Sonderforschungsbereich 314 Kunstliche ¨ Intelligenz - Wissensbasierte Systeme KI-Labor am Lehrstuhl fur ¨ Informatik IV Leitung: Prof. Dr. W. Wahlster
VITRA
Universitat ¨ des Saarlandes FB 14 Informatik IV Postfach 151150 D-66041 Saarbrucken ¨ Fed. Rep. of Germany Tel. 0681 / 302-2363
Bericht Nr. 107
Utilizing Spatial Relations for Natural Language Access to an Autonomous Mobile Robot
Eva Stopp, Klaus-Peter Gapp, Gerd Herzog, Thomas Laengle, Tim C. Lueth
Juli 1994
ISSN 0944-7814
107
Utilizing Spatial Relations for Natural Language Access to an Autonomous Mobile Robot? Eva Stopp1 , Klaus-Peter Gapp1 , Gerd Herzog1 , Thomas Laengle2 , Tim C. Lueth2 1
SFB 314 – Project VITRA, FB 14 Informatik ¨ des Saarlandes, D-66041 Saarbrucken ¨ Universitat email:
[email protected] 2 Institute for Real-Time Computer Systems and Robotics Prof. Dr.-Ing. U. Rembold and Prof. Dr.-Ing. R. Dillmann ¨ Karlsruhe, D-76128 Karlsruhe Universitat email:
[email protected]
Abstract Natural language, a primary communication medium for humans, facilitates better human-machine interaction and could be an efficient means to use intelligent robots in a more flexible manner. In this paper, we report on our joint efforts at providing natural language access to the autonomous mobile two-arm robot KAMRO. The robot is able to perform complex assembly tasks. To achieve autonomous behaviour, several camera systems are used for the perception of the environment during task execution. Since natural language utterances must be interpreted with respect to the robot’s current environment the processing must be based on a referential semantics that is perceptually anchored. Considering localization expressions, we demonstrate how, on the one hand, verbal descriptions, and on the other hand, knowledge about the physical environment, i.e., visual and geometric information, can be connected to each other.
? To appear
in the Proceedings of KI-94
1 Introduction The great flexibility of autonomous mobile robots offers interesting possibilities for more advanced applications in dynamic environments. If, however, the capabilities of intelligent robots are to be fully exploited, flexible human-machine interaction becomes an important issue. We argue that natural language is an appropriate means of making advanced robot systems more easily accessible and more responsive to their human partners. A practical advantage of a natural language interface is that information can be conveyed with changing accuracy and on different levels of abstraction. In [Badler et al. 91, p. 52] it is claimed that the kind of flexibility needed for the instruction of autonomous agents requires a “representation that embodies the same conceptualization of tasks and action as natural language itself.” Tactile, acoustic, and vision sensors provide robots with perceptual capabilities, enabling them to explore and analyze their environment in order to behave more intelligently. Communicating about spatiotemporal aspects of the environment, like the spatial arrangement of objects, plays an important role for human-robot interaction. Thus, a natural language interface must not only consider the relationship between expressions of a knowledge representation language and natural language expressions, but must also rely on the definition of a referential semantics that is perceptually anchored. Although both technical fields constitute major research areas within AI, there has been little emphasis on natural language interaction with intelligent robot systems (see, e.g., [Lobin 92; Nilsson 84; Torrance 94]). Other approaches have been concerned with natural language control of autonomous agents within simulated 2D or 3D environments (cf. [Badler et al. 91; Chapman 91; Vere & Bickmore 90]) or with natural language access to visual data (cf. [Bajcsy et al. 85; Herzog & Wazinski 94; Neumann 89; Wahlster et al. 83]). The aim of our joint effort is the integration of the Karlsruhe au¨ & Rembold 94] and the natural tonomous mobile robot KAMRO [Luth ¨ language component VITRA (VIsual TRAnslator) developed in Saarbrucken [Herzog & Wazinski 94]. Focused on here is the problem of spatial reference. The specific need for generating and understanding localization expressions will be shown. In addition, we will describe how such natural language utterances can be processed taking into account information provided by vision sensors and the environment model.
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2 Natural Language Access to the Robot System KAMRO The anthropomorphic robot KAMRO (Karlsruhe Autonomous Mobile Robot) is a two-arm robot system that is being developed to take on service tasks in an industrial environment. It consists of a mobile platform with an omnidirectional drive (3 DoF) on which there are mounted two PUMA-type manipulators (6 DoF) in a hanging configuration. Further mounted on this platform are several different sensor systems for CCD-cameras, ultrasonic range sensors, laser scanner and force-torque sensors. KAMRO is capable of autonomously performing service tasks as transportation, maintenance and assembly. The tasks or robot operations can be described on different levels: assembly precedence graphs, implicit elementary operations (pick, place) and explicit elementary operations (grasp, transfer, fine motion, join, exchange, etc.). Given a complex task, it is transformed by the control architecture from assembly precendence graph level to explicit elementary operation level (cf. Fig. 1). The generation of suitable sequences of elementary operations depends on position and orientation of the assembly parts on the worktable while execution is controlled by the real-time robot control system. Status and sensor data returned to the planning-system enable KAMRO to control the execution of the plan and if necessary correct it. Different strategies are used to avoid error situations that can occur by uncertainties. On the other hand, the robot is also able to recover from error situations. Assembly task
Plan execution system FATE Explicit Elementary Operations
Status and Sensor data Real-time robot control system
Action
Perception Environment
Figure 1: Control Architecture of the KAMRO System
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To use natural language just as a sophisticated command-language for the operator of a robot system would be an insufficient proposal. Flexible human-machine interaction only becomes possible if intelligent robots are made more responsive, i.e., a robot needs the capability to describe and explain its tasks and behaviour as well as the current situation. In a dialog-based approach, which is our choice, further queries can always be utilized to resolve ambiguities or misunderstandings. Four main situations of human-machine interaction can be distinguished in the context of natural language access to the KAMRO system ¨ et al. 94]): (cf. [Luth
Task specification: Operations and tasks to be performed by the robot can be specified on various levels of abstraction. Natural language instructions can refer to elementary operations, like pick and place, and even to explicit elementary operations, if fine control is required. On a higher level, the operator could specify a suitable assembly sequence, in order to perform a designated task. The most abstract specification would only provide a qualitative description of the desired state, i.e., the final positions of the different assembly parts. Execution monitoring: During the execution of a task the robot could generate a simultaneous report concerning its actions. Depending on the demands formulated by the user, descriptions and explanations can be given in more or less detail. Explanation of error recovery: The capability of recovering from error situations leads to dynamic adjustment of assembly plans during execution. This feature makes it more difficult for the operator to predict and understand the behaviour of the robot. Natural language explanations and descriptions of why and how a specific plan was changed would increase cooperativeness of the intelligent robot. Similar descriptions are even more important in error situations that can not be handled by the robot autonomously. Updating the environment representation: Because of the dynamic nature of the environment, geometric and visual information can never be complete, even for an autonomous robot equipped with several sensors. In a natural-language dialog, user and robot aid each other by exchanging additional information about the environment and world model. 4
In any case it is crucial to be able to refer to spatial aspects of the complex environment. Spatial configurations need to be described and specific locations must be refered to. In addition, spatial prepositions can be utilized to refer to assembly parts and other objects; in general one can not rely on unique object identifiers. This leads to the question of how such natural language expressions (e.g. “Take the spacer on the left and put it into the nearest hole of the sideplate.”) can be related to visual and geometric information about the environment.
3 Evaluation of Spatial Relations The prepositions in their spatial meanings (cf. [Retz-Schmidt 88]) combined with descriptions of placement, an object to be localized (LO) and a reference object (REFO), build the class of localization expressions [Herskovits 86]. The semantic analysis of spatial prepositions leads to the term spatial relation as a target-language independent meaning concept. Conversely, the definition and representation of the semantics of spatial relations is an essential condition for the synthesis of spatial reference expressions in natural language. Spatial relations can be defined by specifying conditions for object configurations, such as the distance between objects or the relative position of objects. In this sense, a spatial relation characterizes a class of object configurations (cf. [Andr´e et al. 89]).
3.1
Computing the Elementary Spatial Relations
The perceptual information available to the robot system is encoded in a 3-dimensional geometric model (cf. Fig. 2), which can be accessed by the natural language component. The basic meanings of spatial relations are defined with respect to this geometrical representation.
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Figure 2: A snapshot from KAMRO’s environment Following [Landau & Jackendoff 93], people do not account for every detail of the objects involved when they apply spatial relations. Hence, an approximative algorithm can be utilized for the computation of spatial relations, considering only essential shape properties of an object. For instance, in most cases the center of gravity is sufficient to approximate the object to be localized because the object position is the only information that counts for the applicability of the specific spatial relation. The following idealizations are currently provided in our implementation:
Center of gravity Bounding rectangle (BR) The bounding rectangle of an object with respect to a direction vector ~v , is the minimum rectangle aligned to ~v and containing the object’s 2D representation. 2D representation Bounding right parallelepiped (BRP) 3D representation
Three distinct classes of static spatial relations are considered: The topological relations (e.g., at and near), the projective relations (e.g., in front of , behind, right, left, above, below, and beside), and the relation between, which takes an exceptional position in the group of spatial relations. 6
The algorithm for the computation of applicability of the topological relation’s, at and near, basic meaning considers only the distance between the LO and the REFO scaled by the REFO’s extension in each of the three dimensions, i.e., the local distance. The computation of projective relations also has to include the scaled (local) deviation angle of the LO from the canonical direction implied by the relation. The dependency of the local distance and the local angle from the REFO extension ensures for a bigger REFO in a dynamic enlargement of a relation’s region of applicability (cf. [Gapp 94]). The evaluation of the applicability of a spatial relation differs from one person to another (cf. [Kochen 74]). Because of this we map the local distance and the local angle to user adjustable cubic spline functions SplineRel , which enable a cognitively plausible continuous gradation of the relation’s applicability. The product of both spline values determines the degree of applicability DARel of an elementary projective spatial relation. The evaluation process can be applied to 2-dimensional, e.g., maps, as well as to 3-dimensional environments. In the latter case, we get a 3dimensional extented region of applicability for spatial relations. This seems reasonable when we look at Fig. 3, because “the LO in front of the REFO” is an adequate application of the spatial relation in front of although there is a vertical difference between the LO and the REFO.
Figure 3: LO in front of the REFO To get an idea of a region’s applicability structure we extend the so-called potential fields presentation of a spatial relation (cf. [Schirra & Stopp 93]) into the third dimension. For instance, Fig. 4 is a visual example of the 3D applicability structure of above (a) and right (b) using a building as REFO.
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Spatial prepositions at,near in front of ,behind right,left above,below
at,near in front of ,behind right,left above,below X X
X X
X X
Table 1: Ordered 2-place compositions of some German spatial prepositions
Figure 4: Cross-sections of the 3D applicability structure of above and right
3.2
Compositional Use of Spatial Prepositions
If it is necessary to give a more specific localization of objects it is usual to use more than one spatial preposition. Normally not more than two prepositions are combined, e.g., the LO is in front of and to the right of the REFO. Therefore we also need to consider the composite use of spatial relations and additionally which relations are combinable. The relations we take into account are the topological at and near and the projective in front of , behind, right, left, above, and below. Table 1 gives an overview of the possible ordered two-place compositions of the spatial prepositions (in German use). According to the table, the prepositions in front of , behind, right, and left are reflexive in their compositional use, however at, near, above, and below are not. The latter always occupies the second position in the composition.
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3.3
Computation of Compositional Spatial Relations
For the computation of compositional spatial relations we are able to neglect the problem of which relation occupies the first position. We define two classes of 2-place composite spatial relations, the 2-place 2p composite projective projective (Relcpp ), a composition of two elementary projective relations, and the 2-place composite projective topological 2p (Relcpt ), which is a combination of a projective and a topological. 2p 2p The applicability of Relcpp as well as Relcpt requires positive evidence from both elementary spatial relations involved. Taking into account the general structure of the region of applicability (cf. Fig. 5 and Fig. 6) an important difference between the two classes of compositions 2p becomes obvious. In the case of the Relcpp the combination of both degrees of applicability yields a higher degree of applicability for the composition, i.e., the two elementary relations intensify each other. 2p The Relcpt grounds on two elementary relations which are independent from each other. The composition can be seen as a conjunction of two elementary conditions, i.e., the weakness of one relation can not be compensated by the other. 2p For the computation of the Relcpp we use the following evidence combination calculus (cf. [Wahlster 81]), which guarantees a degree of applicability not higher than 1:
EV1^2 := EV1 + EV2 ? EV1 EV2 ; EV1 ; EV2 > 0 The degree of applicability DARel2cpp p can than be defined as:
(
DARel1 + DARel2 ? jDARel1 DARel2 j :DARel1 ; DARel2 > 0 0 :else with Rel1 2 fin front of ; right ; above g; Rel2 2 fbehind ; left ; below g or Rel1 2 fbehind ; left ; below g; Rel2 2 fin front of ; right ; above g DARel
cpp
:=
2p For the Relcpt , the degree of applicability, DARel2p , is determined by cpt the minimal degree of the single relations:
DARel
cpt
:= MinfDARel1 ; DARel2 g
with Rel1 2 fin front of ; behind ; right ; left g; Rel2 2 fat ; near g
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Figure 5: Potential fields of: to the left, in front of, and their composition
Figure 6: Potential fields of: to the left, near, and the composition
3.4
Interpreting Relation Tuples
In many cases, the interpretation of a proposition containing a spatial relation can be thought of as verifying the given relation tuple with respect to the geometric model. Depending on the arguments, possible instantiations of the relation tuple are computed and the most plausible interpretation will be selected. Sometimes, however, this kind of query is not possible, e.g., if the operator refered to a part of the environment the robot can not currently perceive. Thus, a referential semantics for spatial relations remains uncomplete, if it only defines how a relation 10
tuple can be computed from the underlying sensory data. Similar issues arise in the field of text understanding and have extensivly been dealt with, e.g., within the LILOG project (cf. [Herzog & Rollinger 91]) In [Schirra & Stopp 93], we show how a plausible imagination, i.e., a coherent geometric model, can be generated from a set of propositions. An adequate interpretation is constructed by searching for a maximally typical representation of the situation described by the given propositions. The typicality distribution corresponding to a certain proposition is encoded in a so-called Typicality Potential Field (TyPoF), which is a function mapping locations to typicality values. TyPoFs are instances of typicality schemas associated with the spatial relations. Each TyPoF takes into account the dimensionality, size, and shape of the objects involved. A typicality value associated with a spatial expression corresponds to the (degree of) applicability of a spatial relation for a given object configuration. If several propositions impose restrictions on an object, the corresponding TyPoFs are combined through superimposition. Hillclimbing is employed in order to find an interpretation with maximal typicality.
4 Towards a Natural Language Dialog A simple natural language front-end does not provide enough flexibility for communicating with an autonomous robot, which is situated in a complex dynamic environment. Thus, the natural language interface should have access to all processed information and the environment ¨ et al. 94]). representation inside the intelligent agent (cf. [Luth The architecture for such an integrated system is depicted in Fig. 7. Natural language commands and queries from the user form the input for the natural language access system. The linguistic analysis is carried out by a syntactic-semantic parser and translates the natural language expressions into propositions. The parser we use is a modified version of SB-PATR [Harbusch 86] which is based on a unification grammar with semantic information. The propositions are further interpreted in the evaluation component, which is also responsible for the reference semantic interpretation. The evaluation component realizes the interface to the robot and the shared knowledge sources. It is also responsible for the appropriate reactions and responses of the system. The generation component is responsible for translating selected propositions into natural language descriptions, explanations, and queries. An incremental generator, which is based on Tree Adjoining Grammars (cf. [Harbusch et al. 91]), generates the surface structures.
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Commands Queries Analysis
Evaluation
Morpho-Syntactic Knowledge Conceptual Knowledge
Encapsulated
User Model
of the Robot
Knowledge
Autonomous Mobile Robot
Linguistic Dialog Memory
...
Generation Descriptions Explanations Queries
Task Representation
KAMRO
Environment Representation Execution Representation
Figure 7: Architecture of KAMRO’s natural language interface
4.1
Interpreting Localization Expressions
In general, a proposition corresponding to the linguistic analysis of a localization expression can not provide a fully instantiated relation tupel. Consider a phrase like “the spacer on the left”, which is employed to denote a specific object. The spatial relation (left) and a type restriction for the LO (spacer) are specified. Neither the REFO, nor the frame of reference are given. A reference system is required since projective relations, as well as the corresponding prepositions, depend on an orientation. The following uses can be distinguished (cf. [Andr´e et al. 87; Retz-Schmidt 88]): 1. Intrinsic use Orientation is given by an inherent organization of the REFO, e.g., by perceptual organs (humans, animals), by the characteristic direction of movement (vehicles, etc.), or by functional properties (position of the robot’s manipulators, etc.). Depending on whether the REFO is thought of as being seen from the outside (e.g., buildings, desks, mirrors, etc.), or from the inside (e.g., chairs, clothing, etc.) the back-front-vector and the left-right-vector form either an orthogonal right-handed-system (mirror principle), or left-handed-system (coincidence principle). 2. Extrinsic use Orientation is determined by the position of a possible imaginary observer, i.e., it is given by contextual factors, such as the accessibility of the reference objects or objects in its vicinity (e.g., “Looking though the window, the lever is behind the spacerreservoir”). If the REFO and the observer coincide, the orientation of the observer is transferred to the REFO. If the observer and the 12
REFO are spatially separated, the orientation follows from the mirror principle. 3. Deictic use A specific case of extrinsic use where the observer coincides with the speaker or listener. In our example, the deictic use seems to be plausible and the robot would play both the role of the observer and of the REFO. Depending on the context a different interpretation of the utterance could be more appropriate. During the referential analysis a type of visual search is being carried out in order to generate and test candidates for the uninstantiated arguments of the relation tuple. The most plausible instantiation, with respect to the degree of applicability and focus values in the dialog memory, will be considered as the correct interpretation. Note that even the REFO could be uninstantiated, e.g., if there are several assembly parts of the same type (“the spacer beside the lever”). Even in this example, the interpretation may be specific if there is exactly one spatial configuration involving two objects of the appropriate types, i.e., LO and REFO are both identified by the spatial relation. If the LO or the REFO are not inside the visual field of the robot (e.g., “the lever behind you”) a plausible imagination has to be constructed and added to the environment representation. Revisions or adjustments might be necessary later on if the interpretation is falsified by sensory data. The mechanism of generating an imagination is also required if an utterance refers to a hypothetical or future situation (e.g., “put the lever behind you”).
4.2
Synthesis of Spatial Reference Expressions
For the synthesis and for the analysis of an localization expression similar aspects must be considered:
Selection of the reference objects The following criteria guide the selection: 1. Distance between LO and REFO, i.e., prefer objects which are closer to LO. 2. Salience of the REFO, depends on factors like shape, size, color, etc. 3. Linguistic context, i.e., prefer objects which have been previously mentioned and which are in focus. 13
Selection of spatial relations Not only the degree of applicability of a relation tuple, but also the size of the corresponding region of applicability must be taken into account, e.g., a composite projective relation could enable a more specific spatial reference than a binary topological relation like “in” or “on”. Selection of the frame of reference In the case of projective relations, the system must choose an orientation. This selection depends on the properties of the REFO and on the situational context.
A detailed description of the generation of localization expressions within our system is given in [Andr´e 88].
5 Summary Advances in both technical fields during the last decade form a promising basis for the design and construction of integrated natural language interfaces for autonomous mobile robots. Such a natural language access could provide better human-machine interaction for the flexible use of intelligent robots. The required communicative capabilities can only be achieved if the natural language interface has access to the robot’s perceptual information and if that knowledge about the environment is accounted for in the interpretation and generation of utterances. Thus, a referential semantics that is perceptually anchored must be defined. In this contribution we have focused on the problem of spatial reference. We propose to utilize spatial relations as an intermediate representation between the linguistic level and the sensory level. These conceptual structures bridge the gap between geometric data and natural language concepts, such as spatial prepositions. Our computational model is based on a continuous gradation of the applicability of a spatial relation and supports both, the synthesis and the analysis of propositions containing spatial relations. The current implementation copes with elementary topological and projective relations as well as compositional spatial relations. Important aspects of the analysis and generation of localization expressions, such as determination of the frame of reference and the selection of reference objects, have been considered. The ideas presented here, however, still need to be integrated into a fully operable prototype of a natural language interface for the autonomous mobile robot KAMRO.
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