such as preshaping the hand for grasping an object, closing the fingers until object contact, maintaining a target force level when holding an object and reacting with a preset compliance when a human tries to take the object away.

These primitives permit the robust grasping of “roundish” objects; however, the tactile discrimination of the finger tip sensors is insufficient to permit automatic grasps of more complex objects. As a remedy, we have extended the sensing system in a “non-biological” way by adding a hand camera that evaluates a close view of the object in order to orient the fingers appropriately for a successful grasp. During grasping itself, this setup permits the combined use of tactile information from the finger tip sensors with visual information from the camera. For initial results on the integration of these two sensory channels to judge grasp reliability, see, e.g., [16].

5 Issues of ”Real World Learning”

Although particularly the described hand posture recognition system relies heavily on learning algorithms to attain its capabilities, we think that learning in this as well as in many similar artificial systems still differs very much from what biological nervous systems do. We do not suspect that the main reason for this is that the “microscopic” learning rules operating at the level of the formal neurons are too far off the mark; they are grounded in well-established principles such as Hebbian learning and error correction and we would expect more sophisticated learning rules of the future to differ in detail but not so much in principle. However, we do suspect that the main difference lies at the level of the architecture that organizes how and what the different modules learn and what information they have available.

A first and major difference with biological systems is that learning is not really integrated with the operation of the system. Instead, the training phase is separate and training occurs at the level of the system modules, not at the level of the system as a whole. Both features are rather typical for most current approaches to exploit learning for systems that are comprised of several adaptive modules.

A second difference is the need for carefully prepared training data sets in which input and target values typically must be provided in well-defined positions of a data vector (e.g., an array of 2D finger tip positions for the first and a vector of joint angles for the second stage of the present system).

These differences make learning still rather artificial and limited. Real world situations offer no nice separation of operation and training phase; they also offer no nicely labeled training data vectors and they give even less opportunity for “system surgery” to permit training of individual modules.

We think that the sketched problems reflect the lack of a good architecture that specifically supports learning. While the issue of training “inner” (or “hidden”) modules has been addressed quite forcefully in the context of the multilayer perceptron type approaches, this approach has faced difficulties with the scaling issues discussed earlier; it is also well known that multilayer perceptrons are vulnerable to “catastrophic interference” when used for incremental learning, a difficulty that is exhibited to a much lesser extent by other, more “local” network models, such as radial basis functions, local linear maps or other kernel based approaches, such as the recently very popular support vector machine


. On the other hand, these models usually lack the elegant scheme for backpropagating training signals through entire modules, which made the multilayer perceptron so attractive for the construction of modular systems. However, it is well known that each backpropagation step leads to a significant attenuation of the training signal, so that this technique again tends to be limited to shallow architectures that seldom possess more than four hidden layers. To circumvent these difficulties, researchers have developed an arsenal of various, often rather heuristic “tricks”

[34]; other important strategies have been a combination of training and incremental network construction, such as the cascade correlation architecture [10] that allows also the use of modules that offer no error backpropagation mechanism [26]. However, all these approaches can in our view at best provide a partial solution to the above problems.

We suspect that a more severe reason for the limitations of current learning approaches is an undue emphasis of the view of learning as a task of identifying an unknown input-output mapping, assuming input and target values as given. It is this latter assumption that is almost totally unfulfilled in most real world learning situations. The paradigm of reinforcement learning [43] tries to address this issue by weakening the requirements on the available training information to the extent that the learning system is only informed (e.g., by a scalar “reinforcement value”) about relative success or failure. This leads to conditions that can be met in many real world situations; at the same time the now greatly impoverished conditions to acquire useful information make the learning task much harder. This has prevented reinforcement learning to scale to situations of realistic complexity, with the exception of a select number of cases where ingenious encoding of task variables has succeeded to focus the search of the reinforcement learning algorithm on the most promising part of the state space from the outset [27].

In our view, the more promising way to lift learning to the complexity of real world situations is not to impoverish the conditions for information acquisition, as in the paradigm of reinforcement learning, but instead to put very large efforts into gaining as rich information from the environment as possible. Such an approach also seems to be much better in line with what we see in living brains which obviously devote a large proportion of their processing capacity to extract useful regularities from a rich spectrum of sensory inputs and to actively coordinate the available sensors and processing resources in order to optimize this process in various ways. Such optimization might also require a combined use of supervised and unsupervised learning strategies, and it has recently been suggested [8] that the subdivision of the brain into the neocortex, the cerebellum and the basal ganglia might reflect an architecture, in which these three structures provide the substrate for unsupervised, supervised and reinforcement learning, respectively.

In the following section we argue that the main task of such a system, the extraction of useful regularities from the environment, shares many goals and computational issues with the field of datamining and suggest to consider the realization of more powerful learning architectures from a datamining perspective.

6 A datamining perspective of learning

Datamining is a field that has emerged in the last decade in response to the needs created by the explosive growth of data acquired in many fields of science, but also in finance, business enterprises, communication networks and other areas of daily life [11, 1]. Automated data analysis in such situations has become one of the main challenges for the future since the explosive growth of our data acquisition abilities seems to face no imminent limit. In particular in the business and finance sectors, the early detection of important trends or regularities in the acquired data can be of vital importance for the survival of a company. Yet, the explosive growth of our data acquisition abilities rules out any solution that relies on human inspection. Therefore, companies but also scientists find themselves in a position where they have to develop tools that can autonomously process large collections of data looking for patterns and regularities that can often only be vaguely characterized in advance of their detection.

This situation is very analogous to that faced by the brains of higher animals: they, too, are connected to a huge number of sensors that continuously acquire raw data at a very high rate. Again, the relevant amount of information in these sensory data constitutes only a tiny fraction of their total volume and is most often encoded in subtle patterns that must be detected against a massive background of noise and irrelevant signal variability. In the case of living organisms the extremely high survival value of, e.g., recognizing a predator early even if highly masked in a complex visual or acoustic background is obvious and we are highly impressed by the superb solutions crafted by natural evolution in response to these needs. While our own vision of the world has been created by the same process and imposes on our imagination a strong bias as to what the world “is”, we are aware that our own perception is just a species-specific solution of extracting from a particular combination of sensory data streams behaviorally relevant regularities that are mediated to us in categories that are so deeply engrained into our conscious existence that we have the greatest difficulty of imagining anything beyond the “rendering result” of our own, species specific solution. Yet we see that brains of other species are connected to sensors that provide them with access to sensory dimensions totally alien to us, such as ultrasonic reflections, electric and magnetic field lines, or polarization of light, to name a few of the cases that have attracted considerable research.

Therefore, we think that much of the activity of brains is rather aptly characterized as a sophisticated form of datamining evolved by nature: the rapid and highly performant extraction of even subtle regularities from huge amounts of raw sensory “data” and their representation in stable entities that are suitable as a basis for rapid decisions about reactions involving the comparably low-dimensional motor apparatus of an organism.

From this perspective, the task of developing a datamining system might be best viewed as the task of building an artificial brain for a sensory domain that does not occur in nature but in the domain of the particular application. Conversely, one may expect that the search for deeper insights into processing strategies of real brains might benefit from experiences and methods developed in the field of datamining.

In fact, there are already many points of contact visible between methods employed in datamining and brain modeling [38]

: One major task in both fields is the need for dimension reduction as a first step to cope with the high bandwidth of incoming data. This is reflected in the high amount of attention devoted in both fields to methods such as principal component analysis (PCA), independent component analysis (ICA), as well as their nonlinear generalizations, such as self-organizing maps or autoassociator-networks. Another important issue in both domains is the identification of regularities and structures in data of various kinds, putting cluster algorithms, prototype formation and identification of manifolds into a shared focus. Further closely related issues are model extraction, classification and prediction, for which we have seen the development of neural network based classifiers, non-linear regression and, more recently, the support vector machine approach whose neurobiological significance still awaits clarification.

It is hard to believe that the prominent presence of these methods in the two fields is largely coincidental. Instead, we think that this situation reflects a rather tight relationship between tasks in both fields that should be exploited in future research. In particular, experience from our current work shows us that the realization of more powerful learning architectures can benefit from such a program in at least two ways:

1. Datamining can contribute many techniques that are well-suited to create a first layer of representations that reflect important regularities and invariances of the environment and that offer more stable features than the sensory data themselves. In the described hand posture recognition system we have created this layer still by design. Its automatic and data-driven construction is an essential step to avoid the limiting need for manually prepared training data. We were already able to carry out this program to a considerable extent in a somewhat different domain of discrete object classification [15, 14], using a combination of PCA and clustering, and are currently working towards extending these techniques to include cases involving continuous manifolds.

2. Methods for data visualization and – more recently – data sonification

[17, 18] can help to summarize the inner state of a complex learning system in various compact ways to facilitate monitoring of the learning process for a human observer. While this does not directly aid the learning process itself, it provides the researcher with additional “windows” into the learning dynamics that can be very helpful to gain insights into the interactions that may occur among learning modules. So far, we have explored this approach for still rather simple situations [19]; our next target is to apply this method to the gesture instructed robot system described in the following sections.

However, to view learning from a datamining perspective alone cannot make disappear the “curse of dimensionality” that plagues any approach that attempts to learn everything from autonomous and unsupervised exploration. To overcome this problem requires to offer the learner more useful information than reinforcement alone can provide [40]. In the next section we will argue that the paradigm of imitiation learning appears very promising to fill this need.

7 Learning as imitation of actions

Learning from a datamining perspective alone still would confront the learner with the formidable task of having to make a large number of difficult “discoveries” in a huge search space. Everyday experience suggests that at least human learning is rather different: while we occasionally learn new things by pure exploration and discovery (which then usually takes rather long), we acquire a much larger number of skills by imitation of observed, successful actions of others. While such “imitation learning” still is highly non-trivial for reasons more fully pointed out below, it provides the learner from the outset with much richer information that is suitable to narrow down search spaces considerably so that the more general, but weaker learning strategies of unsupervised or reinforcement learning can provide the now much smaller, missing information.

The term “imitation learning” might suggest that learning that follows this paradigm now becomes extremely easy. Unfortunately, this is by no means true. The reasons are at least threefold ([4, 28]): , usually, the observed action cannot just be copied “verbatim” but must instead be transformed and adapted for the learner’s situation that usually differs in several respects from that of the “model”. Related with this is the problem that the available input often provides only very partial information, e.g., in the case of a hand action the learner may be able to visually follow the spatio-temporal geometry of the hand movements, but will have no sensory access to the tactile sensations and the forces that accompany (and may be essential for) successful carrying out of the action. Finally, , the observed action is only available in the form of “low level” sensory inputs embedded in a usually complex background of sensory events that are not directly relevant to the action of interest. The learner has to do extensive preprocessing to extract from such input a more concise representation of the observed action that then can serve as a starting point for solving the remaining issues and .

Figure 14: Gestural control of robot actions in the GRAVIS system as a basis scenario towards imitation learning

To study these issues more concretely, we have chosen the scenario of gesture-based control of a multifingered robot hand to carry out pick-and-place operations on objects laying on a table and indicated by manual pointing gestures of a human instructor (Fig 14). While this does not yet follow exactly the paradigm of imitation learning (the robot does not imitate the gestures of the human instructor; instead it visually observes the gestures and then carries out actions indicated by them) this scenario shares many issues that are central to imitiation learning and the developed solutions will then serve as a sound basis that makes proper imitation learning feasable in a next step.

For the realization of GRAVIS (Gestural Recognition Active VIsion System robot[42]) we had to complement the so far described functional modules for hand posture recognition (of which the system only contains a strongly simplified version) and finger control by a significant number of further modules for subtasks such as performing various coordinate transformations (to address ), robot arm and manipulator control (augmenting the approaches sketched in Sec. 4 to address ) and object recognition (contributing to issues ). In the following section, we will describe the GRAVIS system more fully. However, for lack of space we will not go into any details about the involved modules; instead we will concentrate the discussion on the architecture level, in particular, on the attention mechanism that allows the system to focus its processing resources on those parts of the input that are likely to contain useful information for following the instructor’s actions.

8 Gesture based control of robot actions

GRAVIS central task is to watch a human instructor for commands that it then will carry out. GRAVIS is the result of an ongoing, larger scale research effort (Bielefeld Special Collaborative Research Unit SFB 360 [37]) aiming towards the systematic investigation of principles needed to build artificial cognitive systems that can communicate with a human in an intuitive way, including the acquisition of new skills by learning.

As a result of our interest in the role of hands for understanding sensory-motor intelligence and its links with intuitive, demonstration-based communication, a major part of the development of GRAVIS is centered around the recognition, learning and carrying out of hand actions and gestures in the context of communication. Currently, GRAVIS is able to recognize three-dimensional pointing gestures of its instructor and to interprete them as commands to pick up objects laying around on a table and to deploy them at positions that again can be designated by pointing gestures.

Obviously, an important element for such a mode of communication is the establishment and continuous maintenance of a shared focus of attention between robot and human user [39]. Since the majority of robot work tasks are related to vision and geometry and in view of the important role of deictic and spatial gestures in this process, our robot is equipped with an active stereo camera system to enable the attention mechanism to shift its attention across a large portion of the visual scene.

Figure 15: The saccade subsystem integrates spatial aspects of the task by summation and multiplication of spatial feature maps.

For the implementation of the attention mechanism we use a layered system of topographically organized “neural” feature maps whose task is the integration of different low-level cues into a continually updated focus of attention (Fig. 15). This structure is loosely motivated by the current neurobiological picture about sensory integration mechanisms in the superior colliculus, which is responsible for targeting visual saccades. One attractive feature of this approach is its simple extensibility by additional layers, allowing a flexible integration of new feature types into the attention control mechanism. Similar approaches, however with fewer or less complex maps have been investigated also by [12] in the context of the COG project [5] or by [9, 3, 46].

In more detail, the vision system computes from the stereo images a number of feature maps indicating the presence of oriented edges, HSI-color saturation and intensity, motion (difference map), and skin color. As one of the main goals of the system is to recognize and track human hands, we multiply the difference map (indicating movement) by the skin segmentation map (indicating a hand). The result is a “moving skin” map, which is then treated as a feature map in its own right. A weighted sum of these feature maps is multiplied coordinate-wise with a further manipulation map and a fadeout-map to form the final attention map.

After a Gaussian smoothing to favor small saccades, the location of maximal activity in the attention map is used to define the next fixation point. The multiplicative nature of the manipulation map allows one to direct (or suppress) fixations into (or within) whole regions according to the availability of top-down information (cf. below). Finally, the fade-out map has the task to suppress activity in recently visited regions and also in areas that would command fixations that are incompatible with the joint limits of the camera head. However, in order to maintain interest in pointing gestures, the “moved skin” map is always additively superimposed to the fade-out map. As a last step, the location of maximal activity in the attention map is centered on the nearest object in its vicinity, and a stereo matching algorithm is used to estimate depth and to obtain a stereo correction that centers both cameras on the new fixation point [23].

Figure 16: A pointing gesture is evaluated in several steps, using skin segmentation (upper left) and mult-layer perceptrons to classify pointing (upper right). A corresponding restriction of attention to a region of interest is generated and projected on the table, here indicated by the square in the lower middle display. Other points of interest in the raw image (left) and after stereo matching (right) are further candidates for fixations in case any pointing information is available.

The weighted summation of the topographic maps provides also a very convenient way for top-down propagation of information from higher processing levels to the perceptual level. For instance, interaction with the human user can modify the attention map by two different mechanisms. If the hand and gesture recognition system detects a pointing gesture in the image, the 3D-direction of the pointing finger is computed and its 2D projection into the viewing plane is used to define a sector-shaped “pointing cone” emanating from the pointing finger into the scene. The pointing cone is represented as correspondingly localized activity in the “manipulation map” layer that then is multiplied point-wise with the attention map to restrict the explorative attention of the next step to the area of the pointing cone (Fig. 15). Additionally, if a spoken instruction references a colored object (“ … the red cube …”) the corresponding weight of the corresponding color map is increased to bias the attention system towards red spots in the image. This increases the probability for fixations on red blobs, but after some time a decay mechanism drives the weighting back to a default level.

While the attention system is a key component to coordinate the activity of the primarily visuo-spatially directed modules of the GRAVIS system, the entire task of gesture-controlled pick-and-place operations requires participation of a considerable number of further perceptual and motor basis skills. Fig. 17 provides a simplified overview of these and of their mutual interactions. Besides the already mentioned visuo-spatial modules for hand recognition, hand tracking, fingertip recognition, 3D pointing recognition and stereo matching, additional modules have to deal with the motor control of 3D-fixations of the stereo camera system, recognition of the target object and its orientation, a corresponding grasp choice and pre-shaping of the hand, the control of robot arm and finger movements, and, finally, the evaluation of the attained grasp based on feedback from the finger sensors.

To organize the control flow between the modules implementing these skills, which represent primarily motor-directed aspects of the robot’s behavior, we use finite-state automata that can activate or de-activate mutually exclusive behavior modules. Several of these modules are again implemented by using artificial neural networks, can adapt to changing characteristics of the task, and are realized in a hierarchical fashion similar to the hand recognition system described above.

Figure 17: The functional modules of the GRAVIS architecture, blocks depict software modules, circles hardware control, and arrows the (simplified) control flow. The dashed line shows the behavior sequence for object deployment starting from the rest-position in the lower left corner.

At the software level, the entire system is implemented as a larger number of separate processes running in parallel on several workstations and communicating with a distributed message passing communication system (DACS [13]) developed earlier for the purpose of this project.

At the hardware level, the current system comprises the already mentioned binocular camera head equipped with two color-cameras and motorized lenses [6] with a total of 10 DOF’s and the three-fingered robot hand described in Sec. 4. The hand is mounted on a PUMA 560 manipulator with six DOFs operated in real time and is additionally equipped with a wrist camera to view the end-effector region.

9 Discussion

Usually, systems of the complexity of the described GRAVIS system or beyond are research prototypes that are rather far from a portable product that can be easily set up in the same manner in a different laboratory. At the same time, these systems are still extremely simple from a biological point of view.

So an important issue to address is: what insights – beyond experience – can be gained from the construction of such prototypes?

A first answer to that question is that systems of the kind of GRAVIS allow us to experimentally explore the interactions of a collection of sensory, memory and motor skills in the context of real-world tasks instead of highly simplified and artificial toy problems that are not characteristic of the tasks brains were evolved for. Such experiments may give clues about processing strategies that might otherwise be very difficult to obtain.

For instance, the exploration behavior generated by the attention module tends to fixate repetitively upon the most interesting points, which are in most cases objects. Here we find a typical “emerging regularity” grounded in a perception-action loop which can for instance be used to establish a visual memory by temporal integration which stabilizes only the most salient points. Though in this case the regularity is rather obvious and detectable in the chain of 3D-fixations alone, the availability of a technical system that is open to any kind of “surgery” in conjunction with the datamining techniques discussed in Sec. 6 offers the chance to detect also more subtle patterns, implicit dependencies and mutual relationships between sensory signals, their intermediate representations, and behavioral states or actions that can point to less obvious “emergent” processing strategies. If we consider a more complex chain of events in Fig. 17, for instance a repetitive pointing to objects, the regularities will typically not be manifest and easily detectable on the hardware level of sensory signals or the stage of a single module alone. Then to endow the system with an efficient learning architecture requires to collect information from different hierarchical levels and many modules, to generate useful combined representations and to define appropiate learning on several stages from calibration of hardware-near feedback loops up to high-level organisation of the interactions between functional modules. Though there remain many questions in integrating these levels, we find that the attentional mechanism discussed above provides one very flexible approach to generate as well regularities to be exploited by higher level modules as a way to propagate top-down higher level knowledge to modulate the lower level processing. As the human brain compared to our robotic system is much more complex, we would expect that its cognitive abilities as well rely on a multi-scale hierarchy of processing and thus their understanding will require explanations reaching from the level of neural connectivity up to the level of interrelations of functional modules and, as a key element, the organisation of their mutual relationships.

A second answer is connected with an important issue that is totally absent in small systems: the issue of scaling. While simple tasks invite many workable approaches, their number gets drastically reduced when task requirements increase. For instance, in earlier work we found that the 3D postures of computer-rendered hand configurations could be rather accurately predicted with a single, “monolithic” neural net that was trained with a sufficient number of computer generated pictures as input and the known joint angles as output values. However, we found that we were unable to generalize this pure “black-box” approach to the identification of hand postures of real human hands. Initial attempts rapidly revealed that the visual variability of a real hand was much larger than that of the computer hand model used in previous work. Contributing factors were not only the higher shape variability, but also visual effects such as shadows or variability in lighting that were not modeled in the artificial images. Another factor was the more limited amount of training data that could be produced: while synthetic images can be generated in almost unlimited amounts, it becomes difficult to prepare more than, say, a few thousand real hand images for training. Finally, for a computer rendered hand image, all joint angles are known by construction while for real images such information is absent.

All these factors were more or less directly related to issues of scaling: the transition from computer rendered to real images involved a significant up-scaling of image variability; at the same time, size of the available training corpus was scaled downward and in addition the learning algorithm had to be adapted to be workable for much more restricted training information (2D finger tip locations instead of 3D joint angles).

The higher shape variability, together with the more limited available training information led to the necessity of introducing an intermediate representation at the 2D level (in the present example the finger tip locations) from which then the 3D information is obtained only by a separate stage. The task of this intermediate representation was twofold: to represent the sensory input with a set of features (in the present example the 2D finger tip locations) that could be effectively correlated with available training information (marked 2D locations in training images) so that one can construct the representation by data-driven learning. to reduce the task complexity for the remaining stages by “condensing” the task-relevant information in a set of features that are stable with respect to non-relevant changes in the sensory input.

A third answer, closely related with the issue of scaling, is that only the construction of larger systems provides us with sufficiently realistic opportunities to explore architectural principles for the organization of a larger collection of functional modules and their communication. While the implementation of GRAVIS required in many places also the use of handcrafted heuristics to make things work, there were a number of more general principles that proved valuable in many situations:

the adaptive, data-driven construction of feature mappings, using neural networks that combined elements of both local and global feature encoding.

the use of feature maps to encode low-level visuo-spatial information in a format that is flexible for shaping interactions and for later addition of new modules.

a “vertical” organization into several, hierarchically organized processing stages in which each stage has the more modest goal of a cautious narrowing down of the solution space instead of attempting an early final result (for instance, first identify finger tip regions, then in each region location candidates, only then final location).

a “horizontal” organization into several processing streams that were directed at the same goal (e.g., identify important image locations) but that used different computational strategies (neural network based prediction, boundary detection, pattern template) proved efficient for achieving robustness in an extensible way.

To what extent can work as reported here be useful in neuroscience? Certainly, we are fully aware that work such as reported here can make no direct contribution to modeling the biological brain structures themselves, since systems as the above provide no direct correspondences to experimentally observable neural structures. We would not even claim that there is any close correspondence in terms of functional units or their interaction patterns. However, we think that approaches of the kind as described here can be useful to get an impression of the computational challenges of problems solved by the brain, and we can explore the feasability of general computational strategies that underlie current hypotheses about processing in the brain at a system level.

For example, by building systems that can extract the 3D posture of hands from images, we can study how different representational schemes (e.g., holistic versus hierarchical) can cope with the problem of representing information about a complex articulated shape. The implemented system then permits to study how strategies, such as the use of multiple processing streams, can be exploited to increase robustness. By employing learning algorithms to train the artificial system we can get upper bounds on the required number of training views in order to achieve a particular recognition accuracy. The building of actual systems also confronts us with limitations of current learning approaches, e.g., their need of labelled training examples for efficient learning, often in conjunction with ”system surgery” to train the individual modules, and provides us at the same time with an experimental platform to explore ways to overcome these limitations and to develop — in the long run — learning architectures that come in their abilities closer to those of living systems.

Since the abilities of self-generated actions, as well as the imitation of observed actions are very likely to be crucial for most forms of real-world learning, we also need to build interactive robot systems, such as exemplified by the GRAVIS system reported here. Here too, we illustrated how such systems permit to study biologically motivated computational strategies, such as the use of topographic activity maps for attention control, or behavior-based architectures consisting of interconnected networks of basis behaviors. Again, individual modules in our system (as depicted in Fig. 17) cannot be matched directly onto putative counterparts in the brain. Despite the absence of such a correspondence a system such as GRAVIS permits us to explore the feasability of particular computational approaches — in this case, a map-based attention control within a behavior-based architecture — that have been inspired by current ideas about how action chains might be controlled in a neural system. In this way, robot systems can provide us with a useful ”scratchpad” for better assessing the workability of ideas about how a complex perception-action system might achieve the observed, highly flexible coordination of its sensing and acting capabilities. Thus, they can aid us towards a clearer picture about sufficient conditions to generate particular capabilities.

Last, but not least, it may be comforting to find that architectural principles that are in line with current views of brain function can prove valuable for the solution of demanding tasks of machine perception. Thus, research prototypes of such systems can offer additional avenues to explore some of the strengths and limitations of such architectures at a more abstract level and in ways that might not be feasible with real brains.


Part of this research was funded by the German Science Foundation (DFG CRC 360).


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