5 citations · 11 across the 6 of their papers we have counts for
6 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…
Robo-Saber: Generating and Simulating Virtual Reality Players
Nam Hee Kim, Jingjing May Liu, Jaakko Lehtinen +3
We present the first motion generation system for playtesting virtual reality (VR) games. Our player model generates VR headset and handheld controller movements from in-game objec…
Discovering Fatigued Movements for Virtual Character Animation
Noshaba Cheema, Rui Xu, Nam Hee Kim +5
Virtual character animation and movement synthesis have advanced rapidly during recent years, especially through a combination of extensive motion capture datasets and machine lear…
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
Setareh Cohan, Nam Hee Kim, David Rolnick +1
Policies produced by deep reinforcement learning are typically characterised by their learning curves, but they remain poorly understood in many other respects. ReLU-based policies…
ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills
Zhaoming Xie, Hung Yu Ling, Nam Hee Kim +1
Humans are highly adept at walking in environments with foot placement constraints, including stepping-stone scenarios where the footstep locations are fully constrained. Finding g…
Learning to Correspond Dynamical Systems
Nam Hee Kim, Zhaoming Xie, Michiel van de Panne
Many dynamical systems exhibit similar structure, as often captured by hand-designed simplified models that can be used for analysis and control. We develop a method for learning t…