Accepted papers
No.1 Construction of a Multimodal Learning Model Based on Integrating Stochastic Models
Ryo Kuniyasu, Tomoaki Nakamura, Takayuki Nagai, and Tadahiro Taniguchi
No.2 Measuring Task Uncertainty in Meta-Imitation Learning
Tatsuya Matsushima, Naruya Kondo, Yusuke Iwasawa, Kaoru Nasuno, and Yutaka Matsuo
No.3 Integrating Simultaneous Localization and Mapping with Map Completion Using Generative Adversarial Networks*
Yuki Katsumata, Lotfi El Hafi, Akira Taniguchi, Yoshinobu Hagiwara, and Tadahiro Taniguchi
No.4 Cognitive Architecture for Joint Attentional Learning of word-object mapping with a Humanoid Robot
Jonas Gonzalez-Billandon, Lukas Grasse, Alessandra Sciutti, Matthew Tata, and Francesco Rea
No.5 Combining Causal Generative Model and Deep Reinforcement Learning for Cognitive Agents in Minecraft.
Andrew Melnik, Lennart Bramlage, Hendric Voss, Federico Rossetto, and Helge Ritter
No.6 A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing
Timo Korthals, Daniel Rudolph, Malte Schilling, and Jurgen Leitner
No.7 Learning Deep Features for Multi-Modal Inference With Robotic Data
Atabak Dehban, Lorenzo Jamone, and Jose Santos-Victor
No.8 Integration of Multiple Generative Modules for Robot Learning
Kazuki Miyazawa, Tatsuya Aoki, Takato Horii, and Takayuki Nagai