Abstract The need to plan motions for agents with variable shape constraints such as under different formations appears in several virtual and real‐world applications of autonomous agents. In this work, we focus on planning and execution of formation‐aware paths for a group of agents traversing a cluttered environment. The proposed planning framework addresses the trade‐off between being able to enforce a preferable formation when traversing the corridors of the environment, versus accepting to switch to alternative formations requiring less clearance in order to utilize narrower corridors that can lead to a shorter overall path to the final destination. At the planning stage, this trade‐off is addressed with a multi‐layer graph annotated with per‐layer navigation costs and formation transition costs, where each layer represents one formation together with its specific clearance requirement. At the navigation stage, we introduce Corridor Shortest Path Maps (CSPMs), which produce a vector field for guiding agents along the solution corridor, ensuring unobstructed in‐formation navigation in cluttered environments, as well as group motion along lengthwise‐optimal paths in the solution corridor. We also present examples of how our multi‐layer planning framework can be applied to other types of multi‐modal planning problems.

This paper proposes a methodology for computing, visualizing, and analyzing critical decision boundaries for the selection of shortest paths in a given environment. Decision boundaries are defined as the points in a map from which two or more different shortest paths exist towards a destination. This paper introduces the problem of visualizing their evolution, taking into account moving obstacles, moving goals, and as well multiple goals. The proposed visualizations enable analyzing which paths should be taken and at which departure times, such that a destination can be reached by the shortest possible path when taking into account a moving target or time-varying areas to be avoided. The proposed techniques are also applied to the analysis and improvement of exit placement in a given environment, in order to improve the evacuation flow in emergency situations .

Abstract In order to be successfully executed, collaborative tasks performed by two agents often require a cooperative strategy to be learned. In this work, we propose a constraint‐based multi‐agent reinforcement learning approach called constrained multi‐agent soft actor critic (C‐MSAC) to train control policies for simulated agents performing collaborative multi‐phase tasks. Given a task with phases, the first phases are treated as constraints for the final task phase objective, which is addressed with a centralized training and decentralized execution approach. We highlight our framework on a tray balancing task including two phases: tray lifting and cooperative tray control for target following. We evaluate our proposed approach and compare it against its unconstrained variant (MSAC). The performed comparisons show that C‐MSAC leads to higher success rates, more robust control policies, and better generalization performance.

Abstract This article introduces the new problem of planning spatially distributed lanes for supporting multi‐agent navigation applications in 3D environments. Our proposed approach computes the max‐flow of a 3D medial axis representation of the environment in order to globally compute collision‐free lanes exploring the entire free space of the volumetric scene. Our method addresses agent clearance and path dispersion in order to provide a comprehensive solution to globally compute lanes to be used by multiple agents in 3D environments. By selecting the desired lane dispersion our approach offers an intuitive and powerful way to explore variations in the computed collections of lanes. Dispersion is addressed with a combination of new techniques based on max flow computation, clearance‐based path separation, and adaptive shortcut‐based smoothing.

Abstract We present a new methodology for enabling virtual humans to autonomously detect and perform complex full‐body interactions with their environments. Given a parameterized walking controller and a set of motion‐captured example interactions, our method is able to detect when interactions can occur and to coordinate the detected upper‐body interaction with the walking controller in order to achieve full‐body mobile interactions in similar situations. Our approach is based on learning spatial coordination features from the example motions and on associating body‐environment proximity information to the body configurations of each performed action. Body configurations become the input to a regression system, which in turn is able to generate new interactions for different situations in similar environments. The regression model is capable of selecting, encoding and replicating key spatial strategies with respect to body coordination and management of environment constraints as well as determining the correct moment in time and space for starting an interaction. As a result, we obtain an interactive controller able to detect and synthesize coordinated full‐body motions for a variety of complex interactions requiring body mobility. Our results achieve complex interactions, such as opening doors and drawing in a wide whiteboard. The presented approach introduces the concept of learning interaction coordination models that can be applied on top of any given walking controller. The obtained method is simple and flexible, it handles the detection of possible interactions and is suitable for real‐time applications.

We introduce a new method for computing optimal path maps on the GPU using OpenGL shaders. Our method explores GPU rasterization as a way to propagate optimal costs on a polygonal 2D environment, producing optimal path maps which can efficiently be queried at run-time. Our method is implemented entirely with GPU shaders, does not require pre-computation, addresses optimal path maps with multiple points and line segments as sources, and introduces a new optimal path map concept not addressed before: maps with weights at vertices representing possible changes in traversal speed. The produced maps offer new capabilities not explored by previous navigation representations and at the same time address paths with global optimality, a characteristic which has been mostly neglected in animated virtual environments. The proposed path maps partition the input environment into the regions sharing a same parent point along the shortest path to the closest source, taking into account possible speed changes at vertices. The proposed approach is particularly suitable for the animation of multiple agents moving toward the entrances or exits of a virtual environment, a situation which is efficiently represented with the proposed path maps.

Abstract While significant research has been dedicated to the simulation of fluids, not much attention has been given to exploring new interesting behavior that can be generated with different types of non‐Newtonian fluids with nonconstant viscosity. Going in this direction, this paper introduces a computational model for simulating the most interesting phenomena observed in non‐Newtonian shear thickening fluids, which are fluids where viscosity increases with increased stress. These fluids have unique and unconventional behavior, and they often appear in real‐world scenarios such as when sinking in quicksand or when experimenting with popular cornstarch and water mixtures. The fluid exhibits unique phase changes between solid and liquid states, great impact resistance in its solid state, and strong hysteresis effects. Our proposed approach builds on existing non‐Newtonian smoothed particle hydrodynamics (SPH) fluid models in computer graphics and introduces an efficient history‐based stiffness term that is essential to produce the most interesting types of shear thickening phenomena. The history‐based stiffness is formulated through the use of fractional derivatives, leveraging the fractional calculus ability to depict both the viscoelastic behavior and the history effects of history‐dependent systems. Simulations produced by our method are compared against real experiments, and the results demonstrate that the proposed model successfully captures key phenomena specific to discontinuous shear thickening fluids.

We address the problem of efficient navigation planning for virtual characters with multiple locomotion behaviors. One main difficulty in this problem is the need to rely on expensive collision detection computations which typically lead to methods unsuitable for realtime applications. At the same time, the alternative of using reactive path following methods lead to characters less capable of addressing cluttered environments. In this paper we propose a locomotion planning approach that incorporates the behavioral capabilities of the character during the path planning stage without the need to rely on expensive collision detection computations. Our approach is based on computing paths and clearance tests in layered navigation meshes, where layers are positioned at different body heights in order to reduce 3D collision checking to fast 2D clearance determination per layer. The proposed approach significantly improves the overall planning time making our method suitable for real-time applications while successfully addressing tradeoffs between path length and selection of narrow passages according to preferred locomotion behaviors.

Character navigation in virtual environments is traditionally approached by first planning a path in the free portion of the environment and then employing steering behaviors that reactively adapt to constraints encountered during path following. Unfortunately a shortcoming of this approach is that the path planning stage does not take into account locomotion behavior choices and trade-offs during the path computation. We propose an approach for incorporating the behavioral capabilities of the character in the path planning stage. The produced paths address trade-offs related to path length and navigation behavior for handling narrow passages. The proposed behavioral path planner uses a combination of clearance-based path planning and character geometry collision detection with the 3D environment in order to achieve results suitable for interactive navigation in cluttered environments. The resulted paths address the natural behavior of preferring paths with enough clearance for regular walking when possible, while also considering shorter paths which need a combination of collision avoidance and lateral steps to be executed.

A navigation mesh is a representation of a 2D or 3D virtual environment that enables path planning and crowd simulation for walking characters. Various state-of-the-art navigation meshes exist, but there is no standardized way of evaluating or comparing them. Each implementation is in a different state of maturity, has been tested on different hardware, uses different example environments, and may have been designed with a different application in mind.

The widespread availability of high-quality motion capture data and the maturity of solutions to animate virtual characters has paved the way for the next generation of interactive virtual worlds exhibiting intricate interactions between characters and the environments they inhabit. However, current motion synthesis techniques have not been designed to scale with complex environments and contact-rich motions, requiring environment designers to manually embed motion semantics in the environment geometry in order to address online motion synthesis. This paper presents an automated approach for analyzing both motions and environments in order to represent the different ways in which an environment can afford a character to move. We extract the salient features that characterize the contact-rich motion repertoire of a character and detect valid transitions in the environment where each of these motions may be possible, along with additional semantics that inform which surfaces of the environment the character may use for support during the motion. The precomputed motion semantics can be easily integrated into standard navigation and animation pipelines in order to greatly enhance the motion capabilities of virtual characters. The computational efficiency of our approach enables two additional applications. Environment designers can interactively design new environments and get instant feedback on how characters may potentially interact, which can be used for iterative modeling and refinement. End users can dynamically edit virtual worlds and characters will automatically accommodate the changes in the environment in their movement strategies.

Thanks to recent advances on motion capture devices and stereoscopic consumer displays, animated virtual characters can now realistically interact with users in a variety of applications. We investigate in this paper the effect of avatars, stereo vision and display size on task execution in immersive virtual environments. We report results obtained with three experiments in varied configurations that are commonly used in rehabilitation applications. The first experiment analyzes the accuracy of reaching tasks under different system configurations: with and without an avatar, with and without stereo vision, and employing a 2D desktop monitor versus a large multi-tile visualization display. The second experiment analyzes the use of avatars and user-perspective stereo vision on the ability to perceive and subsequently reproduce motions demonstrated by an autonomous virtual character. The third experiment evaluates the overall user experience with a complete immersive user interface for motion modeling by direct demonstration. Our experiments expose and quantify the benefits of using stereo vision and avatars, and show that the use of avatars improve the quality of produced motions and the resemblance of replicated msotions; however, direct interaction in user-perspective leads to tasks executed in less time and to targets more accurately reached. These and additional tradeoffs are important for the effective design of avatar-based training systems.

In order to deliver information effectively, virtual human demonstrators must be able to address complex spatial constraints and at the same time replicate motion coordination patterns observed in human-human interactions. We introduce in this paper a whole-body motion planning and synthesis framework that coordinates locomotion, body positioning, action execution and gaze behavior for generic demonstration tasks among obstacles. Human-like solutions are achieved with a coordination model extracted from experiments with human subjects. Given an observer location and a target demonstration to be performed, the proposed planner automatically identifies body placements respecting visibility constraints, locomotion accessibility, and action feasibility among obstacles. Actions are modeled with clusters of example motions and a fast collision avoidance procedure in blending space is introduced to avoid nearby obstacles when needed. Locomotion towards new placements integrates planning among obstacles and is based on a motion capture database organized for efficient synthesis of motions with precise path following and arrival constraints. The proposed solution introduces effective approaches for modeling and solving complex demonstrative tasks for interactive applications.

We present a new approach for whole-body motion synthesis that is able to generate high-quality motions for challenging mobile-manipulation scenarios. Our approach decouples the problem in specialized locomotion and manipulation skills, and proposes a multi-modal planning scheme that explores the search space of each skill together with the possible transition points between skills. In order to achieve high-quality results the locomotion skill is designed to be fully data-driven, while manipulation skills can be algorithmic or data-driven according to data availability and the complexity of the environment. Our method is able to automatically generate complex motions with precise manipulation targets among obstacles and in coordination with locomotion.

This paper addresses the problem of group path planning while maintaining group coherence and persistence. Group coherence ensures that a group minimizes both longitudinal and lateral dispersion, and is achieved with the introduction of a deformation penalty to the cost formulation. When the deformation penalty is significantly high, a group may split and later merge. Group persistence is modeled by introducing split and merge actions in the action space, and adding a split penalty to the cost measure. We formulate the problem domain (state, action space, and cost formulation), present our path planning approach for coherent and persistent groups, and provide empirical results demonstrating the capabilities of our method on a variety of challenging scenarios.

We present in this paper a new GPU-based approach to compute Shortest Path Maps (SPMs) from a source point in a polygonal domain. Our method takes advantage of GPU polygon rasterization with shader programming. After encoding the SPM in the frame buffer, globally shortest paths are efficiently computed in time proportional to the number of vertices in the path, and length queries are computed in constant time. We have evaluated our method in multiple environments and our results show a significant speedup in comparison to previous approaches.

We present preliminary results of a framework that can synthesize parameterized locomotion with controllable quality from simple deformations over a single step cycle. Our approach enforces feet constraints per phase in order to appropriately perform motion deformation operations, resulting in a generative and controllable model that maintains the style of the input motion. The method is lightweight and has quantifiable motion quality related to the amount of deformation used. It only requires a single cycle of locomotion. An analysis of the deformation is presented with the quantification of the valid portion of the deformed motion space, informing on the parameterization coverage of the deformable motion cycle.

The Local Clearance Triangulation (LCT) of polygonal obstacles is a cell decomposition designed for the efficient computation of locally shortest paths with clearance. This article presents a revised definition of LCTs, new theoretical results and optimizations, and new algorithms introducing dynamic updates and robustness. Given an input obstacle set with n vertices, a theoretical analysis is proposed showing that LCTs generate a triangular decomposition of O ( n ) cells, guaranteeing that discrete search algorithms can compute paths in optimal times. In addition, several examples are presented indicating that the number of triangles is low in practice, close to 2 n , and a new technique is described for reducing the number of triangles when the maximum query clearance is known in advance. Algorithms for repairing the local clearance property dynamically are also introduced, leading to efficient LCT updates for addressing dynamic changes in the obstacle set. Dynamic updates automatically handle intersecting and overlapping segments with guaranteed robustness, using techniques that combine one exact geometric predicate with adjustment of illegal floating-point coordinates. The presented results demonstrate that LCTs are efficient and highly flexible for representing dynamic polygonal environments with clearance information.

We present a new software framework for the development of immersive collaborative virtual reality applications with emphasis on full-body interaction with virtual humans. The framework is designed to be scalable and adaptable to different hardware configurations, using either commodity or more expensive hardware setups. High level programming abstractions handle object replication and communication channels for configurations based on multiple rendering nodes. The system provides tools for full-body character animation, real-time full-body tracking and retargeting to virtual humans, including motion reconstruction from a small set of sensors. Such capabilities allow new full-body interaction metaphors to be developed in fixed or user-perspective stereo visualization. The framework is scalable, customizable, cross-platform and it is only based on tools with source code available.

We present new solutions based on Virtual Reality technologies for improving the delivery of physical therapy and rehabilitation. Three main aspects are addressed: 1) the ability to allow therapists to create new exercises and therapy programs intuitively by direct demonstration, 2) automatic therapy delivery and monitoring with the use of an autonomous virtual tutor that can monitor and quantitatively assess the motions performed by the patient, and 3) networked collaborative remote therapy sessions via connected applications displaying the motions of both the therapist and the patient. We also provide 3D assessment tools for monitoring changes in the range of motion, and for allowing the visualization of a number of properties during or after the execution of exercises. The presented system has been implemented for a low-cost hardware solution based on Kinect and for a high-end immersive virtual reality facility.

ABSTRACT We present a system that allows non‐programmers to create generic controllers for physically simulated characters. The core of our system is based on a directed acyclic graph of trajectory transformations, which can be modified by feedback terms and serve as reference motions tracked by the physically simulated character. We then introduce tools to enable the automatic creation of robust and parameterized controllers suitable for running in real‐time applications, such as in computer games. The entire process is accomplished by means of a graphical user interface, and we demonstrate how our system can be intuitively used to design a SIMBICON‐like walking controller and a parameterized jump controller to be used in real‐time simulations. Copyright © 2013 John Wiley & Sons, Ltd.

This issue is a special issue with selected papers from Motion in Games (MIG) 2012, which was held during November 15–17 in Rennes, France. Five papers were selected by a review committee composed of Paul Kry, McGill University; Rachel McDonnell, Trinity College Dublin; and Arjan Egges, Utrecht University. The review committee took into account not only the manuscripts but also the presentations and the potential for impact on the motion in games area that is being nurtured by MIG. This special issue contains four out of the five selected papers. The fifth paper will appear in the next issue of Computer Animation & Virtual Worlds. The first paper on this issue is from Peter Sandilands, Myung Geol Choi and Taku Komura, from the University of Edinburgh, UK. The authors propose a technique for action motion capture that allows them to capture an object's motion and geometry alongside a character's movement and local environment, using a magnetic motion capture system and a RGB-D sensor. Traditional methods of actor motion capture do not give any information about the spatial relationship between objects you may interact with, or are limited to large props and motions that are not occluded during capture. The proposed method not only gives greater information when placing a character in the scene, but enables the authors to digitally recreate the scene in motion without significant animator work after capture. The second paper by Junghyun Ahn, Stephane Gobron, Daniel Thalmann, and Ronan Boulic, from Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland, and NTU, Singapore, addresses emotional expressivity for embodied conversational agents by considering asymmetric facial expressions. The asymmetry of facial expressions helps to convey complex emotional feelings such as conflicting and/or hidden emotions due to social conventions. The proposed linear model can automatically drive a large number of autonomous virtual humans, or support the interactive design of complex facial expressions over time. The approach produces facial expressions for most of the emotional spectrum and it can also achieve more complex ambivalent feelings when differing emotions are applied on the left and right sides of the face. The third paper by Robert Backman and Marcelo Kallmann from the University of California, Merced, presents a system that allows non-programmers to create generic controllers for physically-simulated characters. The core of the proposed system is based on a directed acyclic graph of trajectory transformations, which can be modified by feedback terms and serve as reference motions tracked by the physically simulated character. The authors introduce tools to enable the automatic creation of robust and parameterized controllers suitable for running in real-time applications, such as in computer games. The entire process is accomplished by means of a graphical user interface. The paper demonstrates how the system can be intuitively used to design a simbicon-like walking controller and a parameterized jump controller to be used in real-time simulations. The last paper of this issue is by Jongmin Kim, Yeongho Seol and Jehee Lee, from the Seoul National University, Korea. The authors describe a real-time performance animation system that reproduces full-body character animation based on sparse 3D motion sensors on a performer. Producing faithful character animation from this setting is a mathematically ill-posed problem because input data from the sensors are not sufficient to determine the full degrees of freedom of a character. Given the input data from 3D motion sensors, similar poses are selected from a motion database and a local model is built for transforming on-line the low-dimensional input signal into a high-dimensional character pose. A regression method based on kernel CCA (Canonical Correlation Analysis) is employed and it effectively handles a wide variety of motions. Examples show that various human motions are naturally reproduced by the proposed method.

ABSTRACT We propose a new method based on the use of fractional differentiation for improving the efficiency and realism of simulations based on smoothed particle hydrodynamics (SPH). SPH represents a popular particle‐based approach for fluid simulation and a high number of particles is typically needed for achieving high quality results. However, as the number of simulated particles increase, the speed of computation degrades accordingly. The proposed method employs fractional differentiation to improve the results obtained with SPH in a given resolution. The approach is based on the observation that effects requiring a high number of particles are most often produced from colliding flows, and therefore, when the modeling of this behavior is improved, higher quality results can be achieved without changing the number of particles being simulated. Our method can be employed to reduce the resolution without significant loss of quality, or to improve the quality of the simulation in the current chosen resolution. The advantages of our method are demonstrated with several quantitative evaluations. Copyright © 2013 John Wiley & Sons, Ltd.

We propose feature-based motion graphs for realistic locomotion synthesis among obstacles. Among several advantages, feature-based motion graphs achieve improved results in search queries, eliminate the need of postprocessing for foot skating removal, and reduce the computational requirements in comparison to traditional motion graphs. Our contributions are threefold. First, we show that choosing transitions based on relevant features significantly reduces graph construction time and leads to improved search performances. Second, we employ a fast channel search method that confines the motion graph search to a free channel with guaranteed clearance among obstacles, achieving faster and improved results that avoid expensive collision checking. Lastly, we present a motion deformation model based on Inverse Kinematics applied over the transitions of a solution branch. Each transition is assigned a continuous deformation range that does not exceed the original transition cost threshold specified by the user for the graph construction. The obtained deformation improves the reachability of the feature-based motion graph and in turn also reduces the time spent during search. The results obtained by the proposed methods are evaluated and quantified, and they demonstrate significant improvements in comparison to traditional motion graph techniques.

We introduce an approach for enabling sampling-based planners to compute motions with humanlike appearance. The proposed method is based on a space of blendable example motions collected by motion capture. This space is explored by a sampling-based planner that is able to produce motions around obstacles while keeping solutions similar to the original examples. The results therefore largely maintain the humanlike characteristics observed in the example motions. The method is applied to generic upper-body actions and is complemented by a locomotion planner that searches for suitable body placements for executing upper-body actions successfully. As a result, our overall multi-modal planning method is able to automatically coordinate whole-body motions for action execution among obstacles, and the produced motions remain similar to example motions given as input to the system.

Two studies investigated the nature of environmental context on various parameters of pointing. The results revealed the need for extreme temporal precision and the need for efficient algorithms to parse out different styles of pointing. Most variability in pointing came from individual differences, and a method to classify the kind of point and derive its temporal parameters is discussed. These results and methods improve the pragmatism of virtual reality, making events appear more realistic by emphasizing temporal precision.

This paper presents a multi-skill motion planner which is able to sequentially synchronize parameterized motion skills in order to achieve humanoid motions exhibiting complex whole-body coordination. The proposed approach integrates sampling-based motion planning in continuous parametric spaces with discrete search over skill choices, selecting the search strategy according to the functional type of each skill being coordinated. As a result, the planner is able to sequence arbitrary motion skills (such as reaching, balance adjustment, stepping, etc) in order to achieve complex motions needed for solving humanoid reaching tasks in realistic environments. The proposed framework is applied to the HOAP-3 humanoid robot and several results are presented.

We introduce the use of fractional differentiation for simulating cloth deformations underwater. The proposed approach is able to achieve realistic underwater deformations without simulating the Eulerian body of water in which the cloth is immersed. Instead, we propose a particle-based cloth model where half-derivative viscoelastic elements are included for describing both the internal and external dynamics of the cloth. These elements model the cloth responses to fluid stresses and are also able to emulate the memory-laden behavior of particles in a viscous fluid. As a result, we obtain fractional clothes , which are able to correctly depict the dynamics of the immersed cloth interacting with the fluid even though the fluid is not simulated. The proposed approach produces realistic underwater cloth deformations and has obvious advantages in simplicity and speed of computation in comparison to volumetric fluid simulation approaches.

Abstract This paper presents a novel whole‐body analytical inverse kinematics (IK) method integrating collision avoidance and customizable body control for animating reaching tasks in real‐time. Whole‐body control is achieved with the interpolation of pre‐designed key body postures, which are organized as a function of the direction to the goal to be reached. Arm postures are computed by the analytical IK solution for human‐like arms and legs, extended with a new simple search method for achieving postures avoiding joint limits and collisions. In addition, a new IK resolution is presented that directly solves for joints parameterized in the swing‐and‐twist decomposition. The overall method is simple to implement, fast, and accurate, and therefore suitable for interactive applications controlling the hands of characters. The source code of the IK implementation is provided. Copyright © 2007 John Wiley & Sons, Ltd.

A central challenging problem in humanoid robotics is to plan and execute dynamic tasks in dynamic environments. Given that the environment is known, sampling-based online motion planners are best suited for handling changing environments. However, without learning strategies, each task still has to be planned from scratch, preventing these algorithms from getting closer to realtime performance. This paper proposes a novel learning-based motion planning algorithm for addressing this issue. Our algorithm, called the Attractor Guided Planner (AGP), extends existing motion planners in two simple but important ways. First, it extracts significant attractor points from successful paths in order to reuse them as guiding landmarks during the planning of new similar tasks. Second, it relies on a task comparison metric for deciding when previous solutions should be reused for guiding the planning of new tasks. The task comparison metric takes into account the task specification and as well environment features which are relevant to the query. Several experiments are presented with different humanoid reaching examples in the presence of randomly moving obstacles. Our results show that the AGP greatly improves both the planning time and solution quality, when comparing to traditional sampling-based motion planners.

Editing recorded motions to make them suitable for different sets of environmental constraints is a general and difficult open problem. In this paper we solve a significant part of this problem by modifying full-body motions with an interactive randomized motion planner. Our method is able to synthesize collision-free motions for specified linkages of multiple animated characters in synchrony with the characters' full-body motions. The proposed method runs at interactive speed for dynamic environments of realistic complexity. We demonstrate the effectiveness of our interactive motion editing approach with two important applications: (a) motion correction (to remove collisions) and (b) synthesis of realistic object manipulation sequences on top of locomotion.

We evaluate the use of dynamic roadmaps for online motion planning in changing environments. When changes are detected in the workspace, the validity state of affected edges and nodes of a precompiled roadmap are updated accordingly. We concentrate in this paper on analyzing the tradeoffs between maintaining dynamic roadmaps and applying an on-line bidirectional rapidly-exploring random tree (RRT) planner alone, which requires no preprocessing or maintenance. We ground the analysis in several benchmarks in virtual environments with randomly moving obstacles. Different robotics structures are used, including a 17 degrees of freedom model of NASA's Robonaut humanoid. Our results show that dynamic roadmaps can be both faster and more capable for planning difficult motions than using on-line planning alone. In particular, we investigate its scalability to 3D workspaces and higher dimensional configurations spaces, as our main interest is the application of the method to interactive domains involving humanoids.

In most vehicle simulators, complete physical mockups equipped with steering wheel, gearshift and pedals are required. These devices are essential in trying to simulate real conditions, but as a drawback the system becomes expensive, huge (non mobile), and limited to reflect changes on the vehicle type, dimensions, or interior design. We have implemented different configurations for an immersive vehicle simulator, ranging from the use of a real mockup equipped with force-feedback steering wheel, gearshift and pedals, to fully virtual control metaphors, which are based only on trackers and datagloves. We propose such fully virtual control metaphors as an alternative to minimize the use of physical devices in simulators.

Abstract We present new techniques that use motion planning algorithms based on probabilistic roadmaps to control 22 degrees of freedom (DOFs) of human‐like characters in interactive applications. Our main purpose is the automatic synthesis of collision‐free reaching motions for both arms, with automatic column control and leg flexion. Generated motions are collision‐free, in equilibrium, and respect articulation range limits. In order to deal with the high (22) dimension of our configuration space, we bias the random distribution of configurations to favor postures most useful for reaching and grasping. In addition, extensions are presented in order to interactively generate object manipulation sequences: a probabilistic inverse kinematics solver for proposing goal postures matching pre‐designed grasps; dynamic update of roadmaps when obstacles change position; online planning of object location transfer; and an automatic stepping control to enlarge the character's reachable space. This is, to our knowledge, the first time probabilistic planning techniques are used to automatically generate collision‐free reaching motions involving the entire body of a human‐like character at interactive frame rates. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism

We describe an approach to construct interactive virtual environments, which are suitable for the development of artificial virtual human life simulations. Our main goal is to have virtual human actors living and working autonomously in virtual environments. In our approach, virtual actors have their own motivations and needs, and by sensing and exploring their environment, an action selection mechanism is able to determine at anytime the suitable actions to take. We adapt basic actor motivations and needs to urban situations, where most actions involve interactions with the environment. Thus, a specific technique to define actor-object interactions is used, where pre-defined interaction plans are put inside interactive objects, and just selected during the simulation. We explain the steps taken in order to construct and animate such environments, and we also present a test simulation example.

In this paper we present a new framework in Augmented Reality context for rapid evaluation of prototypes before manufacture. The design of such prototypes is a time consuming process, leading t o the need of previous evaluation in realistic interactive environments. We have extended the definition of modelling object geometry with modelling object behaviour being able to evaluate them in a mixed environment. Such enhancements allow the development of tools and methods to test object behaviour, and perform interactions between real virtual humans and complex real and virtual objects.We propose a framework for testing the design of objects i an augmented reality context, where a virtual human is able to perform evaluation tests with an object composed of rea and virtual components. In this paper our framework is described and a case study is presented.

The paper shows a novel approach to model and control interactive objects for simulations with virtual human agents when real time interactivity is essential. A general conceptualization is made to model objects with behaviors that can provide: information about their functionality changes in appearance from parameterized deformations, and a complete plan for each possible interaction with a virtual human. Such behaviors are described with simple primitive commands, following the actual trend of many standard scene graph file formats that connects language with movements and events to create interactive animations. In our case, special attention is given to correctly interpret object behaviors in parallel; a situation that arrives when many human agents interact at the same time with one same object.

Performing 3D interactions with virtual objects easily becomes a complex task, limiting the implementation of larger applications. In order to overcome some of these limitations, this paper describes a framework where the virtual object aids the user to accomplish a pre-programmed possible interaction. Such objects are called Smart Objects, in the sense that they know how the user can interact with them, giving clues to aid the interaction. We show how such objects are constructed, and exemplify the framework with an application where the user, wearing a data glove, can easily open and close drawers of some furniture. Keywords Virtual Reality, manipulation, interaction, data glove, virtual objects, virtual environments. 1. INTRODUCTION Virtual Reality (VR) technology has been employed on various different applications, as for example: human factor analysis, navigation in virtual environments, training, visualisation, and design of objects. A common point to all applications i...