Storytelling in Virtual Reality for Training Nicolas Mollet1 and Bruno Arnaldi2 1 2

IRISA/INRIA Rennes, France. [email protected] IRISA/INRIA Rennes, France. [email protected]

Abstract. We present in this paper researches focussed on training scenario specification. These researches are conducted in the context of a collaboration with Giat-Industries (a French military manufacturer) in order to introduce Virtual Reality(VR) in maintenance training. The use of VR environments for training is strongly stimulated by important needs of training on sensitive equipment, sometimes fragile, unavaiblable, costly or dangerous. Our project, named GVT3 , is developped in a research/Industry collaboration. A first version of GVT is already available as a final product, which allows maintenance virtual training on GIATIndustries equipments. Internal models have been designed in order to achieve reusability and standardization for the efficient development of new virtual training environments. In particular, we defined a scenario language named LORA, which is both textual and graphical. This language lets non-computer scientists author various and complex tasks in a virtual scene.

1

Introduction

Giat-Industries is an important french industrialist, specialized in military equipment such as the Leclerc Tank. All about those equipments is complex and enormous: size, cost, maintenance operations, dangerous manipulations, etc. Virtual Reality (VR) appears to be a good solution for training on such equipments, especially for costs, risks (for human and materials) and new pedagogical actions which are not possible in the reality. Creating VR environments for training requires the development of complex softwares, mixing multiple disciplines and actors: graphical and behavioural computer engineering, physics simulation, pedagogical approaches, specialized know-how in the field of the training, etc. Furthermore, such complex development is generally done in new projects, where the reusability of existing developments is a major issue. In this context, Giat-Industries has started the Giat Virtual Training (GVT) project, in collaboration with INRIA-Rennes and CERV research laboratories. The aim of this project is to propose a full author-platform for building VR training applications. For this purpose, we propose generic and reusable approaches. The first part of this paper is a short state of the art on storytelling in virtual environments. The second part presents the general organization of GVT, what kind of models we have developed and how they interact. The third part is 3

GVT is a trademark of GIAT-Industries

focused on our model of scenarios: how describe complex procedures for noncomputer scientist authors ? Then, we finish this paper by a conclusion.

2

State of the art - Storytelling in Virtual Reality

First approaches for arranging actions in a virtual environment were based on a low-level approach. As models are able to describe complex behaviors of objects, those same models should be able to describe complex sequences of actions using such objects. We can find this approach with Badler and his Pat-Nets model[1], and also with Cremer and Kearney with the use of the HCSM model[2] to describe traffic scenarios. Those technics have two main drawbacks: a lowlevel of abstraction, and the use of computer-language. Without a high-level of abstraction, scenarios are linked with low-level developments. And by using computer-language, non-computer scientists can not write scenarios. Therefore, another level of abstraction is needed, that’s the role of scenariolanguages. We can first cite the works of Ponder[3], who used interactive stories to realized a tool for training on decision-taking. Interactive stories are named so because the actions done in the environment influence the story. This method is very interesting to describe a complex variable story with multiple characters, and to train on decision-taking. But interactive stories are less interesting when the goal is to describe a fixed story, like a maintenance procedure. For this kind of problems, two main approaches can be found: a complete description of the sequence of actions, or an approach based on constraints and goals. In the description of complete sequences, we can find the works of Goldberg on Improv[4], Vosinakis[5] with ideas on the reuse of parts of scenarios, and Ishida with the Q language[6][7], a language adapted to non-computer scientists. From those different scenario languages, we can extract interesting properties: hierarchical concept, parallel actions, parametrization, etc. We can also cite the works of Leitao[8], who proposed the use of the graphical language Grafcet[9] to describe traffic scenarios. This visual aspect is, from our point of view, very interesting. Graphics are more international than textual languages: diagrams are easy to understand for everyone. Scenarios can also be managed with constraints and goals. With Ridsdale[10], actors of a virtual theatre are managed by an expert system, which use goals, rules and constraints on the actors to construct the sequence of actions. Querrec[11][12] proposed the MASCARET model to describe collaborative procedures for firemen. Each fireman has a role, and actions are arranged with temporal constraints and preconditions. We can find such approaches with Devillers[13] and the SLuHrG languages which used temporal constraints and reservations process on actors, and the Steve project[14][15] where procedures are described with causal links and ordering constraints. We have also the works of Badler[16] combined goals with natural language description, and his PAR model has been used to validate maintenance procedures with humanoids.

To conclude this short state of the art, we can underline those few points. First, describing scenarios with constraints and goals is more flexible than a complete description of sequences. It allows for example the readaptation of the scenario when a non-predicted event happens. Nevertheless, this flexibility could be a problem when the aim is to learn an exact procedure. Second, describing scenarios is generally a hard task for non-computer scientists. Efforts have sometimes been done, but it’s still hard to express complex scenarios with a simple and efficient representation.

3

GVT organization, a generic platform for procedures training

3.1

The role of GVT

The main current application of GVT is virtual training on Giat’s maintenance procedures. That’s what can be found in the latest release of the product. But GVT allows virtual training on more general procedures: maintenance procedure, starting procedure, showing procedure, diagnosis procedure, etc. Nevertheless, GVT has not been created to teach technical gestures. GVT has actually been designed to be used alone on a single computer for one trainee, and more generally to be used on a network(fig.1) with one trainer and several trainees at different places, with different equipments, levels and procedures to learn. As GVT offers pedagogical agents able to manage students (we’ll talk about those agents in the third subsection), we consider that a human supervision is essential to realize a good training. From this point of view, GVT can be seen as a tool for expert trainers in teaching process. 3.2

VR platform and genericity

GVT uses OpenMASK4 for VR aspects. As we are using this platform, GVT is totally independant from devices which are standard modules with input/output data, managed by OpenMASK. Furthermore, the paradigm of interaction in GVT is based on the selection of objects and dynamic menus(fig.2), which is a convenient choice to standardize the interaction. But if it is needed, we can also include dedicated devices for special interactions. Our models are not limited to our general paradigm of interaction. So, the same code can run on a laptop computer or in a full immersive room, that’s exactly the same software. In an immersive room(fig.1), we can manage stereovision, speakers with voice synthesis, a microphone with voice recognition, a tracker wich handle a pointer in the environment, and a dataglove to make the selection. On a laptop computer, a trainee can simply use the keyboard and the mouse to interact. For example, if speakers are not available, the voice synthesis is simply replaced by text on the screen. 4

http://www.openmask.org

Fig. 1. GVT on a network: Virtual Training with one trainer and distant trainees on different hardware configurations

Fig. 2. The dynamic menu of interactions, generated with interactions capacities of selected objects

We have tested GVT on three hardware configurations. The laptop, the immersive room, and what we can call a standard GVT configuration(fig.1): a computer with two screens and a microphone. One screen for the 3D scene, and one for pedagogic documents and the trainee GUI. Those three configurations reflect 3 applications: training near the real equipment, training in a classroom, and training in an immersive environment when the trainer considers it important to have a better immersive sensation. 3.3

Elements of the GVT kernel

The first need of GVT was to define an interactive environments, composed of complex and various objects. We proposed a model of object and interaction on objects, named STORM[17] for Simulation and Training Object-Relation Model. The second need was the description of complexe procedure. That’s the role of the language LORA[17]. As GVT is a training tool, we also had to define a pedagogical model. Those three models are using in the kernel of GVT. The global vision is illustrated on fig.3. We have four elements in this kernel: - a reactive and behavioral environment. For this purpose, we proposed a model of behavioral object in the definition of STORM . By using this model, we create a reactive and informed 3D world, composed of various behavioral objects which can interact with each other. The world can be composed of really complex objects: for example we can simulate the evolution of pressure between two complex hydraulic objects. Behaviors of an object is define by using the

HPTS++[18][19] model. the STORM model is an important work we won’t present here, that’s not in the scope of this article. More informations on the STORM model can be found in [17]. The 3D world with behavioral objects has been developed in OpenMASK, which manages VR aspects: 3D rendering, the distribution of this environment on a network, etc. - an interaction engine. The interaction engine is the STORM engine. The role of this element is to manage interactions between STORM objects, by using the interaction capacities of those objects. Those interaction capacities are defined in the model, and represent what an object offers as interactions. The realization of the interactions is also described in the STORM model. More informations on this model can be found in [17]. - a pedagogy engine, the teacher assistant. This engine has to react to trainee’s actions. Trainees are different and the engine has to adapt his reaction to each trainee’s particularities, such as a level or a pedagogic strategy. For example, the pedagogy engine can block trainee’s action when the action is dangerous for him or for the equipment, or simply because it’s not the right thing to do at this moment and that the trainer chooses to forbid what is not in the procedure. The pedagogy engine can also highlight objects, it can show pedagogical documents (videos, pdf, etc.). It can, for example, turn an interactive button of the environment if necessary or ask the automatic realization of what the trainee should do. The pedagogy engine also has to detect that a real trainer should be contacted to supervise personally the trainee, for example when those automatic helps are too often requested. Pedagogical aspects of GVT are really important, they are realized with the AReVi5 platform in the CERV laboratory, which can be contacted for more informations. A complete description of the pedagogy engine is not in the scope of this paper. - a scenario engine. The role of this engine is, in particular, to describe what has to be done at each moment of the training session. As actions are done in the environment, the engine evolves and gives the next steps to do. This engine can also be called to realize actions, it can do the procedure. We present this engine in the next section. Those elements are the base of our generic approach, our platform of development. All the objects and behaviors design with the STORM model are totally reusable. The STORM engine doesn’t need any special configuration, it just uses the informed environment. The pedagogy engine can use the scenario engine and the trainee’s characteristics to create automaticaly his strategy of responses and actions. And, for the scenario engine, parts of scenarios can be reused in other training sessions when they represent a common sequence of actions.

5

http://www.enib.fr/ harrouet

DB Scenarios DB STORM

Interactive Environment composed of STORM objects

Scenarios Engine

Interactions Engine

pédagogy Engine DB pédagogy

Fig. 3. Global vision of GVT

4 4.1

Scenarios in GVT Language LORA: choices

In our Industrial context, procedures and especially procedures of maintenance are very strict and complex. Things have to be done in a certain order, and trainees have to learn this. Generally, this strict order has a sense, a causal link between the differents steps. As we mentionned in our state of the art, we could use a system based on goals, causality, preconditions, etc. But, we have to consider that those kind of procedures are really big, with a very important number of steps and a long time of realization. It’s usual to have scenarios with several hours of works for one maintenance operation ! In the same ideas, the description of all the real maintenances procedures are taking several ring binders, in several cupboards ! In a pedagogical tool, it’s not reasonable to consider a whole scenario which is too long, and we have to cut it in multiple scenarios. And sometimes, trainers are just interested in parts of scenarios. For that matter, it’s impossible to use only a ”goals and constraints approach”: causality may never be described, and the exact procedure is not necessarily the only solution for the local set of constraints and goals. Our approach is based on the complete description of the procedure. By using this technic, we could also add causal informations on steps if necessary. We also have to think about who will write and read scenarios. In our context, it might be for example experts of training sessions on the real equipment. Those experts know the procedures, and know what they want to teach. It could also be a specific person, whose role would be to translate the actual procedures which are expressed in a natural language. But he would never be a computer

scientist. That’s why our approach is based on graphical languages. Based on the STORM model, which describes as we explained in the previous section behavioral objects and interactions between them, we proposed a scenario language named LORA Language for Object-Relation Application. This language is a graphical one, but it can also be read in a textual version. A scenario described with LORA contains all the steps which have to be done, but more generally all the steps which could be done. 4.2

LORA: the model

We have tested different graphical languages, and the one which offered the best response to our needs was the Grafcet. This language was the begining of our work. The Grafcet defines what has to be done, in a strict sequence. But in our case, we are not describing robotic actions, we have to describe strict procedures which contain a human factor: choice. That’s why the philosophy of our language is to describe what could be done at each point of the scenario, where the Grafcet approach is to describe what must be done. Another part of our work was to simplify general patterns we were having when we were using the Grafcet. With the choice aspect and the simplification process, we have created the basis of our model. LORA is a hierarchical parallel transition system language. It is composed of a set of hierarchical step machine, each step machine is composed of a set of variables, steps and links between steps. A step machine has a current state: a set of actives steps, which represent what could be done as long as they are active. Steps Steps have 5 possible types. Begin, end, standard, callback or forbidden. A callback step is a special step which role is to supervise a particular action, and to generate a certain sequences of actions in the environment when the particular action is realized. A forbidden step is the description of identified forbidden step. It’s not an assumption on what the trainee will do. It’s not a step impossible to do in the scenario engine. It’s the possibility to describe, in the scenario, forbidden ways: for example dangerous actions which must not be done at a special point of the procedure. Steps have 5 possibles roles. Each step can be a calculus step, which allows operations on step machine’s variables. It can be a conditional step, which transition depend on it’s evaluation, using the machine step’s variables. The third possible role is a consultation step. This role allows a step to consult states of objects in the virtual environments. The two last roles are a hierarchical step, and an action step. The first one contains another machine step, and the second describes an atomic action. Signals. The begin step generates a signal of activation. Each step has an input and an output for this signal. The conditional step has two outputs (fig.4).

When a step receives a signal activation, the step become active. When the step is finishing (for example, an action step will terminate when the action it represented is done in the environment), the signal can be transfered by the output to what the step is connected to. A step can be a terminaison of a branch, and can be connected to nothing. Such steps are called optional steps: their realization has no direct consequence on the rest of the scenario.

Condition on machine−

False

step variables True

Fig. 4. A conditional step, and a link with 4 connectors (with one NOT connector) and 2 outputs.

Links and RPS The role of a link is to transfer the signal activation, and thus to arrange the sequence of actions. A link has one or more connectors. When all connectors are activated by an input signal activation, the link transfers the signal activation to it’s outputs. The link can have NOT connector (fig.4). If such connectors are activated, the link will not be able to transit after. Links can be linked with other links. One advantage of those links is the realization of graphical conditions(fig.5). At the output of a link, the signal activation can be divided in multiple ways. That’s what we call the RPS : all ways are Potentialy Realizable Simultaneously. That’s the case in the example of the graphical condition(fig.5) : steps A, B, C and D can be realized as they are all activated at the same moment by the first link (at the top of the diagram). It is possible to precize special RPS. It can be a parallel realisation between branches (actions must be done in parallel) or exclusivity of realization between branches (as a branch is begun, other becomes impossible to do). On the fig.5, the condition realized is (((A OR B) AND C) OR D). It’s a real case of use, with buttons controling floodgates. Here, the procedure was you now have to empty the cistern, turn A or B floodgate, with C floodgate, or only the D floodgate which will empty all cisterns. There’s multiple ways to realize this operation, and RPS technics allow us a certain flexibility in writing this condition and it’s realization. Textual representation The textual representation of LORA is an XML document. Such documents can be read directly, and for those types of data it’s easy to manipulate it as a standard computer language. By using XML, we can use many tools: generated GUI to create scenarios, or traduction of the language in other language with XSLT rules, etc. The complete graphical and textual norm of LORA is detailed in ??.

A

B

C

D

Fig. 5. Graphical realization of (((A OR B) AND C) OR D).

4.3

RunTime: the scenario engine

The scenario engine is a multi-agents system. Each machine step, each step and link is an agent managed by the scenario engine. The interest of such a system is it’s dynamic and automatic organization. Each agent have a birth, an activity, and a death. As a new agent appears with a signal activation, it will find its place within the other agents and communicate with some of them. The activity of an agent depends on his role. For example, an agent which represents an action will have to keep on eye on the environment, to check if the action it represents is begin realized or not. As an agent dies, the signal activation can be transfered if it has to be. Agents are the dynamic point of view of the scenario engine. They represent the current state, what could be done. The scenario engine has a more static part. That’s the representation of the perfect procedure: this is the nominal scenario. This representation is used by agents when they are created, as a model. From this point of view, we can consider that the scenario engine is a virtual machine for the LORA language, which interprets procedures by loading and activating agents in memory. By the way, the representation of the procedure can be modified during the Run-Time execution. At each moment, the scenario engine can give a list of actions the trainee has to do. To preserve the complex realization of some scenarios, the scenario engine tests if all of it’s agents represent possible actions, in function of the real state of the world. It’s possible that an action becomes impossible: simply with a problem of ressource. For example, if an object has to do an interaction but is currently in use. As the procedure engine tests if actions are possible, scenarioauthors don’t have to take care about ressources, it’s automaticaly managed by the engine. It is also possible for the scenario engine to realize actions in the environment. It’s possible, with LORA, to specify some automatic parts of the scenario. When the pedagogy needs the automatic realization of steps, it can make this request to the scenario engine which can realize the whole procedure if needed.

Fig. 6. Like in the real world (first picture), the trainee have to unscrew caps on a tank suspension (second picture) Unscrew Take and Put(UTP) string a; string target;

unscrew $a$

Macro2 a="screw2" target="table"

take $a$ UTP put $a$ on $target$

Fig. 7. Hierarchical Step and parametrization of a machine-step: unscrew, take and put an object

4.4

Two short examples

In this point, we show two short examples of the use of LORA. The first(fig.7) one is an example of a real reusable procedure, which is defined like a parameterizable function. This procedure represents the sequence : unscrewing an object, taking the object, and putting the object somewhere. “Object” and “somewhere” are the two parameters “a” and “cible”. The figure 6 represent an application of this procedure, where the trainee have to unscrew caps on a tank suspension. The second example(fig.8) is a part of a real maintenance procedure. In this example, 3 ways are possible to access to the next part. There’s no preference between those ways, and each of them can be began without any consequence. Except when one is realized: the other branches will be desactivated. An example of correct realization would be A30 (the realization in the environment of what the step A30 represent), A20, A21, A11, A22. As the branch A2i is finished, all the active steps in A1i and A3i will be desactivated. To compare our language to Grafcet, we show the minimal solution of this problem with the Grafcet(fig.9).

Action 10

Action 20

Action 30

Action 11

Action 21

Action 31

Action 12

Action 22

Fig. 8. The solution with LORA. C0 A10

C0 Action 10

f10

A11

A12

Action 12 f12

A21

f22+f31

f12+f31 Action 21

f21

f22+f31

C0 Action 20

f20

f22+f31

Action 11 f11

A20

A22

A30

Action 30 f30

A31

f12+f22 Action 31

f12+f31

f12+f22

Action 22 f22

f12+f31

f31

Fig. 9. The simplest solution with Grafcet.

5

Conclusion

GVT is a very challenging project, involving two laboratories and an industrial company. This project has leaded to the depot of 6 patents[20][21][22][23][24][25] and has been presented at 4 meetings : Eurosatory, Perf-RV6 , Laval-Virtual[26] and Intuition7 workshop. We proposed in this project different models, especially STORM and LORA. We have presented in this paper the language LORA. Developed with this language there are now about 20 true industrial training scenarios on 5 differents equipments. Our models give operational responses to GVT needs, and are turned towards the future. We are still working on advanced authoring tools, which use the genericity and the reusability capacity of the GVT platform. Special aknowledgements to Charles Saint-Romas(Giat-Industries), Bernard Barroux (Giat-Industries), Jacques Tisseau (CERV), Fr´ed´eric Devillers (CERV), Eric Cazeaux (CERV), Xavier Larrod´e (INRIA), St´ephanie Gerbaud (INRIA) and Julien Perret(INRIA).

References 1. Norman I. Badler, Barry D. Reich, and Bonnie L. Webber. Towards personalities for animated agents with reactive and planning behaviors. In Creating Personalities for Synthetic Actors, Towards Autonomous Personality Agents, pages 43–57. Springer-Verlag, 1997. 2. J. Cremer and J. Kearney. Scenario authoring for virtual environments. In IMAGE VII Conference, pages 141–149, 1994. 3. Michal Ponder, Bruno Herbelin, Tom Molet, Sebastien Schertenlieb, Branislav Ulicny, George Papagiannakis, Nadia Magnenat-Thalmann, and Daniel Thalmann. Immersive vr decision training: telling interactive stories featuring advanced virtual human simulation technologies. In EGVE ’03: Proceedings of the workshop on Virtual environments 2003, pages 97–106, New York, NY, USA, 2003. ACM Press. 4. Ken Perlin and Athomas Goldberg. Improv: a system for scripting interactive actors in virtual worlds. In SIGGRAPH ’96: Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 205–216, New York, NY, USA, 1996. ACM Press. 5. Spyros Vosinakis and Themis Panayiotopoulos. A task definition language for virtual agents. In WSCG, pages 512–519, 2003. 6. Toru Ishida. Q: A scenario description language for interactive agents. Computer, 35(11):42–47, 2002. 7. Yohei Murakami, Toru Ishida, Tomoyuki Kawasoe, and Reiko Hishiyama. Scenario description for multi-agent simulation. In AAMAS ’03: Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pages 369–376, New York, NY, USA, 2003. ACM Press. 8. M. Leitao, A. A. Sousa, and F. N. Ferreira. A scripting language for multi-level control of autonomous agents in a driving simulator. In DSC’99, pages 339–351, July 1999. 6 7

http://www.perfrv.org http://www.intuition-eunetwork.net

9. Rene David and Hassane Alla. Du Grafcet aux reseaux de Petri. Hermes, 1989. 10. G. Ridsdale and T. Calvert. Animating microworlds from scripts and relational constraints. Computer Animation ’90, pages 107–118, 1990. 11. R. Querrec. Les systemes multi-agents pour les Environnements Virtuels de Formation. Application a la securite civile. PhD thesis, October 2002. 12. R. Querrec and P. Chevaillier. Virtual storytelling for training : An application to fire-fighting in industrial environment. International Conference on Virtual Storytelling ICVS 2001, (Vol 2197):201–204, September 2001. 13. Frederic Devillers. Langage de scenario pour des acteurs semi-autonomes. PhD thesis, IRISA Universite Rennes1, 2001. 14. J. Rickel and W. Johnson. Steve: An animated pedagogical agent for procedural training in virtual environments. In Intelligent virtual agents, Proceedings of Animated Interface Agents: Making Them Intelligent, pages 71–76, 1997. 15. J. Rickel and W. Johnson. Animated agents for procedural training in virtual reality: perception, cognition, and motor control. In Applied artificial intelligence, volume 13, pages 343–382, 1999. 16. Norman I. Badler, Charles A. Erignac, and Ying Liu. Virtual humans for validating maintenance procedures. CACM, 45(7):56–63, July 2002. 17. N. Mollet. De l’Objet-Relation au Construire en Faisant: Application a la specification de scenarios de formation a la maintenance en Realite Virtuelle. PhD thesis, IRISA, 2005. 18. Stphane Donikian and Bruno Arnaldi. Complexity and concurrency for behavioral animation and simulation. In Fifth Eurographics workshop on animation and simulation, pages 101–113, 1994. 19. Fabrice Lamarche and Stphane Donikian. Automatic orchestration of behaviours through the management of resources and priority levels. In AAMAS ’02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems, pages 1309–1316. ACM Press, 2002. 20. N. Mollet, E. Cazeaux, F. Devillers, E. Maffre, B. Arnaldi, and J. Tisseau. Patent: Methode de modelisation graphique et comportementale tridimensionnelle, reactive et interactive d’au moins deux objets. Number : FR0314480, december 2003. 21. N. Mollet, E. Cazeaux, F. Devillers, E. Maffre, B. Arnaldi, and J. Tisseau. Patent: Methode de scenarisation de session de formation. Number : FR0406229, june 2004. 22. E. Maffre, E. Cazeaux, N. Mollet, F. Devillers, B. Arnaldi, and J. Tisseau. Patent: Methode pedagogique detectant l’intention de l’apprenant. Number : FR0406228, june 2004. 23. N. Mollet, E. Cazeaux, F. Devillers, E. Maffre, B. Arnaldi, and J. Tisseau. Patent: Methode de construction d’une session de formation. Number : FR0406230, june 2004. 24. N. Mollet, E. Cazeaux, F. Devillers, E. Maffre, B. Arnaldi, and J. Tisseau. Patent: Systeme de formation a l’exploitation, a l’utilisation ou a la maintenance d’un cadre de travail. Number : FR0406231, june 2004. 25. N. Mollet, E. Cazeaux, F. Devillers, E. Maffre, B. Arnaldi, and J. Tisseau. Patent: Systeme de formation a l’exploitation, a l’utilisation ou a la maintenance d’un cadre de travail dans un environnement de realite virtuelle. Number : 05291204.5, june 2005. 26. E. Cazeaux, F. Devillers, C. Saint-Romas, B. Arnaldi, E. Maffre, N. Mollet, and J. Tisseau. Giat virtual training formation a la maintenance. In Laval Virtual, 2005.

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