153 citations · 222 across the 7 of their papers we have counts for
7 papers
Correspondence learning between morphologically different robots via task demonstrations
Hakan Aktas, Yukie Nagai, Minoru Asada +2
We observe a large variety of robots in terms of their bodies, sensors, and actuators. Given the commonalities in the skill sets, teaching each skill to each different robot indepe…
The world seems different in a social context: a neural network analysis of human experimental data
Maria Tsfasman, Anja Philippsen, Carlo Mazzola +3
Human perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli dif…
Exploration with Intrinsic Motivation using Object-Action-Outcome Latent Space
Melisa Sener, Yukie Nagai, Erhan Oztop +1
One effective approach for equipping artificial agents with sensorimotor skills is to use self-exploration. To do this efficiently is critical, as time and data collection are cost…
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing
M. Tuluhan Akbulut, Erhan Oztop, M. Yunus Seker +3
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exp…
Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning
Kyoichiro Kobayashi, Takato Horii, Ryo Iwaki +2
Generative adversarial imitation learning (GAIL) has attracted increasing attention in the field of robot learning. It enables robots to learn a policy to achieve a task demonstrat…
A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder
Pablo Lanillos, Daniel Oliva, Anja Philippsen +3
This survey presents the most relevant neural network models of autism spectrum disorder and schizophrenia, from the first connectionist models to recent deep network architectures…
Improving interactive reinforcement learning: What makes a good teacher?
Francisco Cruz, Sven Magg, Yukie Nagai +1
Interactive reinforcement learning has become an important apprenticeship approach to speed up convergence in classic reinforcement learning problems. In this regard, a variant of…