T H E I N T E L L I G E N T H A N D:
A N E X P E R I M E N T A L A P P R O A C H T O H U M A N O
B J E
C T R E C O G N I T I O N
A N D I M P L I C A T I O N S F O R R O
B O T I
C
D
E S I G N
Susan J. Lederman
Queen's University at Kingston
Depts. of Psychology and C omputing & Information Science
Kingston, Ontario
Canada K7L 3N6
Abstract
The scientific study of biological systems offers an approach to the development of sensor-based robots that is complementary to the more formal analytic methods currently favoured by roboticists. I initially propose several general lessons from the biological field. Next, I consider a specific example selected from the work of Lederman & Klatzky, which focuses on human haptic object processing. An empirical base and recent theoretical developments from our research program on this topic are described. The human haptic system is an information-processing system that combines inputs from sensors in skin, muscles, tendons, and joints with motor capabilities to extract different object properties. A general model of human haptic object identification, which emphasises how object exploration is controlled, is presented. The model describes major architectural elements, including representations of haptically accessible object properties and exploratory procedures (EPs), which are dedicated movement patterns specialized to extract particular properties. These architectural units are related in processing-specific ways. The resulting architecture is treated as a system of constraints, which guide the exploration of an object during the course of identification. Empirical support for the model is also examined. To conclude, I show how this scientifically-based approach might be applied
to developing strategies for active manual robotic exploration of unstructured environments.
1 Outline
In this talk, I treat the concept of "intelligence" as covering a broad domain that includes sensing and perceiving, thinking, and acting on the environment. In Section 2, I argue that there are a number of general lessons offered by the scientific study of intelligent
biological organisms for sensor-based robotic design. In
Section 3, I provide an example that focuses on the
problem of human object perception and recognition.
However, rather than follow the mainstream route by
investigating vision, Roberta Klatzky and I have selected
the "haptic" system for study. We formally define this as
an information-processing system that uses inputs from
receptors in skin, muscles, tendons and joints to perceive
the concrete world and to guide actions within it. I
present some of our experimental and empirically-based
theoretical work on human haptic object processing,
with particular emphasis on the nature and role of active
manual exploration. In Section 4, I suggest how this
research programme may be modified and extended to
guide the development of high-level manual exploration
strategies for robots equipped with a haptic perceptual
system. Section 5 provides a more general summary of
how knowledge of biological systems may contribute to
the field of robotics.
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the parameters are determined by principles of physics. As the system is typically too complex to model without resorting to approximations, the parameter set is reduced arbitrarily by the investigator. The scientific method provides an alternate and complementary approach to the design of sensor-based robotic systems.
A l l sciences are based on careful, systematic and repeatable observation. In addition, when we actually systematically control or manipulate the parameters under investigation, we are said to be using the "experimental method". The scientific study of any system provides a coherent framework within which to study a given problem, whether this pertains to living or artificial systems. It provides formal methods (experimental and statistical) with which to systematically and rigorously test the validity of one's hypotheses, based on empirical results. We see such issues as being critical as well to the successful development and implementation of intelligent sensor-based (tele)robotic systems. The rigorous principles and methodologies of the experimental method expose some
of the weaknesses and limitations of current robotic practice. We have argued that the scientific approach offers roboticists a powerful set of general tools with which to complement their formal analytic methods (see Lederman & Pawluk, 1992, for a mini-tutorial on the scientific method and its applications to robotics).
Let us turn now to one example involving the scientific study of intelligent biological systems, specifically the human haptic system, and how it processes and represents objects.
3 T h e h u m a n h a p t i c system a n d object
processing2
3.1 Background
Several years ago, we (Klatzky et al, 1985) demonstrated that humans are remarkably skilled at recognizing common objects (e.g., hammer) using only touch. We asked blindfolded observers to identify a set of 100 common objects as quickly and as accurately as possible. Subjects' accuracy approached 100%, while the majority
of objects were identified within only 2-3 seconds. This was surprising at the time since others had suggested that the human sense of touch is incapable of such high-level information processing (e.g., Walk & Pick, 1981).
We began to suspect that how people actively and manually explore such multidimensional objects might
2Section 3 is based on material from Klatzky & Lederman (1991 and in press). play a critical role in uncovering, as well as eventually explaining, the substantial information-processing capacities we had demonstrated. In our next experiment (Lederman & Klatzky, 1987; Expt. 1), we asked subjects
to perform a haptic "match-to-sample" task; on each trial, subjects were initially presented with a "standard" object followed by a set of 3 serially presented multidimensional "comparison" objects. Although all
four objects in any set varied along many different object dimensions (e.g., texture, shape, etc., as underscored in Figure 1), subjects were instructed to attend to a single dimension, such as texture. They were to select the comparison object that best matched the standard object
on the dimension named. Over the entire experiment,
we used different unfamiliar custom-designed object sets
for each of the dimension-matching instructions, some of which are shown (underscored) in Figure 1.
Figure 1. Exploratory Procedures and associated object properties (Lederman, 1991; adapted from Lederman & Klatzky, 1987).
We videotaped and subsequently analyzed subjects' hand movements during each trial. Our results indicated
that manual exploration is very systematic; subjects performed highly stereotypical movement patterns that
we have called "exploratory procedures" ("EP"s). Further, they chose to execute particular EPs in association with specific dimension-matching instructions, the ones most relevant for the current talk being shown in Figure 1. Thus, a Lateral Motion EP (tangential movements on a surface) was typically
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2 General lessons for designing sensor-based
robots1
2.1 Areas of application
It is possible to describe a continuum along which robotic systems can be placed. At one end, we would find those that clearly attempt to reproduce natural living systems. At the other end, we would find those that equally blindly reject the anthropomorphic approach. Yet there is an alternate approach that roboticists may adopt for deriving potentially valuable information from the scientific study of biological systems. According to this approach (e.g., Lederman & Pawluk, 1992; Lederman et al, 1992), one gains new conceptualizations, scientific methodologies, and specific empirical results about how living systems deal with problems that roboticists have yet to solve. Anatomical, biomechanical, neural, and behavioural constraints on information processing are all relevant areas of concern.
The most likely applications of a biological approach are not to be found in highly structured environments (e.g., industrial automation), which may be precisely controlled or modified - under such circumstances, there may be little benefit from copying human processing. It is rather in robotic environments over which the human has little or no control; examples would include those requiring underwater repair and recovery, service and maintenance of the space station, disposal of radioactive waste, exploration of unknown planets, and microrobotic surgery. For operation within such highly unstructured environments, roboticists may benefit from learning how biological systems accomplish complex sensory, cognitive and motor tasks in flexible, efficient ways.
2.2 Overlapping problem domains
Scientists who, study biological systems have addressed many of the same problems that roboticists now face. Consider the following examples: sensor performance, sensor fusion, selection of primitives for scene segmentation and object recognition, object representations, active exploration vs. passive perception, motor control and planning for reaching, grasping and manipulating objects. Both groups need to address
1Section 2 is based on material from Lederman & Pawluk (1992). hardware considerations to understand how such constraints affect the way information is processed and represented, and how this in turn affects system performance.
2.2 Living organisms are functioning, multi-level
integrated systems
It is important to recognize that biological organisms are complete, multi-level, integrated systems that actually work, despite the complexity of the many problems they must handle (see examples above). As such, living systems clearly demonstrate the complexity of the task facing the roboticist. Both biological scientists and roboticists have found it simpler to treat the different sensory modalities as independently operating units; however, the most recent work with biological systems clearly demonstrates the ultimate fallacy and limits of this approach.
2.3 Designing human-machine interfaces for
teleoperation
Initial predictions about the relatively rapid creation of highly flexible, sensor-based autonomous robots have proved overly optimistic. As a result, attention has turned to the design of teleoperated robots, which retain the human operator in the control loop. The rationale
is that it is possible to short-circuit the design process by taking advantage of our own considerable sensory, cognitive and motor competencies. With an intact human central nervous system, it is no longer necessary
to build an artificial one - as obvious by now, no mean feat! Those of us who study human systems, however, are quick to point out that with this approach, it becomes critical for the roboticist to learn about how our own human sensory systems process information, and about the constraints under which these operate. Such capabilities and limitations must be understood to achieve an effective interface with any teleoperated system. Since we are unable to present all information from the remote workspace, what information should be presented to the human operator? and what are the most effective ways to display it? Until now, such considerations have been ignored or noted too late for appropriate modification.
2.4 What the scientific method can contribute to
robotics
In robotics and engineering, it is most common to model
a system analytically using differential equations, where
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performed for the texture-matching instructions; Pressure (applied normal forces or torques about an object axis) was usually selected for hardness: Static Contact (simple static contact between an object surface and the skin) was associated with thermal matching; Unsupported Holding (lifting the object away from a supporting surface, usually in the form of dynamic hefting) was used for weight matching; Enclosure (finger molding to the object envelope) was selected most often to extract both volumetric and global (coarse) shape; Contour Following (edge following) was used most in conjunction with both global shape and exact (fine) shape.
3.2 The macrostructure of human haptic object
identification
There are a number of computational models in the field of cognitive science that have successfully dealt with broad and complex domains of human information processing. Our own general approach to haptic object processing can be appreciated first by analogy to the computational model of reading proposed by Just and Carpenter (1980; 1987) and outlined in Figure 2.
READING:
FROM EYE FIXATIONS
TO C OMPREHENSION
INPUT TEXT
Move Eyes to Next Word
Local Interpretation of Word: Access word meaning
(parallel lookup)
Assign sentence function to word HAPTICS:
FROM OBJEC T
EXPLORATION TO
IDENTIFICATION
INPUT OBJEC T
Move Hand to Next Object Region
Execute Exploratory Procedure
Local Interpretation of Region
Compute Object Property
(parallel over accessible
properties)
representation. Within a fixation, these stages are
performed as completely as possible; over successive
fixations, they subsequently recur. The representation is
continually updated with the inputs from each new
fixation.
In our own model, a period of manual exploration
corresponds to a period of eye fixation. During the
manual period, what we have called the "selection-
extraction loop" takes place. An EP (or EPs) is selected
and performed at some area on the object, on the basis
of activation flow. The resulting data are used to
interpret a local object region, which in turn is used to
build a global object representation for comparison with
stored categorical representations. As much of the local
and global processing is performed within an exploratory
period as " possible; both recur during subsequent
exploratory periods and object regions. Eventually, the
system recognizes an object or selects the next EP for
execution.
Build Representation of Text
Meaning
Go back to start (view a new word) or exit ...
-to alter previous knowledge
representation
-to act Build Representation of Object (material and geometric
properties)
Go back to start (move to a region) or exit ...
-to discriminate
-to classify
-to name
-to use a tool
Figure 2. Analogous stages between haptic object recognition and the reading model of Just and Carpenter (1980; 1987)
They separate text comprehension into a number of stages: moving the eye to the next location, fixation, local (e.g., lexical) analysis, and creating a global Figure 3. Model of the macrostructure of haptic object identification (from KJatzky & Lederman, 1991 and in press).
Figure 3 presents our model of the macrostructure of
the haptic object-identification system, including the different data representations and the links we presume exist among them. An object component represents
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particular objects (e.g., wrench) and their specific property values. A property component represents the attributes along which an object may potentially vary (texture, hardness, etc.), rather than specific property values. More recently, we have divided properties into two major groups: "material" and "geometric". A material property is defined as having a factor affecting the response of a given material to imposed stimuli and constraints, independent of the shape and size of a given sample, for example, texture (Rosenthal & Asimow, 1971). A geometric (sometimes called technological) property is one that relates to the geometry of a particular material sample (e.g., shape, size). "Hybrid" properties directly reflect both geometry and material (e.g., mass). An EP component represents an exploratory procedure. The underlying neural mechanisms control EP execution and process property information arising from inputs to the sensory receptors. The latter sensorimotor component is not something we have modeled in our work; however, there is much relevant research that addresses both the neurophysiology and psychophysics of the somatosensory system.
Links between object and property components reflect the relative strengths of a given property for a particular object (e.g., texture is important for sandpaper), while links between object components are primarily intended
to reflect the hierarchical classification relations documented in the cognitive literature (e.g., Rosch, 1978); for example, wrenches and nuts are linked because of their common tool-related function. match-to-sample experiment tend to produce optimal performance in the constrained version of the experiment.
Table la: EP-to-property weights (from Klatzky & Lederman, in press; adapted from Lederman & Klatzky, 1990a).
L M
PR
SC
U H
EN
CF
tex
2
1
1
1
1
hard
1
2
1
1
1
temp
1
1
2
1
1
1
wt
2
1
1
vol
1
1
2
1
global
shape
1
1
2
1
exact
shape
3
Table lb: Breadth of sufficiency and average duration* (s) for each EP
Lateral Motion (LM)
Pressure (PR)
Static Contact (SC)
Unsupported Holding (UH)
Enclosure (EN)
Contour Following (CF)
Durations from Lederman & Klatzky (1987; Expt. 1)
Breadth of
Sufficiency
3
3
4
5
6
7
Duration (s)
3.46
2.24
0.06
2.12
1.81
11.20
Links between EP and property components represent the precision of information about a property extracted by a particular EP. We have empirical data (Lederman & Klatzky, 1987; Expt. 2) that address this issue. These were obtained from a variant of the match-to-sample experiment discussed above. On any trial, subjects were now constrained to perform a single designated EP in conjunction with a named property, with all possible EP/property combinations performed over the experiment. Both accuracy and response times were measured. With these data, we were able to compare the relative precision with which each EP could extract
a designated property. The results are shown in Table
la in the form of an EP-property weight matrix. The entries are based on relative accuracy and speed. A cell entry of "0" indicates that subjects could not perform the property-matching task above chance level with the EP shown. An entry of "1" indicated sufficient, but not optimal performance. A "2" indicated that performance with the given EP was optimal and sufficient, although
it was not necessary. A "3" indicated that the given EP was necessary as well as optimal. Note that, in general, those EPs that were executed spontaneously in the initial
Table lb provides us with additional important information. By summing the number of non-zero cells across a row in Table la, we can represent the relative breadth of sufficiency of each EP; thus, Lateral Motion and Pressure each provide sufficient information about several different properties, whereas Enclosure and Contour Following provide coarse information about most object properties considered in this study. However, the breadth of property information provided by C ontour Following must be weighed against its relatively slow execution time, also shown in Table lb.
Links between EP components represent the extent to which the EPs may be co-executed, each extracting the property(ies) for which an EP is optimal/sufficient. For example, Lateral Motion and Pressure may be executed simultaneously, thus providing information about texture and hardness. We have developed a set of visible kinematic and dynamic parameters that formally differentiate the EPs; these parameters were derived from an extensive body of hand-movement data, based on a large number of common and custom-designed
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multidimensional objects tested over a wide range of experimental conditions. Values of four parameters were observed to occur reliably for a given EP across the different circumstances just described. Each parameter may be described as capturing some constraint inherent in an EP when it must be performed to extract certain
types of information. The parameters and their
stereotypic values are shown in Table 2a: these are Movement (static or dynamic), Direction (force applied normal or tangential to the surface), Region (of the object contacted by the end effector, i.e., surface, edge, or both), and Workspace constraint (supporting surface required or not). It is assumed that compatibility between a pair of EPs only exists to the extent that the constraints inherent in their parameter values can be
satisfied simultaneously through some manner of exploration. The information in Table 2a allows us to determine whether any two EPs are compatible or not. Clearly, two EPs having identical parameter values would be compatible; however, they could not be differentiated. Still, it is possible to achieve compatibility by selecting some form of exploration that satisfies the constraints inherent in both EPs. For example, if one EP must be executed along the edges, whereas another must be applied to both edges and interior surfaces, in satisfying the second less restrictive constraint the first more restrictive constraint is simultaneously satisfied. Hence, the two EPs can be considered to be compatible.
Table 2a: Values of EPs on four parameters (from Klatzky and Lederman, 1991 and in press)
SC PR L M EN CF U H
Movem'nt Static
Dynam. Dynam. Static
Dynam. Static Direct'n Normal
Normal Tang.
Normal Tang.
Normal Workspace
Region C
onstraint?
Surface No
Surface No Surface No Surf & Edges No
Edges No Surf & Edges Yes
Table 2b: C ompatibility relations between EPs (+ means compatible; - means incompatible), (from Klatzky and Lederman, 1991 and in press)
PR LM EN C F UN
SC + + + PR + + - + LM - +
EN - + CF
We have expressed such compatibilities and
incompatibilities in the form of an EP-EP weight matrix
(Table 2b). A "+" represents compatibility between two EPs (e.g., Lateral Motion and Pressure); a "-" represents an incompatibility (e.g., Static C ontact and Lateral Motion, because it is not possible to resolve the mismatch between two Movement parameter values). 3.3 Selecting an EP and the selection-extraction loop In keeping with the interactive activation perspective (e.g., McC lelland & Rumelhart, 1981), we treat haptic object identification as a parallel interactive process, with sequential constraints imposed by EP execution. Figure 4 shows how the process proceeds in a sequence of selection-extraction loops. During each step, an EP is selected and executed (along with any other compatible EPs). In this way, information about associated properties is extracted; the precision of the information is determined by the weights on the links between EPs and properties. Over a sequence of these loops, an object representation is built up and used as a probe to match against stored object representations. When a match criterion is satisfied, the search process is terminated, the object is said to be recognized.
Figure 4. The selection-extraction loop (from Klatzky & Lcdcrman, 1991 and in press). 3.3.1 C onstraints on EP selection
The primary goal during the selection-extraction loop is to choose an EP for execution, under a number of competing constraints, for example, the need to know as much about the object as quickly as possible or the need to learn about a desired object property. There may also be inherent biases that govern the use of certain EPs; for example, Contour Following is relatively time consuming
and, for humans, also fails to provide sufficiently precise
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contour information to effect fine shape discrimination. This may generally discourage the use of C ontour Following. In contrast, Enclosure might be generally favoured inasmuch as it is relatively fast and provides coarse information about many different properties (broad EP sufficiency).
In principle, these constraints and biases can be represented by the weights between different components in our system. For example, associations
between specific objects can be represented by associative weightings within the object component, while expectations concerning the diagnostic value of
various properties can be represented by connections between the property and object components. Hand movement precision and breadth of sufficiency are represented by connections between property and EP components (Table lb); EP compatibility (Table 2b) is determined by constraints inherent in the EP parameters
(Table 2a). Finally, intrinsic biases, such as duration of execution, may be represented as item-specific bias terms.
In this first output table, the complete weight matrix of Table la was used, thus including positive weights on all EPs that were at least sufficient for extracting a given property. This might represent a condition in which the observer wishes to extract as much information as possible initially. Note that no matter which EP is clamped, the maximum activation level always occurs for
an Enclosure, which is not only broadly sufficient but also compatible with other EPs. The next most active
element is Unsupported Holding, which is compatible with Enclosure. Thus, this pair of EPs could be selected and executed within the same loop. Table 3. Activation level of each EP after constraint satisfaction when each property has been activated externally. Results arc given for two sets of weights. Also shown are the time to relax (in multiples of 52 updates) and the goodness level at the point
of relaxation, (from Klatzky & Lederman, 1991 and in press) 3.3.2 A constraint satisfaction approach
Collectively, these constraints function to select an EP, given a specific object and certain prior expectations. In connectionist terms, the EP selection process can be treated as a constraint satisfaction algorithm, in which the weights serve as constraints to be progressively relaxed until some elements are maximally activated. A system with symmetric weights and asynchronous updating minimizes a cost function over the set of constraints (weights), eventually selecting an optimum state or activation pattern over the associated units (Hopfield, 1982). In our case, constraint satisfaction serves as a method for selecting the next EP in a sequence during manual exploration. The weights are theoretically and/or empirically derived associations among EPs and properties (and potentially, objects). As the system progressively relaxes, a stable activation pattern eventually emerges that is used to predict which EP will be executed next in some exploratory situation. To consider the consequences of the associations between EPs and properties and the compatibilities between EPs, we implemented the weights in Tables la and 2b as a constraint satisfaction system. The nodes represented the EPs and properties. This is equivalent to examining a single generic object.
Table 3a shows the activation level of each EP (as well as the time to relax and goodness level at the point of relaxation) after constraint satisfaction. Each property was clamped to represent an externally set property goal.
A. FULL WEIGHT MATRIX
Texture .45 .58 .48 .63* .56 .56 8 9.1 Hardness .40 .60 .47 .63* .59 .54 7 9.1 Exact Sh. .36 .54 .58 .60* .55 .53 9 8.6 Global Sh. .33 .55 .46 .66* .59 .57 9 9.0 Size .33 .55 .46 .66* .59 .57 9 9.0 Weight .33 .55 .47 .64* .62 .54 9 8.8 Temp. .39 .58 .46 .63* .59 .59 8 9.2
B. OPTIMAL EP-PROPERTY WEIGHTS ONLY Texture Hardness Exact Sh. Global Sh. Size Weight Temp.
.53* -.36 .39 -.39 -.39 -.40 -.40
-.34 .52* -.38 .37 .37 .38 .37
.50 -.51 .62* -.50 -.50 -.50 -.50
-.50 .50 -.50 .59* .59* .50 .50
-.46 .46 -.46 .45 .45 .56* .46
-.46 .46 -.46 .45 .45 .46 .56* 12 12 11 11 11 11 11 4.4 4.4 4.7 4.5 4.5 4.4 4.4
*highest activation for the given property
Table 3b models a different situation, which might occur following the initial stage of coarse exploration -- if more precise information is required than can be extracted by an EP that is merely sufficient for that property, the optimal EP would be required. To model this we used a second set of weights in which only optimal EP-property weights in the matrix were included. Note the very different activation levels - here we see that clamping a particular property
results in the optimal EP being selected.
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3.4 Behavioural support for a constraint satisfaction
approach
3.4.1 Human experiments on the 2-stage EP sequence
The results of our modeling are supported by evidence of a 2-stage exploratory sequence, which was adopted by subjects during an object classification task (Lederman & Klatzky, 1990b). On each trial, subjects were initially asked a yes/no question of the type: "Is this X further a Y?" (e.g., "Is this wood-working implement further a piece of sandpaper?"). An object was then placed in the subjects hands for exploration; on half the trials, the object was in fact an exemplar of both X and Y classes named in the question, while on the remaining trials, an object from the same X but different Y class was presented (e.g. file). The most diagnostic property for each object class named in the questions had been determined previously in a separate experiment (Lederman & Klatzky, 1990b).
Position of EP in Sequence
Figure 5. Cumulative percentage of occurrence of each EP as a function of position in the exploratory sequence (from Lederman & Klatzky, 1990b). The solid lines indicate the grasp/lift combination. Static Contact is not included because it occurred very infrequently.
The hand movements from each trial were analyzed as a sequence of EPs. This analysis indicated two separate stages of manual exploration, as evident in Figure 5. Each function depicts the cumulative percentage of EP occurrence as a function of serial position in the EP sequence. The two solid dark lines indicate that the first two EPs in the sequence were an Enclosure followed by an Unsupported Holding, which together comprise what we refer to as a grasp/lift routine. Both of these are relatively broadly sufficient and would presumably provide considerable coarse information about any object. The remaining EP functions occurred after this initial exploratory sequence,
the particular EP being predicted by the property that was known to be most important for object identification. For example, since texture was most diagnostic for deciding whether or not the wood-working
tool in the subject's hands was a piece of sandpaper, we predicted that Lateral Motion would be selected following execution of a grasp/lift routine, since it is optimal for extracting texture. Such predictions were confirmed. The second stage of exploration was more specifically directed toward extracting further precise information about the critical property. In short, these data on manual exploration during object identification support our approach to exploratory control as a constraint satisfaction process.
3.4.2 Experiments on the selection-extraction loop and
property extraction
In addition to determining how EPs are selected, our model also addresses how EP selection affects the precision with which information about an object can be extracted. We assume that the strength of relations between EPs and properties (Table la) should predict
the extent to which an object's properties may be perceived and learned using a particular form of manual exploration. This assumption has several implications. Learning about a property should be fastest when exploration involves an optimal EP, because less sampling is necessary to obtain a given amount of information. When only a single optimal EP is executed, incidental information about other properties will be restricted to those for which that EP is sufficient. Selecting compatible EPs allows for co-execution, thus making available information about all properties for which either is sufficient. Presumably, selecting one EP
is assumed to prevent the co-occurrence of any other incompatible EPs also selected; rather, these must be performed in sequence. If the eliminated EPs are necessary for a given property, then no learning about
that property will occur.
C
learly, these predictions highlight the "gatekeeper" role that EPs play during object perception.
We have investigated the role of EPs in limiting accessibility to object properties in a series of experiments (Klatzky et al., 1989; Reed et al, 1990; Lederman et al, in press). The tasks generally required subjects to learn to classify sets of multidimensional objects into groups according to different classification rules. For these tasks, we designed sets of objects that varied factorially in a number of properties, such as texture, hardness, shape and size. At least one of these properties was used to divide objects into categories.
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Take an example of a 1-property rule:, the roughest objects were all in category "A", the intermediate roughness objects all in category "B", and the smoothest all in category "C ". Over a set of trials, subjects were required to indicate to which category the objects in a set belonged. Response time for object classification was measured as time from contact to verbal response. Across one experiment, the categorization tasks demanded that subjects extract information about one or more diagnostic properties of each presented object. We expected response time to decrease as the sets were repeatedly presented, because subjects would learn to eliminate those EPs that were not optimal for learning about the diagnostic property. And indeed, we note in Figure 6 that response time decreased to an asymptotic value for three different sets of objects regardless of the number of diagnostic object properties used to classify objects in a given set (1, 2, or 3).
Figure 6. Mean response time for classifying each object based on 1, 2-redundant, and 3-redundant property classification rules (adapted from Klatzky et al, 1989). The 1- and 2-redundant functions were both produced by averaging data from several different property-classification conditions.
One important reason for the reduction in response time becomes evident when we examine the corresponding EPs performed over successive time periods in the 1-property case (Figure 7). We observe that subjects chose to streamline their manual exploratory activity over time. Thus, EPs that were optimal for extracting the diagnostic property (texture, shape, or hardness) continued to occur frequently and across blocks; in contrast, the other EPs scored in the study occurred less often initially and subsequently declined. Similar results were obtained for the 2- and 3-property categorization conditions discussed next.
Figure 7. Proportion of EP occurrence (Lateral Motion, Pressure, C ontour Following, and Enclosure) over sequential periods for three different 1-property classification conditions (adapted from Klatzky et al. 1989). LM= Lateral Motion; PR= Pressure; EN= Enclosure; C F=C ontour Following.
We also predicted that when more than one property redundantly defines the categories (e.g., all "A"s are both very rough A N D very hard, all "B"s are of intermediate roughness A N D intermediate hardness, and all "C "s are both very smooth A N D very soft), categorization times should be faster than with a 1-property classification rule. This is evident in Figure 6 by the fact that the 2-and 3-property curves lie below the 1-property curve, indicating a "redundancy gain". We argue that this reflects the savings resulting from the fact that the EPs that were optimal for extracting two properties were compatible (e.g., Lateral Motion and Pressure for redundant texture and hardness). That there is no additional savings from adding a third redundant dimension (shape) to redundant texture and hardness specifically reflects incompatibility of Contour Following with either Lateral Motion or Pressure.
We further predicted that when a single EP is used to explore an object, incidental knowledge about other properties will accrue depending on the weight between each property and that EP. To assess this, we turn now to another experimental approach, which involves what we have called the "withdrawal" paradigm. We presented subjects with sets of objects that were redundantly defined with different 2-redundant property classification rules (texture/hardness; texture/shape; hardness/shape). Subjects were initially told to classify the objects on the basis of a single named dimension (e.g., texture), even
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though objects varied redundantly on two dimensions (texture and hardness); when performance asymptoted, the object set was switched to a one-dimensional rule (i.e., objects only varied in texture) since values of the second property (hardness) were now held constant. That is, variation on the second property was "withdrawn". We reasoned that if subjects had previously incidentally learned about the second property, then their response times should increase just after it was withdrawn. Figure 7 presents the results. Note a strong withdrawal effect for texture/hardness redundancies, regardless of which dimension was withdrawn. In contrast, withdrawal effects for the other two combinations were very small, and typically not statistically significant. This result was also expected as Contour Following (for shape) is incompatible with both Lateral Motion (texture) and Pressure (hardness) for the set of planar stimulus objects used: shape information was only available at the edges, while texture and hardness were both found in the interior surface areas. In contrast, when texture and shape are available in the same local region (created by using fully 3-dimensional ellipsoids of revolution), we obtained strong withdrawal effects. We predicted this outcome because information about both shape and texture was simultaneously available from the now fully compatible EPs, C ontour Following and Lateral Motion.
Figure 8. C lassification response time as a function of period for three 2-redundant property classification rules. Separate effects of targeting one property while withdrawing the second are shown in each panel. X/Y -> X should be read as properties X and Y are both initially presented together; Property Y is subsequently withdrawn, (from Klatzky et al, 1989)
property was targeted. We predicted that knowledge about objects would be determined on the basis of which EPs were spontaneously selected. The prediction was tested experimentally by having subjects sort, according to perceived object similarity, the complete set of multidimensional planar shapes used in the immediately preceding series. We found (Klatzky et al, 1987; Summers et al, submitted) that when subjects could use only haptic exploration, they preferred to sort by hardness and texture, their selection apparently reflecting the cost (speed, accuracy) of executing a C ontour Following to extract shape information. However, note what occurred when the instructions stressed attending to visual images or to visual cues when they were also provided. Such instructions presumably created a bias toward shape; as would be expected, Contour Following and Enclosure were selected most frequently. 4 A p p l i c a t i o n to r o b o t i c e x p l o r a t i o n 3
In keeping with Gibson's earlier observations (e.g. 1966), much research with humans and other living organisms has highlighted the importance of active exploration in perceptual activities. Our own work again confirms this general principle with respect to human haptic object processing. In the robotic domain, Bajcsy (1989) may be credited with emphasizing the need for exploration, particularly when information about object properties must be used to interact with unstructured environments. Presumably, such is true whether or not identification is requisite.
Bajcsy and a number of others have recently linked the biological and robotic exploration domains by specifically adoping the concept of an EP as a systematic testing procedure, and by implementing robotic versions of the human EPs described above (e.g., Allen & Michelman, 1990; Bajcsy & Campos, 1992; Sinha, 1992; Stansfield, 1988). However, neither the selection of particular EPs nor the sequence in which these should be performed is intuitively obvious. We advocate adopting the experimental paradigm used by Lederman & Klatzky (1987, Expt. 2; see Section 3.2 of this paper) to develop a more general robotic search solution for EP selection when multidimensionally varying objects are explored. This experimental approach allows the
systematic determination of the relative performance characteristics of the set of robotic EPs selected, which could then be used in conjunction with a constraint satisfaction approach to select efficient EP sequences. In the previous set of experiments, a particular property was targeted. Another series of experiments focused on EP selection under conditions in which no
3
Section 4 is based on material from Lederman et al (1992).
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Consider the following scenario. We begin with the view of an EP as a motoric routine that is optimal for extracting one property, although it may also be sufficient for extracting several others. The properties and associated EPs will depend upon the particular robotic end effector and sensing system selected, and could be quite different from what humans use (unless an anthropomorphic design has been deliberately adopted). Having selected a set of properties and EPs, one can now experimentally test the relative constraints on EP performance, however this is defined by the roboticist. The constraints on human EPs listed in Table la may be applied to any exploring system, along with any others that are specifically relevant to the
robotic domain. The experiment described in Section 3.2 can be used as a methodological guide for systematically evaluating the relative performance of each EP in extracting each property; note that the tasks should involve different levels of property discrimination if EP performance is to be fully and properly assessed. Also relevant to this approach is the extent to which robotic EPs may be co-executed, that is, the issue of EP compatibility. To determine the compatibilities and incompatibilities between EPs, the EP descriptions must be specifically defined in terms of robotically relevant constraint parameters. C
ollectively, the results concerning the relative strengths of the EP-to-property and inter-EP-compatibility associations can be used to rank robotic EPs for use in associated computational models of EP selection.
5 S u m m a r y In closing, I would like to repeat my claim that the scientific study of biological organisms can further the development of current sensor-based robots in many different ways, without being constrained by, or limited to, anthropomorphic design. On a general level, I have argued (Lederman & Pawluk, 1992; Lederman et al, 1992) that such work: a) addresses many of the same problem domains, b) provides an example of, and framework for, designing working, multilevel integrated systems, and c) offers valuable suggestions for presenting robotically extracted information to a human operator in teleoperation. Further, d) the scientific method highlights the value of properly constraining the problem, formulating testable hypotheses, designing rigorous and unbiased experimental tests of the hypotheses, and using statistical techniques for assessing the validity, reliability and generality of the experimental findings. On a more specific level, based on our familiarity with the scientific results of experiments on biological touch, my colleagues and I have further proposed a number of particular suggestions for designing robotic tactile/haptic systems, including the
example discussed in Section 4. For biological scientists, who attempt to understand the bases of natural intelligence, and roboticists, who attempt to create such behaviour in machines, serious collaboration may provide a new and potentially valuable approach to robotic design.
Acknowledgments
The research program on human haptics is based on a long-standing collaboration with Dr. Roberta Klatzky (University of California at Santa Barbara). I also wish to thank Dr. Ruzena Bajcsy (Pennsylvania) for numerous fruitful discussions about related biological and robotic issues. My thanks to the other valuable collaborators with whom some of the work reported here was performed: Dr. C athy Reed (Pennsylvania), Dr. C raig Summers (Laurentian), and Dianne Pawluk (Harvard). Cheryl Wilson helped format this manuscript for publication. The paper was prepared with the financial assistance of the Institute for Robotics and Intelligent Systems, a Canadian federal centre of excellence, and the Information Technology Research C entre of Ontario centre of Excellence.
References
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as if 用法归纳 1． as if 从句的作用as if一般可由as though来代替。 引导表语从句，可在look, seem等系动词后。如： He looked as if / though he was ill. 他看起来好像生病了。It seems as if it is going to rain.看来好像要下雨了。 引导方式状语从句。如： I remember the whole thing as if / though it happened yesterday. The child talked to us as if he were a grown-up. 那个小孩跟我们讲话就像个大人一样。 2． as if 从句的语气及时态 ①as if从句用述语气。当说话者认为句子所述的是真实的或极有可能发生或存在的事实时， It sounds as if it is raining. 听起来像在下雨。 He talks as if he is drunk. 从他说话的样子来看他是醉了。 ②as if从句用虚拟语气。当说话人认为句子所述的是不真实的或极少有可能发生或存在的情况时， 表示与现在事实相反，谓语动词用一般过去式。如： You look as if you didn’t care. 你看上去好像并不在乎。When a pencil is partly in a glass of water, it looks as if it were broken． 表示与过去或与谈话时间为止发生的事实相反，谓语动词用
“had +过去分词”。如： He talked about the Great Wall as if he had been there before.说起长城来好像他以前去过那里。 The girl listened as if she had been turned to stone.女孩听着，一动也不动，像已经变成了石头。 表示与将来事实相反，谓语动词用“would / could / might + 动词原形”。如： He opened his mouth as if he would say something. 他开嘴好像要说什么。 It looks as if it might snow. 看来好像要下雪了。 3． as if从句中的省略。如果as if 引导的从句是“主语+系动词”结构，可省略主语和系动词等成份，这样as if 后就只剩下名词、不定式、形容词（短语）或动词-ing形式等。 He acts as if (he was) a fool. 他做事像个傻子。 He paused as if (he was going) to let the sad memory pass. The girl left the room hurriedly as if (she was) angry. 女孩匆忙离开房间，好像生气的样子。 From time to time, Jason turned round as if (he was) searching for someone. 词汇学习 1. A smelly gas came out of the cracks.
a，an和the用法区别 a，an和the 用法你们都了解了吗?今天给大家带来a，an 和the 用法，希望能够给帮助到大家，下面就和大家分享，来欣赏一下吧。 英语无捷径|| a，an和the 用法区别，记住就不要再犯错了! 1. 不定冠词 a 还是an 判断用a还是用an的依据是其后的单词的发音，而不是字母!以元音音素开头的单词前用an来修饰，以辅音音素开头的单词前用a来修饰。 常用的搭配规律： 1) 多数以元音字母(a、e、i、o、u)开头的单词，首音节都是发元音：I atean an apple yesterday. It’s sweet.我昨天吃了一个很甜的苹果。An American usually speaks English.美国人通常讲英语。Driving carefully helps avoid an accident.谨慎驾驶有助于避免事故。Learn how to survive anearthquake.学习如何在地震中生存。He went to aninterview yesterday.他昨天去面试了。She is cutting anonion.她在切洋葱。
2) 元音字母U开头的单词，首音节可能发元音或者辅音[ju:] 【j】a university [junivsiti] studenta unique [junik] styleA university is where you do your degree.大学是你获得学位的地方。Have you ever seena UFO/an unidentified flying object?你见过不明飞行物吗? 3) 记住三个h开头却不发音的名词：hour，honst，honourHalf an hour has passed.半小时过去了。Its an honour to be invited to your dinner.很荣幸被邀请参加你的晚宴。 4)European 欧洲人，欧洲的，元音字母e不发音，首音节是[ju:]He is a European, and he has lived in Hangzhou for 10 years. 他是欧洲人，在杭州住了10年。 5)如果名词前有形容词做定语修饰，则我们看形容词是辅音还是元音开头。There stands an oak tree in front of the house.房子前面有一棵橡树。An important lessonIve learned from it is never to give up.我从中学到的重要一课是永不放弃。He drives an old car.他开一辆旧车。In those years he was just an unknown pianist.在时的他只是一个默默无闻的钢琴家。An honest man never lies.诚实的人从不说谎。 2. 定冠词the 定冠词the : 表示特指某事物或人
like的用法大全 今天给大家带来了like的用法，快来一起学习吧，下面就和大家分享，来欣赏一下吧。 喜欢和爱：like的用法大全 I think anybody who falls in love is a freak. Its a crazy thing to do. Its kind of like a form of socially acceptable insanity. ——Her 我觉得陷入爱河的人都是疯子。谈恋爱本来就是件疯狂的事，只不过是大众可以接受的那种。 ——《她》 一、下面我们来看看like有几种含义 adj. 1.相似的having similar qualities to another person or thing The brothers are very like. 这几个兄弟很相像。
2.相同的;同类的closely resembling the subject or original Things which seem to be like may be different. 看来相同的东西实际可能不同。 adv. 1.【口】可能，多半likely, probably 2.同样地;在相同程度上to some extent conj. 好像，如同in the same way as Even though me were friends, it was just like he didnt know me at all. 尽管我们是朋友，他表现得好像根本不认识我。 n. (冠以物主代词)同样的人(或事物);匹敌者a person or thing that is similar to another Have you even heard the like of it? 你听见过这样的事情吗? 2.爱好the things that you like
a a n和t h e的用法球类运动前面不用冠词 在操场上是固定搭配ontheplayground （一）不定冠词：a∕an的用法： ⑴表示一个 例：Shehasacleverson.她有个聪明的儿子。 ⑵表示每个 例：wehave3Englishclassesaweek.我们每周上3次英语课。 ⑶表示某个 例：Thebookis∕waswrittenbyastudent.这本书是一个学生写的。 ⑷表示某类之一 例：Iamateacher,heisadoctor.我是一名老师，他是一名医生。 ⑸第一次提到的人或物用不定冠词表示，再次提到时用定冠词。 例：Ihaveabike,thebikeisgreen.我有一辆自行车，这辆自行车是绿色的。 ⑹用于可数名词单数形式前，表示类别。 例：Ateachermustlovehisstudent.老师应该爱学生。 ⑺用于表示价格，速度，比率，时间等意义的名词前 例：3timesaday.一天三次 10yuanameter.10元一米
⑻用于抽象名词前，表示一种… 例：anewculture一种新文化 ⑼用于句型：“a∕an+Mr.∕Mrs.∕Miss.+姓氏”中 例：aMr.Wang一位姓王的先生（不认识） Mr.Wang王先生（认识） ⑽用于某些短语中 例：alotof许多，大量 haveagoodtime玩的开心，过的愉快 （二）定冠词the的用法： ⑴表示特定的人或事物 例：Thebookonthedeskismine.桌子上的书是我的。 ⑵表示听话人，说话人彼此都很熟悉的人或事物 例：WhereisTom汤姆在哪儿？ Heisintheroom.他在屋里。 ⑶第一次提到的人或物用不定冠词表示，再次提到时用定冠词。 例：Ihaveabike,thebikeisgreen.我有一辆自行车，这辆自行车是绿色的。 ⑷表示世界上独一无二的东西（专有名词除外） 例：Thesun太阳
#pragma code_seg 格式如： #pragma code_seg( [ [ { push | pop}, ] [ identifier, ] ] [ "segment-name" [, "segment-class" ] ) 该指令用来指定函数在.obj文件中存放的节,观察OBJ文件可以使用VC自带的dumpbin命令行程序,函数在.obj文件中默认的存放节为.text节，如果code_seg 没有带参数的话,则函数存放在.text节中。 push (可选参数)将一个记录放到内部编译器的堆栈中,可选参数可以为一个标识符或者节名 pop(可选参数)将一个记录从堆栈顶端弹出,该记录可以为一个标识符或者节名identifier(可选参数)当使用push指令时,为压入堆栈的记录指派的一个标识符,当该标识符被删除的时候和其相关的堆栈中的记录将被弹出堆栈 "segment-name" (可选参数)表示函数存放的节名 例如: //默认情况下,函数被存放在.text节中 void func1() {// stored in .text } //将函数存放在.my_data1节中 #pragma code_seg(".my_data1") void func2() {// stored in my_data1 } //r1为标识符,将函数放入.my_data2节中 #pragma code_seg(push, r1, ".my_data2") void func3() {// stored in my_data2 } int main() { } 例如 #pragma code_seg(“PAGE”) 作用是将此部分代码放入分页内存中运行。 #pragma code_seg() 将代码段设置为默认的代码段 #pragma code_seg("INIT") 加载到INIT内存区域中，成功加载后，可以退出内存
l i k e的基本用法习题小 学 集团企业公司编码：（LL3698-KKI1269-TM2483-LUI12689-ITT289-
关于l i k e的练习一．选择 1.Doyoulike______housework A.do B.does C.doing D.did 2.I______playingbasketball. A.amnotlike B.don’tlike C.isn’t D.likes 二．填空 1.Ilike_____________.(dance) 2.Helikes____________.(swim) 3.Youlike____________.(eat) 4.Theylike____________.(dive) 5.We?like___________(sing)Englishsongs. 6.Myfatherlikes_______(play)golf. 7.Eddie'smotherlikes________(cook)?? 8.---Doyoulikedancing ----Yes,I________. 9.-----Canyoudancing ------Yes,I__________. 10.-----Doyoulikeskating -----No,I________. 11.I_______(like)reading.
12.She_________(like)reading. 13.She_______________(like)reading. 三，改句子。 1.Ilikeswimming.(改成否定句) ______________________________________________________________ 2.Shelikesdancing.(改成否定句) ______________________________________________________________ 3.Doesshelikesinging(肯定回答) _____________________________________________________________ 4.Dotheylikeplayinggames(否定回答) ______________________________________________________________ 5.Shedoesn’tlikesinging.(改成肯定句) ______________________________________________________________
想说“这个/那个”的时候用the, 想说“（任意）一个”的时候用a。 专指： 专有名词KFC专指肯德基 地名国家名什么的独一无二的事物 类指:凡是同一类：都是苹果有大有小，the big one ，the small one 泛指：很不具体的，无特别指定对象，是和“特指”相对的；例如a girl就是一女孩，普通吧。此时也可是泛指一类人。a beautiful girl is like a evil. 特指：有特别指定对象，和“泛指”相对。the girl可能是上文提到过或者对话人都心知肚明的女孩，是有一个具体对象的. 冠词是用在名词前面，帮助说明名词所指的人或事物，是泛指还是特指的词。冠词是一种虚词。冠词分不定冠词（The Indefinite Article）和定冠词（The Definite Article）a, an是不定冠词，the 是定冠词。 an, a是不定冠词，仅用在单数可数名词前面，表示“一”的意义，但不强调数目观念。a用在以辅音（指辅音音素）开头的词前，an用在以元音（指元素音素）开头的词前 不定冠词的用法： 1. 表示人或事物的某一类 A steel worker makes steel. A plane is a machine that can fly. 2. 表示某一类人或事物中的任何一个。 This is an apple. His father is a teacher. 3. 泛指某人或某物，但不具体说明何人何物。 A comrade is waiting for you downstairs. I met an old man on my way to school. 4. 表示“一个”的意思
“like”前与后-like的用法 like 表示“喜欢”，我们在使用时要重点注意它的前前后后。 I．like前 like和其它行为动词一样，在肯定句中，主语是第一人称、第二人称以及第三人称复数时谓语动词一般现在时用原形，但第三人称单数时要加词尾-s，如：Many people like science. 很多人喜欢科学。 My teacher likes reading books. 我的老师喜欢读书。 但变为疑问句和否定句时要注意： 1. 主语是第一人称（单数和复数）I、we、第二人称you以及第三人称复数they、Li Ping and Mary、the desks等，变为疑问句和否定句时要借助助动词do，谓 语动词不变。如： I like table tennis very much. → Do you like table tennis very much. They like playing basketball. → They don’t like playing basketball. 2. 主语是第三人称单数时，变为疑问句和否定句时要借助助动词does，谓语动 词改为原形。如： Mary l ikes pizza. → Mary doesn’t like pizza. He likes to play the piano. → Does he like to play the piano? II．like后 1. like后跟可数名词表示类别时，名词通常用复数形式，不用单数形式。如： Mary likes dumplings. 玛丽喜欢饺子。 2. like后跟不可数名词时，名词通常用原形。如： Mike doesn’t like pizza. 迈克不喜欢比撒饼。 3. like后跟动词，要用-ing形式。 I like taking photos. 我喜欢照相。 4. like后跟动词，也可用to do形式。 Do you like to play computer games? 你喜欢玩电脑游戏吗？ 注意：通常情况下用-ing形式和to do形式没有较大区别，可互相换用。但有时用-ing形式表示习惯性动作，而用to do形式表示具体的一次动作 like作动词时，意为“喜欢”。常见用法有以下两种： ①“like + 名词 / 代词”表示“喜欢某人或某物”。例如： She likes her students very much. 她非常喜欢她的学生。 This is my computer. I like it a lot. 这是我的电脑，我非常喜欢它。 ②like doing sth.和like to do sth.都表示“喜欢做某事”。like doing sth.着重于习惯、 爱好；like to do sth.着重某次具体的行为或动作。例如： I like reading, but I don’t like to read this evening.
a, an, the的用法 冠词是虚词，本身不能单独使用，也没有词义，它用在名词的前面，帮助指明名词的含义。英语中的冠词有三种，一种是定冠词（the Definite Article），另一种是不定冠词（the Indefinite Article），还有一种是零冠词（Zero Article）。 an, a是不定冠词，仅用在单数可数名词前面，表示“一”的意义，但不强调数目观念。a用在以辅音（指辅音音素）开头的词前， an用在以元音（指元素音素）开头的词前。the是定冠词，修饰特指名词翻译成“这个”。如果泛指某物,用a,/an,具体指某物的话，用the. 注意：（1）当我们使用an时，条件有三：①这个名词的读音必须是以元音音素开头--即它的音标的第一个音素是元音，而不是说它是以元音字母开头。②它必须是个可数名词。③它还必须是个单数名词。我们常常见到这类用法： a university 一所大学 an hour 一个小时 an orange 一只桔子 an engineer 一位工程师 an ordinary man一个普通人 an honest person一位诚实的人
a boy, a city, a girl, a useful animal , an old man, an honest boy, a bad apple, a tall elephant, a university(虽然u 是元音字母，但不读元音), an hour 一个小时 (虽然h 不是元音，但单词读音是元音开头) 不定冠词有a和an两种：a用于辅音音素开头的词前，an用于元音音素开头的词前。例如： a boy, a city, a girl, a useful animal , an old man, an honest boy, a bad apple, a tall elephant, a university(虽然u 是元音字母， 但不读元音), an hour 一个小时 (虽然h 不是元音，但单词读音是元音开头) 1．指某类人或事物中的任何一个。 An elephant is bigger than a horse. A car runs faster than a bike. （2）指人或事物,用来表示“—”的意思，但不强调数的观念，只说明名词为不特定者。指人或事物即不具体说明是何人何物。。例如： There are seven days in a week. We have three meals a day. A teacher is looking for you. We work five days a week.
#pragma预处理命令 #pragma可以说是C++中最复杂的预处理指令了，下面是最常用的几个#pragma 指令： #pragma comment(lib,"XXX.lib") 表示链接XXX.lib这个库，和在工程设置里写上XXX.lib的效果一样。 #pragma comment(linker,"/ENTRY:main_function") 表示指定链接器选项/ENTRY:main_function #pragma once 表示这个文件只被包含一次 #pragma warning(disable:4705) 表示屏蔽警告4705 C和C++程序的每次执行都支持其所在的主机或操作系统所具有的一些独特的特点。例如，有些程序需要精确控制数据存放的内存区域或控制某个函数接收的参数。#pragma为编译器提供了一种在不同机器和操作系统上编译以保持C和C++完全兼容的方法。#pragma是由机器和相关的操作系统定义的，通常对每个编译器来说是不同的。 如果编译器遇到不认识的pragma指令，将给出警告信息，然后继续编译。Microsoft C and C++ 的编译器可识别以下指令：alloc_text，auto_inline，bss_seg，check_stack，code_seg，comment，component，conform，const_seg，data_seg，deprecated，fenv_access，float_control，fp_contract，function，hdrstop，include_alias，init_seg，inline_depth，inline_recursion，intrinsic，make_public，managed，message，omp，once，optimize，pack，pointers_to_members，pop_macro，push_macro，region, endregion，runtime_checks，section，setlocale，strict_gs_check，unmanaged，vtordisp，warning。其中conform，init_seg, pointers_to_members，vtordisp仅被C++编译器支持。 以下是常用的pragma指令的详细解释。 1.#pragma once。保证所在文件只会被包含一次，它是基于磁盘文件的，而#ifndef 则是基于宏的。
【Like用法归纳】 like 一词具有多种词性和词义，以及多种用法。 VJ 用作动词（V.）,意思为”爱，爱好，喜欢”，无进行时态，既表示对人或者事物的真挚的感情, 又表示”对某事有着浓厚的兴趣、爱好”。后面可以接名词、代词、动名词或者不定式。1」ike+名词/代词，意为”喜欢某人或某物”。 汤姆非常喜欢鱼。 王老师是个好老师，我们都喜欢他。 我的小弟弟非常喜欢草莓。 2」ike to do sth. 意为”喜欢做某事（偶尔的、一次性的具体的行为）” It is too hot, I like to swim today. 今天太热了，我想去游泳。（只有今天想去，一次性的行为） 今天我想和你聊一下。 3.like doing sth. 意为”喜欢做某事（经常或习惯地）”。例如： It is too hot, I like swimming in summer. 天太热了，整个夏天我都喜欢去游泳。（表示”经常性的动作”，已经形成习惯） He likes sin gi ng. 他喜欢唱歌。 The boy likes wash ing hands in cold water. 这个男孩喜欢在冷水里洗手。 4.like sb. to do sth .意为”喜欢某人做某事”。例如： Our En glish teacher likes us to ask questio ns. 我们的英语老师喜欢我们提问。 5. would like to do sth. (=want to do sth.) 意为”想要做某事”。例如: rd like to go shoppi ng with you. 我想要和你一起去买东西。 6. would like sb. to do sth. 意为”想要某人做某事”。 rd like you to meet my pare nts. 我想要你见见我的父母亲。 like sb. to do sth. 喜欢某人做某事女口：① ②③ prep. be like/ look like +名词或代词作宾语，意为”像??…;跟??…一样”。例如:
a, an, the的用法 （一）a, an的用法 1.a：用于辅音音素（不是辅音字母）开头的词前，表示单数的数量：“一”。但小学阶段学生一般理解为：单词开头第一个不是元音用a a book 一本书 a desk一张书桌 2.an：用于元音音素（不是元音字母）开头的词前，表示单词的数量“一”。但小学阶段学生一般理解为：单词第一个字母是元音字母用an an apple 一个苹果 a big apple一个大苹果an old man一位老人 3.元音字母：Aa, Ee, Ii, Oo, Uu 4.特殊情况：an hour（h是辅音字母，但它不发音） 5.辅音字母：26个字母中，除了5个元音字母，其他是辅音字母 练习一：用a, an, the填空 1._____book 2._____apple 3._____orange 4._____uncle 5._____eraser 6._____math book 7._____new book 8._____old book 9._____teacher 10.______English 11.____ hour 12._____good teacher 13.____idea 14.____good idea 15.____island 16.____ astronaut （二）the的用法 1.一般用于特指：The girl is my sister. 那个女孩是我的妹妹。 2.第二次出现：There is a book on the desk. The book is mine. 书桌上有一本书，那本书是我的。 3.世界上独一无二：the moon月亮the earth地球the Great Wall长城the sun太阳 4.固定的词组：on the desk在书桌上 5.形容词最高级前：Jenny is the oldest girl in the class. 在班里珍妮是年纪最大的女孩。 练习二：选择 ( )1.That’s _____ island. I like to go there. A.a B.an C.the ( )2.I have ___ new book. Jenny has ___ old book. A.a/ an B.an/ a C.an/ an ( )3.This is ___ orange. That’s ____big orange. A.a/ an B.an/ a C.an/ an ( )4.Yesterday I studied English for ____ hour. A.an B.the C.a ( )5.This is a bag. ____ bag is Tom’s. A.A B.An C.The ( )6.___ man is Lisa’s father. A.A B.The C.An ( )7.____ Great Wall is in China. A.A B.The C.An ( )8.My English book is in ___ desk. A.the B.an C./ ( )9.She has ___ idea. It’s a good idea. A.a B.an C.the 练习三：a, an, the填空 1.Tom wants to be _____ scientist(科学家).He wants to go to ___ moon. 2.Jenny has ______ uncle and _____ cousin. 3.I have _____ eraser. _____ eraser is in my bag. 4._____ boy is Tom’s brother. 5.The bird was here ______ hour ago. 6.There is _____ English book on the desk. ______ English book is Tom’s. 7.I have _____ book. It is _____ English book. And it has _____ goos story. _____ book is in my bag. 8.There is _____ cat under the tree. _____ cat is playing.
#pragma data:code 在Keil中为了节省数据存储器的空间，通过“code”关键字来定义一个数组或字符串将被存储在程序存储器中： uchar code buffer[]={0,1,2,3,4,5}; uchar code string[]="Armoric" ; 而这类代码移值到ICCAVR上时是不能编译通过的。我们可以通过"const" 限定词来实现对存储器的分配： #pragma data:code const unsigned char buffer[]={0,1,2,3,4,5}; const unsigned char string[]="Armoric"; #pragma data:data 注意： 《1》使用ICCAVR6.31时，#pragma data :code ;#pragma data:data ; 这些语法时在"data:cod"、"data:data"字符串中间不能加空格，否则编译不能通过。 《2》const 在ICCAVR是一个扩展关键词，它与ANSIC标准有冲突，移值到其它的编译器使用时也需要修改相关的地方。 在ICCAVR中对数组和字符串的五种不同空间分配： const unsigned char buffer[]={0,1,2,3,4,5}; //buffer数组被分配在程序存储区中 const unsigned char string[]="Armoric" ; //stringp字符串被分配在程序存储区中 const unsigned char *pt //指针变量pt被分配在数据存储区中，指向程序存储区中的字符类型数据 unsigned char *const pt //指针变量pt被分配在程序存储区中，指向数据存储区中的字符类型数据 const unsigned char *const pt //指针变量pt被分配在程序存储区，指向程序存储区中的字符类型数据 unsigned char *pt //指针变量pt被分配在数据存储区中，指向数据存储区中的数据 请问#pragma data:code和#pragma data:data是什么意思？ 前者表示：随后的数据将存贮在程序区，即FLASH区，此区只能存贮常量，比如表格之类。
初中英语中Like用法归纳 like一词具有多种词性和词义，以及多种用法。现简述如下： 一、用作动词： 1.like+名词/代词，意为"喜欢某人或某物"。例如： Tom likes fish very much.汤姆非常喜欢鱼。 Mr Wang is a good teacher.We all like him. 王老师是个好老师，我们都喜欢他。 2.like to do sth. 意为"(偶尔或具体地)喜欢做某事"。例如： I like to swim with you today.今天我喜欢和你一起去游泳。 3.like doing sth. 意为"(经常或习惯地)喜欢做某事"。例如： He likes singing.他喜欢唱歌。 4.like sb. to do sth.意为"喜欢某人做某事"。例如： She likes them to ask questions like this. 她喜欢他们像这样问问题。 5.would like to do sth. (=want to do sth.)意为" 想要做某事"。例如： I'd like to go shopping with you.我想要和你一起去买东西。 6.would like sb. to do sth.意为"想要某人做某事"。 I'd like you to meet my parents.我想要你见见我的父母亲。 二、用作介词： 1. be like, look like后接名词或代词作宾语，意为"像……；跟……一样"。例如：What is he like?他是怎么样的一个人？
The little girl looks like her father.那个小姑娘看起来像她的父亲。 2. feel like后接V?鄄ing形式、代词或名词，意为"想要做某事"。例如： Do you feel like having a rest?你想休息吗？ We'll go for a walk if you feel like it.如果你想散步，我们就去吧。 三、常见句型： 1. What do you like about...？意为"关于……你喜欢什么？"，用来询问对方所喜欢的内容。例如： -What do you like about China?你喜欢中国的什么？ -The food and the people.食物和人民。 2. How do you like...？意为"你认为……怎么样？"(＝What do you think of...？)例如： -How do you like the film?你认为这部电影怎么样？ -It's very interesting.很有趣。 3. Would you like +名词／ to do sth.?意为"你想要……吗？"，用来询问对方是否需要什么或征求意见与看法。例如： Would you like some water?你想要一些水吗？ Would you like to play football with us?你愿意和我们一起去踢足球吗？
有点多请细心看啊不定冠词有"a和an"两种形式."a"用在以辅音开头的词前,"an"用在以元音开头的词前.判断一个词是以元音开头还是以辅音开头,是根据读音而不是根据字母.一般情况下,开头字母是 a、e、f、h、j、l、m、n、o、r、s、x前用不定冠词an. 1. 用于可数名词的单数形式前,表示"一" There is a tiger in the zoo. 动物园里有一只老虎. 2. 表示一类人和东西 A tiger can be dangerous. 老虎可能有危害性. 3. 表示"某一个"的意思 A gentleman wants to see you. 有一位先生要见你. 4. 表示"同一"的意思 They are nearly of an age. 他们几乎同岁. The two shirts are much of a size. 这两件衬衫大小差不多. 5. 表示"每一"的意思 We go swimming four times a week. 我们每周去游泳四次. 6. 用在作表语的单数可数名词前,表示身份、职业 My mother is a teacher. 我妈妈是教师. 7. 第一次提到的人或事物,但不特别指明是哪一个 Long long ago there was an old king who had a very beautiful daughter. 很久很久以前,有一个年老的国王,他有一个非常美丽的女儿. 8. 在英国英语中,以"h"开头的多音节词,如第一个音节不重读,其前亦可用"an" There is a hotel near here. 这附近有一家旅馆. 9. 在such a,quite a句式中 He is quite a good actor. 他是一个相当好的演员. Don't be in such a hurry. 不要如此匆忙. 10. 在感叹句what...的句式中 What a pretty girl she is! 她是一个多么漂亮的女孩呀! 用在某些表示数量的词组中： a lot of 许多 a couple of 一对 a great many 很多 a dozen 一打（但也可以用one dozen） a great deal of 大量[编辑本段] 定冠词的用法 1. 用以特指某（些）人或某（些）事物 This is the house where Luxun once lived. 这是鲁迅曾经住过的房子. 2. 用于指谈话双方都明确所指的人或事物 Open the door, please.
#pragma的用法 在所有的预处理指令中，#Pragma 指令可能是最复杂的了，它的作用是设定编译器的状态或者是指示编译器完成一些特定的动作。#pragma指令对每个编译器给出了一个方法，在保持与C和C++语言完全兼容的情况下，给出主机或操作系统专有的特征。依据定义， 编译指示是机器或操作系统专有的，且对于每个编译器都是不同的。 其格式一般为: #pragma para。其中para为参数，下面来看一些常用的参数。 1)message 参数 message参数是我最喜欢的一个参数，它能够在编译信息输出窗口中输出相应的信息，这对于源代码信息的控制是非常重要的。其使用方法为： #pragma message("消息文本") 当编译器遇到这条指令时就在编译输出窗口中将消息文本打印出来。 当我们在程序中定义了许多宏来控制源代码版本的时候，我们自己有可能都会忘记有 没有正确的设置这些宏， 此时我们可以用这条指令在编译的时候就进行检查。假设我们希望判断自己有没有在源代码的什么地方定义了_X86这个宏, 可以用下面的方法: #ifdef _X86 #pragma message("_X86 macro activated!") #endif 我们定义了_X86这个宏以后，应用程序在编译时就会在编译输出窗口里显示"_86 macro activated!"。 我们就不会因为不记得自己定义的一些特定的宏而抓耳挠腮了。 (2)另一个使用得比较多的pragma参数是code_seg 格式如： #pragma code_seg( ["section-name" [, "section-class"] ] ) 它能够设置程序中函数代码存放的代码段，当我们开发驱动程序的时候就会使用到 它。 (3)#pragma once (比较常用) 只要在头文件的最开始加入这条指令就能够保证头文件被编译一次，这条指令实际上 在VC6中就已经有了， 但是考虑到兼容性并没有太多的使用它。 (4)#pragma hdrstop 表示预编译头文件到此为止，后面的头文件不进行预编译。BCB可以预编译头文件以 加快链接的速度， 但如果所有头文件都进行预编译又可能占太多磁盘空间，所以使用这个选项排除一些头文
l i k e的用法总结 -标准化文件发布号：（9556-EUATWK-MWUB-WUNN-INNUL-DDQTY-KII
Like的用法总结： like主要有两种意思，一个是动词，喜欢；还有是介词，像 like一词具有多种词性和词义，以及多种用法。现简述如下： 一、用作动词： 1.like+名词/代词，意为"喜欢某人或某物"。例如： Tom likes fish very much.汤姆非常喜欢鱼。 Mr Wang is a good teacher.We all like him. 王老师是个好老师，我们都喜欢他。 2.like to do sth. 意为"(偶尔或具体地)喜欢做某事"。例如： I like to swim with you today.今天我喜欢和你一起去游泳。 3.like doing sth. 意为"(经常或习惯地)喜欢做某事"。例如： He likes singing.他喜欢唱歌。 4.like sb. to do sth.意为"喜欢某人做某事"。例如： She likes them to ask questions like this. 她喜欢他们像这样问问题。 5.would like to do sth. (=want to do sth.)意为" 想要做某事"。例如： I'd like to go shopping with you.我想要和你一起去买东西。 6.would like sb. to do sth.意为"想要某人做某事"。 I'd like you to meet my parents.我想要你见见我的父母亲。 二、用作介词： 1. be like, look like后接名词或代词作宾语，意为"像……；跟……一样"。例如： What is he like他是怎么样的一个人 The little girl looks like her father.那个小姑娘看起来像她的父亲。 2. feel like后接V?鄄ing形式、代词或名词，意为"想要做某事"。例如： Do you feel like having a rest你想休息吗 We'll go for a walk if you feel like it.如果你想散步，我们就去吧。 三、常见句型： 1. What do you like about...意为"关于……你喜欢什么"，用来询问对方所喜欢的内容。例如： -What do you like about China你喜欢中国的什么 -The food and the people.食物和人民。 2. How do you like...意为"你认为……怎么样"(＝What do you think of...)例如： -How do you like the film你认为这部电影怎么样 -It's very interesting.很有趣。 3. Would you like +名词／ to do sth.意为"你想要……吗"，用来询问对方是否需要什么或征求意见与看法。例如： Would you like some water你想要一些水吗