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Antti Oulasvirta

2 accepted papers

2024

Interactive Reward Tuning: Interactive Visualization for Preference Elicitation

IROS 2024poster

In reinforcement learning, tuning reward weights in the reward function is necessary to align behavior with user preferences. However, current approaches, which use pairwise comparisons for preference elicitation, are inefficient, because they miss much of the human ability to explore and judge grou…

Cited by 1SourceScholar
2015

Fast and Robust Hand Tracking Using Detection-Guided Optimization

CVPR 2015poster

Markerless tracking of hands and fingers is a promising enabler for human-computer interaction. However, adoption has been limited because of tracking inaccuracies, incomplete coverage of motions, low framerate, complex camera setups, and high computational requirements. In this paper, we present a…

Cited by 298SourcePDFScholar