4,158 research outputs found
Distance Perception During Cooperative Virtual Locomotion
Virtual distances are often misperceived, though most past research ignores co-located cooperative systems. Because active locomotion plays a role in spatial perception, cooperative viewpoint control may impact perceived distances. Additionally, the center of projection is generally optimized for a single tracked user, meaning that a single action will result in different visual feedback for each user. We describe a study investigating the effect of a co-located cooperative locomotion interface on virtual distance perception. Results indicate that a slight center-of-projection offset did affect distance estimates for the untracked user, but that the cooperation actions themselves did not play a role. This study brings new insights to designing interfaces which facilitate accurate spatial perception in cooperative applications.FUI Callist
Sharing emotions and space - empathy as a basis for cooperative spatial interaction
Boukricha H, Nguyen N, Wachsmuth I. Sharing emotions and space - empathy as a basis for cooperative spatial interaction. In: Kopp S, Marsella S, Thorisson K, Vilhjalmsson HH, eds. Proceedings of the 11th International Conference on Intelligent Virtual Agents (IVA 2011). LNAI. Vol 6895. Berlin, Heidelberg: Springer; 2011: 350-362.Empathy is believed to play a major role as a basis for humansâ cooperative behavior. Recent research shows that humans empathize with each other to different degrees depending on several modulation factors including, among others, their social relationships, their mood, and the situational context. In human spatial interaction, partners share and sustain a space that is equally and exclusively reachable to them, the so-called interaction space. In a cooperative interaction scenario of relocating objects in interaction space, we introduce an approach for triggering and modulating a virtual humans cooperative spatial behavior by its degree of empathy with its interaction partner. That is, spatial distances like object distances as well as distances of arm and body movements while relocating objects in interaction space are modulated by the virtual humanâs degree of empathy. In this scenario, the virtual humanâs empathic emotion is generated as a hypothesis about the partnerâs emotional state as related to the physical effort needed to perform a goal directed spatial behavior
Shall I describe it or shall I move closer? Verbal references and locomotion in VR collaborative search tasks
Research in pointing-based communication within immersive collaborative
virtual environments (ICVE) remains a compelling area of study. Previous studies explored
techniques to improve accuracy and reduce errors when hand-pointing from a distance. In
this study, we explore how users adapt their behaviour to cope with the lack of accuracy
during pointing. In an ICVE where users can move (i.e., locomotion) when faced with a
lack of laser pointers, pointing inaccuracy can be avoided by getting closer to the object of
interest. Alternatively, collaborators can enrich the utterances with details to compensate
for the lack of pointing precision. Inspired by previous CSCW remote desktop
collaboration, we measure visual coordination, the implicitness of deixisâ utterances and
the amount of locomotion. We design an experiment that compares the effects of the
presence/absence of laser pointers across hard/easy-to-describe referents. Results show
that when users face pointing inaccuracy, they prefer to move closer to the referent rather
than enrich the verbal reference
Agent-based simulation of collective cooperation: from experiment to model
Simulation models of pedestrian dynamics have become an invaluable tool for evacuation planning. Typically, crowds are assumed to stream unidirectionally towards a safe area. Simulated agents avoid collisions through mechanisms that belong to each individual, such as being repelled from each other by imaginary forces. But classic locomotion models fail when collective cooperation is called for, notably when an agent, say a first-aid attendant, needs to forge a path through a densely packed group. We present a controlled experiment to observe what happens when humans pass through a dense static crowd. We formulate and test hypotheses on salient phenomena. We discuss our observations in a psychological framework. We derive a model that incorporates: agentsâ perception and cognitive processing of a situation that needs cooperation; selection from a portfolio of behaviours, such as being cooperative; and a suitable action, such as swapping places. Agentsâ ability to successfully get through a dense crowd emerges as an effect of the psychological model
Thinking Adaptive: Towards a Behaviours Virtual
In this paper we name some of the advantages of
virtual laboratories; and propose that a Behaviours
Virtual Laboratory should be useful for both biologists
and AI researchers, offering a new perspective for
understanding adaptive behaviour. We present our
development of a Behaviours Virtual Laboratory, which
at this stage is focused in action selection, and show
some experiments to illustrate the properties of our
proposal, which can be accessed via Internet
Respiratory, postural and spatio-kinetic motor stabilization, internal models, top-down timed motor coordination and expanded cerebello-cerebral circuitry: a review
Human dexterity, bipedality, and song/speech vocalization in Homo are reviewed within a motor evolution perspective in regard to 

(i) brain expansion in cerebello-cerebral circuitry, 
(ii) enhanced predictive internal modeling of body kinematics, body kinetics and action organization, 
(iii) motor mastery due to prolonged practice, 
(iv) task-determined top-down, and accurately timed feedforward motor adjustment of multiple-body/artifact elements, and 
(v) reduction in automatic preflex/spinal reflex mechanisms that would otherwise restrict such top-down processes. 

Dual-task interference and developmental neuroimaging research argues that such internal modeling based motor capabilities are concomitant with the evolution of 
(vi) enhanced attentional, executive function and other high-level cognitive processes, and that 
(vii) these provide dexterity, bipedality and vocalization with effector nonspecific neural resources. 

The possibility is also raised that such neural resources could 
(viii) underlie human internal model based nonmotor cognitions. 

Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-level features to plan a local collision-free action, which
can be computationally prohibitive and not robust. More importantly, in
practice the performance of these methods are much lower than their centralized
counterparts.
We present a decentralized sensor-level collision avoidance policy for
multi-robot systems, which directly maps raw sensor measurements to an agent's
steering commands in terms of movement velocity. As a first step toward
reducing the performance gap between decentralized and centralized methods, we
present a multi-scenario multi-stage training framework to find an optimal
policy which is trained over a large number of robots on rich, complex
environments simultaneously using a policy gradient based reinforcement
learning algorithm. We validate the learned sensor-level collision avoidance
policy in a variety of simulated scenarios with thorough performance
evaluations and show that the final learned policy is able to find time
efficient, collision-free paths for a large-scale robot system. We also
demonstrate that the learned policy can be well generalized to new scenarios
that do not appear in the entire training period, including navigating a
heterogeneous group of robots and a large-scale scenario with 100 robots.
Videos are available at https://sites.google.com/view/drlmac
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