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pro vyhledávání: '"De Francesco, Zachary"'
Autor:
Ayub, Ali, Mehta, Jainish, De Francesco, Zachary, Holthaus, Patrick, Dautenhahn, Kerstin, Nehaniv, Chrystopher L.
Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interact
Externí odkaz:
http://arxiv.org/abs/2307.00123
Autor:
Ayub, Ali, De Francesco, Zachary, Holthaus, Patrick, Nehaniv, Chrystopher L., Dautenhahn, Kerstin
For long-term deployment in dynamic real-world environments, assistive robots must continue to learn and adapt to their environments. Researchers have developed various computational models for continual learning (CL) that can allow robots to continu
Externí odkaz:
http://arxiv.org/abs/2305.16332
Autor:
Ayub, Ali, De Francesco, Zachary, Mehta, Jainish, Yaakoub Agha, Khaled, Holthaus, Patrick, Nehaniv, Chrystopher L., Dautenhahn, Kerstin
Publikováno v:
ACM Transactions on Human-Robot Interaction; Dec2024, Vol. 13 Issue 4, p1-39, 39p
