(Steps towards knowledge-based visual servoing)
http://www.cs.bham.ac.uk/~axs (Original title: Perceiving movements involving predictable affordance changes)
But the proposal was too remote from the available technology and the interests of most collaborators, and the proposal could not be pursued, especially as the project had only one more year to run.
Since then the ideas here have continued to develop, but not as part of any project, partly as a result of work on related topics presented in various discussions on this web site. I believe the ideas are still relevant to understanding natural visual systems and understanding why artificial visual systems remain far inferior in their versatility and robustness, even though highly trained specialized applications may be impressive within the scope of the benchmarks against which they are trained and evaluated.
Many of the ideas presented here arose in conversations with Jeremy Wyatt,
before and during the CoSy project. This included the idea of a generalised
NOTE added 18 Oct 2015
A separate document recently added to this web site explores relationships between evolution of abilities to perceive and reason about affordances (interpreted more widely than in Gibson's work) and evolution of mathematical abilities leading up to the discoveries reported in Euclid's Elements. Those abilities are discussed in relation to cognitive/perceptual abilities involved in perceiving differences between pictures of possible scenes and pictures of impossible scenes (e.g. by Reutersvard, Penrose and Escher, among others). The ability to detect that a picture depicts an impossible object is clearly related to the ability to detect that some action under consideration or some change in the environment is impossible, where that is not empirical impossibility, but geometric or topological impossibility (both of which are varieties of mathematical impossibility). See:
And these slides for a workshop at Warwick University on 31st Jan 2018
This file is available in html and pdf formats
It is still liable to change, so please save links to the file rather than copies, which are likely to become out of date.
WARNING: This file is still under development and liable to change .
This file was installed: 18 Nov 2007
THIS VERSION LAST UPDATED:
20 Jan 2017 Added table of contents; 19 Feb 2018 (Links to McClelland)
3 Aug 2016 Some reformatting. Added PDF version; 4 Aug 2016: Minor edits.
12 Jun 2015 (Re-formatted); 18 Oct 2015 (added a link)
28 Mar 2014 (video section brought to top); 30 Mar 2014 (REF added)
19 Jan 2008; 14 Mar 2014 (relocated and videos replaced); 3 Sep 2014 (re-format)
Some failures of robot actions are due to inaccuracy in the production of movements specified by motor control signals. Since the CoSy robot used the Katana robot arm which provides very precise control, that was not the source of the PlayMate's problems. The difficulties arose to a large extent from the content and quality of the information the robot obtained about the current state of the environment, and its dependence on such information.
If the visual system were able to provide exact 3-D locations of every point of every surface of objects in the scene, including the robot's own hand, then in principle the planning and motor control subsystems could produce plans, and motor signals based on those plans, that enabled the robot to achieve its goals far more often (apart from problems such as objects slipping in the gripper when lifted, which are not a serious problem in the current scenario). However, we'll later see that even perfect metrical information about the current scene may not be the most useful basis for intelligent action planning and control.
This is related to the reasons why understanding a proof in Euclidean geometry or topology, based on understanding a diagram or collection of diagrams, does not depend on having perfect metrical information about the diagram, as illustrated in
The poor quality of information available for planning and motor control has three main aspects:
There are two very different ways to try to improve the performance.
(Note: This kind of uncertainty is simply a matter of not having complete information. It need not involve any measure of degree of certainty or uncertainty. If I cannot tell whether a distant gap is too narrow for me to pass through I need not have a measure of my uncertainty, though I may know that I am less certain about a distant gap than about a nearby gap which I can more easily compare with my body width.)
The hypothesis explored here is that the second method is also worth exploring, and in the long run could turn out to be more important for explaining and replicating animal intelligence. That would include use of "cognitive visual servoing" or "knowledge-based visual servoing". This contrasts with servoing in which all the information used is numerical (including derivatives, etc.)
([NOTE: 29 Oct 2015] Since the above was written, work by Jeremy Wyatt and colleagues have made significant steps in that direction, but using methods that continue to depend on metrical details and probabilistic reasoning, unlike the methods suggested below. It is possible that the two approaches could be combined: a topic for another discussion.)
For a tutorial introduction to visual servo control see Hutchinson et al. The methods described in the tutorial depend on use of measurements in images and scenes, and their derivatives. According to Hutchinson et al. "... the design of stable, robust, image-based servoing systems .... has not been fully explored". Perhaps that's because researchers have focused on attempts to maximise precision and minimise risk of error: a pair of conflicting requirements. Things may have changed since that was written.
At present I suspect there are no readily available good, useful algorithms for deriving such information from sensor and effector (e.g. proprioceptive) information. It may also turn out to be the case that getting such information from rectangular grids of numerical measures poses difficulties that are avoided by the physical design of biological visual sensors, which have a totally different structures from frame grabbers. the methods proposed would do better with cameras whose 'retinas' are not rectangular arrays but more like biological retinas, with resolution varying symmetrically around a fovea along with precise control of motion of the fovea when locked on a moving image structure. But there's a great deal of work to be done before any definite claims can be made.
The rest of this paper does not address the problem of improving camera design or the form of information produced by cameras.
Instead we focus on the requirement to use new kinds of information, both about the environment and about the robot's current information processing, to reason about whether and how to change what it is doing. That will require development of
This hypothesis is expanded by drawing attention to the importance of the robot's being able to predict affordance changes that could be produced by its own actions, including predicting changes in action affordances and predicting changes in epistemic affordances.
This requires the ability to think and reason about sets of possibilities at a high level of abstraction, and to find new useful ways of chunking sets of possibilities: an important form of learning that seems not to have had enough attention. (See Sloman (1996))
The hypothesis is based on informal observation of some of the things humans and other animals can do. If we succeed in implementing these ideas in a working system, that could be at least a demonstration of the feasibility of the mechanisms. It may also suggest new experiments that could be done using children and other animals, including investigation of how cognitive visual servoing abilities develop. It is possible that related research could help us understand some intelligent behaviours in some other animals.
In the above figure you can find the highest horizontal green portion by moving your gaze from the top of the image down to find a horizontal fragment, then scanning to left and right to see whether any other green portion crosses your scanning line. A similar procedure makes it easy to find the lowest horizontal green portion. Neither requires any measurement or estimated measurement of height on any particular scale (inches, centimeters, etc.).
It is not so easy to tell which is the tallest black line and which the shortest. That's because there isn't a common base line in the image. However, if they were physical rods and the green surface was a solid object there are several possible physical manipulations that would make the comparison easy, e.g. moving all the rods into the hole with the lowest bottom, and then simply comparing the top ends of the rods, to find the one that projects above all the others, and the one that all the others extend beyond.
I suspect that in the first few years of life a typical human child discovers hundreds of such means of answering questions about metrical relationships without using any measurements, only topological relationships (e.g. touches, overlaps, extends beyond, etc.). This learning is done unconsciously, and the results are used unconsciously.
I suspect also that they develop ways of replacing actual physical motions to support comparisons (e.g. comparisons of length) with imagined or visualised motions, such as visualising the top of the left-most black line (line 1) moving to the top of the fourth black line from left (line 4), and seeing whether that would bring the bottom of line one to a location on line 4 or a location projecting below the bottom.
The original version of this paper made use of some video recordings made in 2005, that don't seem to be viewable using currently available tools. So I have prepared two new videos, demonstrating some of the points made above, using a mug and a pen held in various positions in relation to the mug, and asking questions about what can be seen regarding possibilities for motion.
Both videos have unscripted verbal commentaries.
The first video (.webm, about 30 secs),
introduces the second video (.webm, about 6 mins 30secs):
This shows a mug, a pen and a hand holding the pen and moving it in various positions and orientations relative to the mug, so as to change the affordances: e.g. some positions restrict some vertical motions and some positions restrict some horizontal motions, e.g. left-right horizontal movements, or front-back horizontal movements, or rotational movements.
You can easily do experiments yourself, holding a pen near, above, inside, a mug and moving it in various ways (including translations and rotations). Consider what predictions you can make about how further movements will or will not be constrained if you continue a particular movement. E.g. will the pen make contact with a part of the mug that will constrain further movement? Will continued motion bring the end of the pen into the mug so that further movement sideways and down is constrained by the mug. If motion of the pen is already constrained, what movements would alter the relationships so as to remove the constraint? Consider also what predictions you can make about what information will and will not be available to you.
A task for a vision system is to be able see a movement and to predict that IF that movement continues THEN the relationships between the pen and the mug will (or will not) change in specific ways so as to restrict further movements.
Some of the changes in relationships will be topological, some metrical, some continuous (getting closer), some discrete (coming into contact with, entering the volume directly above an object), some will be changes in action-affordances or epistemic-affordances, between something being possible and it being impossible, with a small, medium, or large region of uncertainty in which the phase change occurs.
None of this implies or requires use of probabilities or conditional probabilities. The most important changes are between what is and is not possible, or what will necessarily be the case (e.g. without a change of direction, the motion of the pen must produce a collision with the wall of the mug).
In some situations there are partial orderings of probability/likelihood but these are pre-mathematical "common sense" notions, not the concepts used in the theory of probability. In this sense, in most circumstances, the more your speed on a motorway exceeds the speed-limit the more likely you are to have a crash. In exceptional circumstances the opposite may be true, e.g. if someone is attempting to crash into you from behind in a vehicle whose maximum speed is lower than yours.
That's because visual comparisons of length, thickness, distance apart, curvature, and other features can, in many cases, be made on the basis of perceived topological relationships (e.g. containment, or overlap), without the use of any measurement operation producing an absolute value.
So animals (and future machines) with such capabilities will be able to have and to achieve intentions to find, or make something that is longer or shorter or the same length, thicker or thinner or the same thickness, straighter or more curved than or curved as much as something else.
More complex intentions can then make use of the results of those comparisons, e.g. making something using the found objects -- such as an archway with two sides the same height, or an item of clothing that fits the wearer well.
That's unnecessary because the ability to detect discontinuities in contents in static scenes, and discontinuities during perception of motion do not require use of such measures. The temporal order in which certain changes are sensed can provide comparative information about spatial measures. So, for at least a subset of cases, it may suffice to be able to detect certain discontinuities in a visual array, and to notice occurrences of something increasing or decreasing. that occur in sensor values as eyes move across a scene (or hands in the case of haptic perception), such as something becoming visible, or ceasing to be visible, or a feature (e.g. reflection, or highlight) moving from one side of another feature, e.g. a crack or mark on a surface)
The videos were recorded using a Logitech Webcam B500, and the 'cheese' program on linux.
Some AI researchers assume (and some critics believe all AI researchers assume) that intelligent systems have to make use of a repeated three stage sequence of processes
In other cases the monitoring continues until the action has advanced to the point where it can be completed ballistically (e.g. grasping a mug) and visual attention then moves to the next location at which action is needed, e.g. the jug to be grasped by the other hand, which requires the hand to be moved towards the jug using continual adjustments to the approach until it is close enough to complete the action without monitoring.
It is obvious that humans and many animals do not fit the sense-decide-act model in their everyday life, and instead do many things, including sensing, deciding and acting concurrently.
Since the early 1990s, the Birmingham CogAff project members have argued that an architecture is needed in which at least 9 different types of process are performed concurrently -- though without making the control-engineer's assumption that those processes are all continuous and of a type for which differential equations form a good representation.
Some may be continuous and some not, including possibly 'alarm' mechanisms that monitor mechanisms of other sorts and have the ability to freeze, modulate, redirect, or abort processes of all sorts.
In particular the notion of servo control, which normally assumes continuous (analogue) information processing can be generalised to include visual servoing which includes discrete processes of high level perception, goal-generation, goal processing, planning, decision making, self-monitoring, learning, and initiating new actions along with continuous control of movements and sensing of actions and environmental changes.
This paper assumes that such an architecture is available, and outlines a hypothesis that some of the information processing that could be useful for a robot (or animal) manipulating objects in the environment uses visual servoing and other kinds of servoing, partly on the basis of predicting changes in at least two kinds of affordances.
In particular we distinguish the ability to predict
I.e. it will need to have additional internal self-observation capabilities in order to detect states in which it lacks information or is uncertain about the information it has, and it will need the ability to use the results of such self monitoring in order to control subsequent planning, decision making, and actions. There are several presentations on varieties of architectures, using the CogAff schema as a framework for comparing alternatives and presenting H-CogAff as a conjectured architectural schema suitable for human-like minds, available here http://www.cs.bham.ac.uk/research/projects/cogaff/talks/
I shall later try to provide a summary presentation focusing on issues relevant to this discussion paper.
The document has been changing frequently since work began on it in mid November 2007, and it is likely to continue to change and develop. Comments, criticisms and suggestions welcome.
Some people have discussed this, e.g.
So, physical actions or processes can change not only the available action affordances, they can also change epistemic affordances -- e.g. what can be perceived, felt, heard, etc. allowing the individual to obtain new information or, in the case of negative affordances, obstructing access to information.
So both action affordances and epistemic affordances can be changed when something moves in the environment and that means that the possibilities for those movements are related to possibilities for adding, removing or modifying action and epistemic affordances.
We can refer to the affordances to produce or modify affordances as "meta-affordances". This paper introduces examples and discusses ways in which meta-affordances can be used in predicting how actions or other events will change affordances.
A particularly important class of actions that can affect epistemic affordances is the set of changes of view point or view direction, but there are many others, including moving an object to make something more visible. Besides epistemic affordances related to vision, there are others related to other sensory modalities, but not much will be said about that here.
I believe that this discussion is closely connected to other CoSy discussion papers concerned with the need for exosomatic, amodal ontologies and limitations of the use of sensorimotor contingencies as a means of representation, but that will have to be discussed in another paper. For more on that topic see
COSY-DP-0603 (HTML): Sensorimotor vs objective contingencies
There are some proposals for using these ideas for dealing with uncertainty by identifying "phase boundaries" between regions of certainty regarding affordances, and keeping away from those phase boundaries to avoid uncertainty.
For example, if you are holding the pen horizontally above the mug, centred on the mug's vertical axis then, if you try moving the pen down, the motion will be limited by the rim of the mug. However there are several actions that will make it possible to move the pen to a lower level including these:
Those are examples where an action (horizontal movement, or rotation about a horizontal axis) produces a new state in which changed affordances allow additional actions (downward vertical movement).
Other movements will restrict the actions possible. E.g. if the pen is pushed horizontally through the handle of a mug and the mug is fixed, that will restrict possibilities for movement of the pen in any direction perpendicular to its long axis.
Such unavailable information can often made available either by moving something in the scene or by changing the viewpoint.
For example, lifting the pen vertically can change the situation so that the first question can be answered. The second and third questions could be answered either by moving to look down from a position above the mug or by moving the viewing position sideways horizontally and viewing the mug and pen from some other positions.
The problem discussed in this paper is: what are the ways in which by performing an action an agent can change not just the physical configurations that exist in the environment, but also the affordances that are available to the agent, including both action affordances and epistemic affordances (i.e. affordances for gaining information).
Humans (though probably not infants or very young children), and also, I suspect, some other animals, are able to perceive scene structure in such a way as to support reasoning about how to change things so as to alter affordances. This competence includes
If all this is correct, then one of the previously unnoticed (?) functions of a vision system is to be able, when seeing a movement of an object the vicinity of another object to predict that IF that movement continues THEN the relationships between the two objects will (or will not) change in specific ways so as to restrict or allow further movements (seeing changing action affordances), or so as to restrict or allow further information acquisition (seeing changing epistemic affordances).
Similar reasoning should be applicable to reasoning about
consequences of possible motions as opposed to actual
motions. This is relevant to both the CoSy PlayMate scenario
and the CoSy Explorer scenario.
[See also the KR'1996 paper "Actual possibilities"]
Either way, states will be represented by a logical or algebraic structure, such as a predicate applied to a set of arguments, or a vector of values, and predictions will involve constructing or modifying such structures.
The abilities described and illustrated below seem to involve the use of a different sort of mechanism: one that makes use of 'analogical' representations in the sense defined in (Sloman 1971), discussed as an example of the use of an internal GL (Generalised language) in this presentation on evolution and development of language.
This ability to reason about how affordances change as a consequence of changing locations, orientations, and relationships of objects also provides illustrations of the notion of Kantian causal competence, contrasted with Human causal competence in presentations by Chappell and Sloman here.
The important point about such reasoning, apart from the fact that it is visual reasoning that uses analogical representations, is that the reasoning is geometric, topological and deterministic, in contrast with mechanisms that are logical or algebraic and probabilistic.
That is because the nature of such restrictions, e.g.
A prevents the motion of B from continuing
does not depend on precise metrical relationships between objects their surfaces and their trajectories. Instead, much coarser-grained relationships, using relatively abstract spatial information, especially topological information and ordering information (e.g. A is between B and C), suffices for most configurations.
For example, if the point of a pen is within the convex hull of an upward facing mug then the material of the mug will eventually constrain horizontal and downward motion if the pen moves, but not upwards motion.
The word 'eventually' is used in order to contrast predicting exactly how much the object can be moved before contact occurs with predicting that contact will occur e.g. before the pen point has reached a target location outside the mug. I.e. the prediction is that a boolean change will occur (some relationship between objects will change from holding to not holding), but not exactly where or when it will change. That prediction does not involve high precision, but is sufficient to indicate the need to lift the pen before moving it horizontally far beyond the width of the mug.
If the mug is lying on its side, and the pen is horizontal with the point in the mug, then the mug constrains vertical movements and some, but not all, horizontal movements. For example, a horizontal movement bringing the pen out of the mug is not constrained, whereas a horizontal movement in the opposite direction into the mug will eventually be constrained -- when the pen hits the bottom of the mug. (The bottom surface is vertical because the mug is lying on its side.)
A robot that understands its environment needs to be able to perceive such constraints and use both in planning future actions and in controlling current actions: e.g. ensuring that the movement will bring about a desired change in constraints by adjusting the direction of motion or the orientation of one of the objects.
The exceptions occur when objects are close to 'phase transitions' e.g. close to the boundary of a convex hull of a complex object, or close to a plane through a surface or edge. In those special cases it is often hard to make binary classifications that are easy in the vast majority of cases. But it is usually easy to make a small movement that will turn a hard problem into an easy one.
Questions relating to Figure 1
Assume that all the pencils shown in the figure lie in the vertical plane through the axis of the mug. So they are all at the same distance from the viewer, as is the axis of the mug.
For each starting point and possible translation or rotation of the pencil we can ask questions like: will it enter the mug?, will it hit the side of the mug?, will it touch the rim of the mug?
In some cases the answer is clear. In cases where the answer is uncertain, because the configuration is in the "phase boundary" between two classes of configurations that would have clear answers we can ask how the pencil could be moved or rotated to make the answer clear. (Compare being unsure whether you are going to bump into something while walking: you can either try to look more carefully, use accurate measuring devices, etc. compute probabilities, etc. or you can alter your heading to make sure that you miss the object.)
TO BE EXTENDED
As illustrated above, when predictions need to be made, an intelligent agent can move the object away from the 'difficult' position or trajectory so that it is far enough from the phase transition for fine control or precise predictions not to be required.
In some cases where being close to a phase transition makes a perceptual judgement difficult (e.g. will an object's motion lead to a collision?) it is possible to resolve the ambiguity by a change of viewpoint. Moving to one side, for example, may alter one's view of a gap so that it becomes clear whether the gap is big enough for an object to fit in it with space to spare. Some simple examples of problems requiring a change of viewpoint are given below.
Similar comments apply to relations not between objects but between their trajectories. The exceptions are hard to deal with, but very many cases are easy, without requiring great precision, because they concern topological or ordering relations rather than metrical information, and a change of viewpoint or slight modification of a trajectory may turn a difficult prediction into an easy one.
Another type of exception is related to the fact that in the 'easy' cases discussed above movements can be visualised in advance with accuracy sufficient for the task of deciding what will happen, and they can also be performed ballistically, without fine-grained feedback control. A different sort of situation occurs when the object being acted on is very small (e.g. it takes up a relatively small portion of the visual field, and relatively small changes in motor signals will always make a difference to whether a finger does or does not make contact with the object). Using a small tool e.g. small tweezers to manipulate such objects requires additional competences beyond those discussed above. But for now we can ignore such actions: they require expertise that probably develops later involving fine-grained visual servoing to control very precise small movements. Such cases are ignored here.
See also the remarks about fully deliberative architectures here.
It is worth mentioning that meta-management capabilities are required for dealing with the problems of uncertainty mentioned above. The individual trying to predict how affordances will be changed if an action is performed, needs to be able to detect when that prediction is hard because the objects and trajectories are close to a 'phase boundary' so that only if precise, noise-free information is available can the prediction be made reliably. If such situations are detected, using a meta-management mechanism to evaluate the quality of current information, then working out how to change the situation so that the problem is removed, e.g. by moving an object or rotating it so as to move it further from the phase boundary can use a deliberative mechanism if the situation is unfamiliar, or a learnt reactive behaviour, if the situation is familiar.
What should a vision program be able to say about the above images (A), (B), (C), (D), each involving a mug, a horizontal pen, and two rigid vertical cards, if asked the following questions in each case:
What should a vision program be able to say about the above images (A), (B), (C), (D), each involving a mug, a pen, and two rigid vertical cards, if asked the following questions in each case:
What should a vision program be able to say about the scene depicted in Figure 4?
Are there any actions a robot could take to shed light on what's going on?
Pictures based on the work of Oscar Reutersvärd (1934)
E.g. as the eye or camera moves forward the location of some object within the visual field indicates whether continued motion in a straight line will cause the eye to come into contact with the object or move past it.
Slightly more complex reasoning is required to tell whether a mouth or beak that is rigidly related to the eyes will be able to bite the object. That situation is analogous to the camera mounted on the PlayMate's arm, near its wrist, as shown here:
For example consider the problem of using camera images to control the motion of the hand with a wrist-mounted camera, when an object is to be grasped, or using eyes mounted above a mouth, when an object is to be grasped with the mouth.
Here are two schematic (idealised) images representing a pair of snapshots that might be taken from a camera mounted vertically above the wrist and pointing along the long axis of the gripper.
One of the images is taken when the gripper is still some way from the block to be grasped and the other is taken when the gripper is lower down, closer to the block. It should be clear which is which. Now, if the camera is mounted above the gripper is the gripper moving in the right direction?
For the robot to use the epistemic affordance here it has to be able to reason about the effects of its movements on what it sees and how the effects depend on whether it is moving as intended or not. It is possible that instead of explicit reasoning (of the sort you have probably had to do to answer the question) the robot could simply be trained to predict camera views and to constantly adjust its movements on the basis of failed predictions.
In one case it needs explicit self knowledge, which can be used in a wide variety of circumstances, and in the other case it needs implicit self knowledge, produced by training, which is applicable only to situations that are closely related to the training situations.
A human making use of the epistemic affordance by reasoning about the information available from the differences between the two views, may make use of logic, a verbal language, and perhaps some mathematics. A less intelligent animal or robot may have that information pre-compiled (e.g. into neural control networks) by evolution or previous training and available for use only in very specific control tasks.
Is there some intermediate form in which the information could be represented and manipulated that could be used by an intelligent animal to deal with novel situations, and which does not depend on knowing logic or a human-like language, but might make use of what we have been calling a GL (a Generalised Language), which has structural variability and compositional semantics and may involve manipulation of representations of spatial structures?
Computational Cognitive Epigenetics
(Sloman and Chappell, to appear in BBS 2007)
In all cases visual servoing requires what could be described as 'self-knowledge' insofar as it involves explicit or implicit knowledge about the agent's situation and actions that can be used to make predictions and to interpret discrepancies between predicted and experienced percepts, and to use those discrepancies to alter what it is doing.
But this does not require an explicit sense of self if that implies that the robot (or animal or child learning how to bite things or grasp things) is able to formulate propositions about its location, its actions, its percepts, its goals, etc.
One way of dealing with that is to attempt to estimate the uncertainty, or the probability distributions of particular measures, and then to develop techniques for propagating such information in order to answer questions about what is going on in the scene, where the answers will not use precise measures but probability distributions.
Another way is to find useful higher level, more abstract descriptions, whose correctness transcends the uncertainty regarding the noisy image features. So for example, the change between the left and right images above could be described something like this (though not necessarily in English):
In the second picture, the image of the target object is larger and higher in the field of view.
The uncertainty and noise in the image can be ignored at that level because all the uncertainty in values in the images is subsumed by the above the description. The description does not say what the exact sizes of the of the images are in the two pictures, or the exact locations, or the exact amount by which it is larger or further from the bottom edge.
So since the gripper is below the camera, the fact that the image is moving up the field of view means that the direction of motion of the gripper is towards a point below the target, requiring the motion to be corrected by moving the wrist up. Exactly how much it move up need not be specified if the motion is slow enough and carefully controlled to ensure that the target object moves towards a location that has previously been learned is where it should be for the gripper to engage with it. If the gripper fingers are moved far enough apart the location need not be precise, and if there are sensors on the inner surface of the fingers they can provide information about when the object is between the fingers and the grip can be closed.
This description is over-simplified, but will suffice to illustrate the point that there is a tradeoff between precision of description and uncertainty and that sometimes the more abstract, less precise, description is sufficiently certain to provide an adequate basis for deciding what to do.
Future domestic robots will also need to have such competences.
The abilities to predict changing affordances form a special case of
understanding causal relationships, in particular Kantian causal
relationships, as discussed in
Brian V. Funt, 1977
WHISPER: A Problem-Solving System Utilizing Diagrams and a Parallel Processing Retina
IJCAI 1977, pp 459-464
Usefully summarised in
Diagrammatic Representation And Reasoning
Machine GRAPHICS & VISION, Vol. 3, Nos. 1/2, 1994, 77-103
See also: Kulpa's
Diagrammatics web page
The hard part will be parsing real visual images to produce the required 2-D manipulable representations.
Comment added 18 Oct 2015
It is clear that what I wrote above in 2007 was over-optimistic. The techniques for manipulation used in graphical tools mostly operate on metrically precise structures, and normally do not support reasoning about what is and is not possible. There may be more recent work on geometrical and topological theorem proving that is relevant, though I suspect everything done so far uses forms of representation and reasoning that are very different from those used in animal brains for reasoning about affordances. For further discussion of abilities to perceive and reason about possibilities and impossibilities (constraints) see the following:
Slightly easier will be software to:
For affordance prediction and the avoidance of phase boundaries it may be useful to be able to grow a "penumbra" of specified thickness around the 2-D image projection of any specified object, and then when an object A moves in the vicinity of object B, d
(a) detect when A's penumbra first makes contact with B's penumbra and where it happens;
(b) detect when one of the penumbras first makes contact with the other object (inside its penumbra)
(c) detect when A itself first makes contact with another object (inside its penumbra)
Choosing penumbra sizes to facilitate reduction of uncertainty will require programs that can analyse aspects of the structure of a scene and detect whether some relationship introduces uncertainty in predictions. Then choosing a penumbra size to use when selecting a movement that is certain not to produce a collision will be a task dependent problem.
[All this is closely related to Brian Funt's PhD. See reference below.]
Arnold Trehub's retinoid mechanism may be useful: The Cognitive Brain (MIT press, 1991) http://www.people.umass.edu/trehub/As mentioned above this work on predicting affordance changes is related to my recent work with Jackie Chappell on GLs (Generalised Languages) evolved for 'internal' use in precursors of humans as well as many other mammals, e.g. chimpanzees and possibly hunting mammals, and in some bird species. GLs are also required by pre-verbal children. See
Why are you hesitating? To check whether my hand will bump into the cube Why did you move your head left? To get a better view of the size of the gap between the cube and the block Can your hand fit through the gap between the two blocks? I am not sure, but I'll try Can your hand fit through the gap between the two blocks? I am not sure, but I can move them apart to make sure it can. Is the block within your reach Yes because I just placed a cube next to it. How can you get the cube past the block? Move it further to the right to make sure it will not bump into the block then push it forward. etc. etc.
There is a wide variety of propositions, questions, goals, plans, and actions, dealing with a collection of spatial, causal and epistemic relationships that can change. If we choose a principled, but non trivial subset related to what the robot can perceive, plan, reason about, and achieve in its actions, then that defines a set of questions, commands, assertions, explanations, that can occur in a dialogue.
That will require working out a suitable initial state, including initial forms of representation, competences, and architecture that is able to support the development of a suitable altricial competence.
http://www.cs.bham.ac.uk/research/projects/cogaff/07.html#717 COSY-TR-0609 (PDF): Natural and artificial meta-configured altricial information-processing systems Jackie Chappell and Aaron Sloman Invited contribution to a special issue of The International Journal of Unconventional Computing Vol 2, Issue 3, 2007, pp. 211--239,