61 research outputs found
Converting Biomechanical Models from OpenSim to MuJoCo
OpenSim is a widely used biomechanics simulator with several anatomically
accurate human musculo-skeletal models. While OpenSim provides useful tools to
analyse human movement, it is not fast enough to be routinely used for emerging
research directions, e.g., learning and simulating motor control through deep
neural networks and Reinforcement Learning (RL). We propose a framework for
converting OpenSim models to MuJoCo, the de facto simulator in machine learning
research, which itself lacks accurate musculo-skeletal human models. We show
that with a few simple approximations of anatomical details, an OpenSim model
can be automatically converted to a MuJoCo version that runs up to 600 times
faster. We also demonstrate an approach to computationally optimize MuJoCo
model parameters so that forward simulations of both simulators produce similar
results.Comment: Submitted to 5th International Conference on NeuroRehabilitation
(ICNR2020
"An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals
Text-to-image generation (TTIG) models, a recent addition to creative AI, can
generate images based on a text description. These models have begun to rival
the work of professional creatives, and sparked discussions on the future of
creative work, loss of jobs, and copyright issues, amongst other important
implications. To support the sustainable adoption of TTIG, we must provide
rich, reliable and transparent insights into how professionals perceive, adopt
and use TTIG. Crucially though, the public debate is shallow, narrow and
lacking transparency, while academic work has focused on studying the use of
TTIG in a general artist population, but not on the perceptions and attitudes
of professionals in a specific industry. In this paper, we contribute a
qualitative, exploratory interview study on TTIG in the Finnish videogame
industry. Through a Template Analysis on semi-structured interviews with 14
game professionals, we reveal 12 overarching themes, structured into 49
sub-themes on professionals' perception, adoption and use of TTIG systems in
games industry practice. Experiencing (yet another) change of roles and
creative processes, our participants' reflections can inform discussions within
the industry, be used by policymakers to inform urgently needed legislation,
and support researchers in games, HCI and AI to support the sustainable,
professional use of TTIG to benefit people and games as cultural artefacts.Comment: 32 pages (incl. appendix), 3 figures, 4 tables. Coding template (31
pages, 10 tables), study invitations (email, social media) and pre-study
survey provided as supplementary (ancillary) material. Accepted with minor
revisions at ACM CHI Play 202
Utilizing gravity in movement-based games and play
This paper seeks to expand the understanding of gravity as a powerful but underexplored design resource for movement-based games and play. We examine how gravity has been utilized and manipulated in digital, physical, and mixed reality games and sports, considering five central and gravity-related facets of user experience: realism, affect, challenge, movement diversity, and sociality. For each facet, we suggest new directions for expanding the field of movement-based games and play, for example through novel combinations of physical and digital elements.
Our primary contribution is a structured articulation of a novel point of view for designing games and interactions for the moving body. Additionally, we point out new research directions, and our conceptual framework can be used as a design tool. We demonstrate this in 1) creating and evaluating a novel gravity-based game mechanic, and 2) analyzing an existing movement-based game and suggesting future improvements
Gender, Age, and Technology Education Influence the Adoption and Appropriation of LLMs
Large Language Models (LLMs) such as ChatGPT have become increasingly
integrated into critical activities of daily life, raising concerns about
equitable access and utilization across diverse demographics. This study
investigates the usage of LLMs among 1,500 representative US citizens.
Remarkably, 42% of participants reported utilizing an LLM. Our findings reveal
a gender gap in LLM technology adoption (more male users than female users)
with complex interaction patterns regarding age. Technology-related education
eliminates the gender gap in our sample. Moreover, expert users are more likely
than novices to list professional tasks as typical application scenarios,
suggesting discrepancies in effective usage at the workplace. These results
underscore the importance of providing education in artificial intelligence in
our technology-driven society to promote equitable access to and benefits from
LLMs. We urge for both international replication beyond the US and longitudinal
observation of adoption
Learning Task-Agnostic Action Spaces for Movement Optimization
We propose a novel method for exploring the dynamics of physically based
animated characters, and learning a task-agnostic action space that makes
movement optimization easier. Like several previous papers, we parameterize
actions as target states, and learn a short-horizon goal-conditioned low-level
control policy that drives the agent's state towards the targets. Our novel
contribution is that with our exploration data, we are able to learn the
low-level policy in a generic manner and without any reference movement data.
Trained once for each agent or simulation environment, the policy improves the
efficiency of optimizing both trajectories and high-level policies across
multiple tasks and optimization algorithms. We also contribute novel
visualizations that show how using target states as actions makes optimized
trajectories more robust to disturbances; this manifests as wider optima that
are easy to find. Due to its simplicity and generality, our proposed approach
should provide a building block that can improve a large variety of movement
optimization methods and applications.Comment: Accepted as a regular paper by IEEE Transactions on Visualization and
Computer Graphics (TVCG) in July 202
Metropolipolitiikan kehittÀmistarpeet
Työ on keskittynyt erityisesti metropolipolitiikkaan toimintatapana. Selvitystyön tavoitteena on metropolipolitiikan vaikuttavuuden parantaminen. TyössÀ on selvitetty muun muassa valtion ja seudun kuntien kumppanuuspohjaisen toimintatavan hyötyjÀ, vaikuttavimpia metropolipolitiikan vÀlineitÀ sekÀ metropolipolitiikan organisointitapaa.
Selvityksen mukaan metropolipolitiikka on myötÀvaikuttanut Helsingin seudun kehitykseen koko Suomen hyvÀksi, ja sitÀ tarvitaan myös jatkossa. Tulevalla hallituskaudella tarvitaan kuitenkin aiempaa tarkemmin mÀÀriteltyjÀ tavoitteita, rajatumpia valintoja, sitovampia sopimuksia, tÀsmÀllisempiÀ vÀlineitÀ sekÀ tehokkaampaa organisoitumista.
Selvityksen mukaan metropolipolitiikka on sijaiskÀrsinyt suurista rakenteellisista uudistusprosesseista, eivÀtkÀ toimijat ole olleet riittÀvÀn sitoutuneita yhteisten tavoitteiden toteuttamiseen. Onnistumisina selvitys nostaa esille erityisesti MAL-yhteensovittamisen ja pitkÀaikaisasunnottomuuden vÀhentÀmisohjelman.
Selvityksen mukaan sisÀllöllisesti ei ole tarvetta tehdÀ radikaaleja muutoksia, mutta metropolipolitiikan ytimeksi kannattaisi asettaa kaksi asiaa: MAL-kysymykset sekÀ kasvu. Kasvuun liittyy olennaisesti kansainvÀlistyminen.
Selvitys korostaa riittÀvÀn johtajuuden ja linjaamiskyvyn merkitystÀ metropolialueen keskeisissÀ kehittÀmiskysymyksissÀ, ja ehdottaa metropolipolitiikalle jatkossa tiiviimpÀÀ organisatorista ydintÀ, jossa on mukana korkein virkamies- ja poliittinen johto.
Selvityksen on laatinut tammi-maaliskuussa 2015 MDI Public Oy Janne Antikaisen johdolla. Työ perustuu kirjalliseen aineistoon ja metropolipolitiikan neuvottelukunnan ja sihteeristön jÀsenten sekÀ asiantuntijoiden haastatteluihin
Voluntary pupil size change as control in eyes only interaction
We investigate consciously controlled pupil size as an input modality. Pupil size is affected by various processes, e.g., physical activation, strong emotional experiences and cognitive effort. Our hypothesis is that given continuous feedback, users can learn to control pupil size via physical and psychological self-regulation. We test it by measuring the magnitude of self evoked pupil size changes following seven different instructions, while providing real time graphical feedback on pupil size. Results show that some types of voluntary effort affect pupil size on a statistically significant level. A second controlled experiment confirms that subjects can produce pupil dilation and constriction on demand during paced tasks. Applications and limitations to using voluntary pupil size manipulation as an input modality are discussed. ACM Classification Keyword
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