17,827 research outputs found
Virtual reality for safe testing and development in collaborative robotics: challenges and perspectives
Collaborative robots (cobots) could help humans in tasks that are mundane, dangerous or
where direct human contact carries risk. Yet, the collaboration between humans and robots is severely
limited by the aspects of the safety and comfort of human operators. In this paper, we outline the
use of extended reality (XR) as a way to test and develop collaboration with robots. We focus on
virtual reality (VR) in simulating collaboration scenarios and the use of cobot digital twins. This is
specifically useful in situations that are difficult or even impossible to safely test in real life, such as
dangerous scenarios. We describe using XR simulations as a means to evaluate collaboration with
robots without putting humans at harm. We show how an XR setting enables combining human
behavioral data, subjective self-reports, and biosignals signifying human comfort, stress and cognitive
load during collaboration. Several works demonstrate XR can be used to train human operators and
provide them with augmented reality (AR) interfaces to enhance their performance with robots. We
also provide a first attempt at what could become the basis for a human–robot collaboration testing
framework, specifically for designing and testing factors affecting human–robot collaboration. The
use of XR has the potential to change the way we design and test cobots, and train cobot operators, in
a range of applications: from industry, through healthcare, to space operations.info:eu-repo/semantics/publishedVersio
Guide to the Networked Minds Social Presence Inventory v. 1.2
This document introduces the Networked\ud
Minds Social Presence Inventory. The\ud
inventory is a self-report measure of social\ud
presence, which is commonly defined as the\ud
sense of being together with another in a\ud
mediated environment. The guidelines\ud
provide background on the use of the social\ud
presence scales in studies of users’ social\ud
communication and interaction with other\ud
humans or with artificially intelligent agents\ud
in virtual environments
I Probe, Therefore I Am: Designing a Virtual Journalist with Human Emotions
By utilizing different communication channels, such as verbal language,
gestures or facial expressions, virtually embodied interactive humans hold a
unique potential to bridge the gap between human-computer interaction and
actual interhuman communication. The use of virtual humans is consequently
becoming increasingly popular in a wide range of areas where such a natural
communication might be beneficial, including entertainment, education, mental
health research and beyond. Behind this development lies a series of
technological advances in a multitude of disciplines, most notably natural
language processing, computer vision, and speech synthesis. In this paper we
discuss a Virtual Human Journalist, a project employing a number of novel
solutions from these disciplines with the goal to demonstrate their viability
by producing a humanoid conversational agent capable of naturally eliciting and
reacting to information from a human user. A set of qualitative and
quantitative evaluation sessions demonstrated the technical feasibility of the
system whilst uncovering a number of deficits in its capacity to engage users
in a way that would be perceived as natural and emotionally engaging. We argue
that naturalness should not always be seen as a desirable goal and suggest that
deliberately suppressing the naturalness of virtual human interactions, such as
by altering its personality cues, might in some cases yield more desirable
results.Comment: eNTERFACE16 proceeding
Deep Reinforcement Learning for Dialogue Generation
Recent neural models of dialogue generation offer great promise for
generating responses for conversational agents, but tend to be shortsighted,
predicting utterances one at a time while ignoring their influence on future
outcomes. Modeling the future direction of a dialogue is crucial to generating
coherent, interesting dialogues, a need which led traditional NLP models of
dialogue to draw on reinforcement learning. In this paper, we show how to
integrate these goals, applying deep reinforcement learning to model future
reward in chatbot dialogue. The model simulates dialogues between two virtual
agents, using policy gradient methods to reward sequences that display three
useful conversational properties: informativity (non-repetitive turns),
coherence, and ease of answering (related to forward-looking function). We
evaluate our model on diversity, length as well as with human judges, showing
that the proposed algorithm generates more interactive responses and manages to
foster a more sustained conversation in dialogue simulation. This work marks a
first step towards learning a neural conversational model based on the
long-term success of dialogues
Simulation from endpoint-conditioned, continuous-time Markov chains on a finite state space, with applications to molecular evolution
Analyses of serially-sampled data often begin with the assumption that the
observations represent discrete samples from a latent continuous-time
stochastic process. The continuous-time Markov chain (CTMC) is one such
generative model whose popularity extends to a variety of disciplines ranging
from computational finance to human genetics and genomics. A common theme among
these diverse applications is the need to simulate sample paths of a CTMC
conditional on realized data that is discretely observed. Here we present a
general solution to this sampling problem when the CTMC is defined on a
discrete and finite state space. Specifically, we consider the generation of
sample paths, including intermediate states and times of transition, from a
CTMC whose beginning and ending states are known across a time interval of
length . We first unify the literature through a discussion of the three
predominant approaches: (1) modified rejection sampling, (2) direct sampling,
and (3) uniformization. We then give analytical results for the complexity and
efficiency of each method in terms of the instantaneous transition rate matrix
of the CTMC, its beginning and ending states, and the length of sampling
time . In doing so, we show that no method dominates the others across all
model specifications, and we give explicit proof of which method prevails for
any given and endpoints. Finally, we introduce and compare three
applications of CTMCs to demonstrate the pitfalls of choosing an inefficient
sampler.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS247 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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Investigation of an emotional virtual human modelling method
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In order to simulate virtual humans more realistically and enable them life-like behaviours, several exploration research on emotion calculation, synthetic perception, and decision making process have been discussed. A series of sub-modules have been designed and simulation results have been presented with discussion.
A visual based synthetic perception system has been proposed in this thesis, which allows virtual humans to detect the surrounding virtual environment through a collision-based synthetic vision system. It enables autonomous virtual humans to change their emotion states according to stimuli in real time. The synthetic perception system also allows virtual humans to remember limited information within their own First-in-first-out short-term virtual memory.
The new emotion generation method includes a novel hierarchical emotion structure and a group of emotion calculation equations, which enables virtual humans to perform emotionally in real-time according to their internal and external factors. Emotion calculation equations used in this research were derived from psychologic emotion measurements. Virtual humans can utilise the information in virtual memory and emotion calculation equations to generate their own numerical emotion states within the hierarchical emotion structure. Those emotion states are important internal references for virtual humans to adopt appropriate behaviours and also key cues for their decision making.
The work introduces a dynamic emotional motion database structure for virtual human modelling. When developing realistic virtual human behaviours, lots of subjects were motion-captured whilst performing emotional motions with or without intent. The captured motions were endowed to virtual characters and implemented in different virtual scenarios to help evoke and verify design ideas, possible consequences of simulation (such as fire evacuation).
This work also introduced simple heuristics theory into decision making process in order to make the virtual human’s decision making more like real human. Emotion values are proposed as a group of the key cues for decision making under the simple heuristic structures. A data interface which connects the emotion calculation and the decision making structure together has also been designed for the simulation system
Perspectives on the Neuroscience of Cognition and Consciousness
The origin and current use of the concepts of computation, representation and information in Neuroscience are examined and conceptual flaws are identified which vitiate their usefulness for addressing problems of the neural basis of Cognition and Consciousness. In contrast, a convergence of views is presented to support the characterization of the Nervous System as a complex dynamical system operating in the metastable regime, and capable of evolving to configurations and transitions in phase space with potential relevance for Cognition and Consciousness
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