Papers
7
Total Citations
89
H-Index
5
About
Michalis Papakostas is a researcher at the intersection of human-robot interaction, assistive technologies, and interactive machine learning. His work focuses on developing intelligent systems that can learn from and adapt to human users in real-time, with applications ranging from robot-assisted therapy to manufacturing collaboration. Papakostas has made significant contributions to grounding robot understanding through multimodal learning, particularly by combining vision, language, and interactive gameplay to teach robots about objects and their meanings. His 2015 paper on grounding word meaning through vision and interactive gameplay (19 citations) demonstrates his innovative approach to democratizing robot training for non-experts. In the healthcare domain, his survey on assistive technologies for motor impairment rehabilitation in Multiple Sclerosis (18 citations) provides a comprehensive framework for technology-assisted therapy. Papakostas has also pioneered the use of physiological signals, including EEG, to predict task performance (17 citations), and developed reinforcement learning frameworks for personalized human-robot collaboration in manufacturing (17 citations). His work on adaptive robot-assisted therapy (10 citations) showcases his commitment to creating systems that learn from both primary users and secondary guidance, making rehabilitation more effective and personalized.
Research Focus
Key Achievements
Top Papers
- 1Grounding the meaning of words through vision and interactive gameplay19 citations · 2015
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- 3Towards predicting task performance from EEG signals17 citations · 2017
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- 6I Spy: An Interactive Game-Based Approach to Multimodal Robot Learning5 citations · 2015
- 7v-CAT3 citations · 2018