67 citations · 192 across the 14 of their papers we have counts for
15 papers
Learning High-Risk High-Precision Motion Control
Nam Hee Kim, Markus Kirjonen, Perttu Hämäläinen
Deep reinforcement learning (DRL) algorithms for movement control are typically evaluated and benchmarked on sequential decision tasks where imprecise actions may be corrected with…
Reflection at Design Actualization (RDA) : A Tool and Process For Research Through Game Design
Prabhav Bhatnagar, Jianheng He, Shamit Ahmed +2
There is a growing interest in researching game design processes, artifacts and culture through active game design. Tools and processes to support these attempts are limited, espec…
LLMCode: Evaluating and Enhancing Researcher-AI Alignment in Qualitative Analysis
Joel Oksanen, Andrés Lucero, Perttu Hämäläinen
The use of large language models (LLMs) in qualitative analysis offers enhanced efficiency but raises questions about their alignment with the contextual nature of research for des…
SIM2VR: Towards Automated Biomechanical Testing in VR
Florian Fischer, Aleksi Ikkala, Markus Klar +6
Automated biomechanical testing has great potential for the development of VR applications, as initial insights into user behaviour can be gained in silico early in the design proc…
Gender, Age, and Technology Education Influence the Adoption and Appropriation of LLMs
Fiona Draxler, Daniel Buschek, Mikke Tavast +4
Large Language Models (LLMs) such as ChatGPT have become increasingly integrated into critical activities of daily life, raising concerns about equitable access and utilization acr…
"An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals
Veera Vimpari, Annakaisa Kultima, Perttu Hämäläinen +1
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…
Cine-AI: Generating Video Game Cutscenes in the Style of Human Directors
Inan Evin, Perttu Hämäläinen, Christian Guckelsberger
Cutscenes form an integral part of many video games, but their creation is costly, time-consuming, and requires skills that many game developers lack. While AI has been leveraged t…
Predicting Game Engagement and Difficulty Using AI Players
Shaghayegh Roohi, Christian Guckelsberger, Asko Relas +3
This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. It has previously been demonstrated that Deep Reinforcemen…
Predicting Game Difficulty and Churn Without Players
Shaghayegh Roohi, Asko Relas, Jari Takatalo +2
We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play game. Our primary contribut…
Deep Residual Mixture Models
Perttu Hämäläinen, Martin Trapp, Tuure Saloheimo +1
We propose Deep Residual Mixture Models (DRMMs), a novel deep generative model architecture. Compared to other deep models, DRMMs allow more flexible conditional sampling: The mode…
Converting Biomechanical Models from OpenSim to MuJoCo
Aleksi Ikkala, Perttu Hämäläinen
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…
Self-Imitation Learning of Locomotion Movements through Termination Curriculum
Amin Babadi, Kourosh Naderi, Perttu Hämäläinen
Animation and machine learning research have shown great advancements in the past decade, leading to robust and powerful methods for learning complex physically-based animations. H…
PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation
Perttu Hämäläinen, Amin Babadi, Xiaoxiao Ma +1
Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach. However, we observe that in a continuous action space, PPO can prematurely s…
Intelligent Middle-Level Game Control
Amin Babadi, Kourosh Naderi, Perttu Hämäläinen
We propose the concept of intelligent middle-level game control, which lies on a continuum of control abstraction levels between the following two dual opposites: 1) high-level con…
An Iterative Closest Points Approach to Neural Generative Models
Joose Rajamäki, Perttu Hämäläinen
We present a simple way to learn a transformation that maps samples of one distribution to the samples of another distribution. Our algorithm comprises an iteration of 1) drawing s…