Masashi Okada | Researchers | Panasonic AI | Panasonic Research and Development on Artificial Intelligence (AI) - Panasonic

Masashi Okada

Masashi Okada

Masashi Okada

Ph.D in Information Science and Technology
Technology Division

He has been working as a researcher of robotics and AI. His research ranges from smart model control of appliances, to the SOTA research area such as fusion of deep learning and control theory.
His works have been accepted to top conferences such as CoRL, ICRA.

*The department is where the interviewee belonged to at that time

Publications

IEEE International Conference on Robotics and Automation (ICRA) 2021

Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction

Masashi Okada, Tadahiro Taniguchi
Link: https://arxiv.org/abs/2007.14535
PLOS ONE

Panacea: Visual exploration system for analyzing trends in annual recruitment using time-varying graphs

Toshiyuki Yokoyama, Masashi Okada, Tadahiro Taniguchi
Link: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247587
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020 (Acceptance Rate:47%)

PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference

Masashi Okada, Norio Kosaka, Tadahiro Taniguchi
Link: https://arxiv.org/abs/2003.00370
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020 (Acceptance Rate:47%)

Domain-Adversarial and -Conditional State Space Model for Imitation Learning

Ryo Okumura, Masashi Okada, Tadahiro Taniguchi
Link: https://arxiv.org/abs/2001.11628
IEEE International Conference on Robotics and Automation (ICRA 2020) (Acceptance Rate:42% (1,483/3,512))

Multi-person Pose Tracking using Sequential Monte Carlo with Probabilistic Neural Pose Predictor

Masashi Okada, Shinji Takenaka, Tadahiro Taniguchi
Link: https://arxiv.org/abs/1909.07031
Conference on Robot Learning (CoRL 2019) (Acceptance Rate:28% (110/398))

Variational Inference MPC for Bayesian Model-based Reinforcement Learning

Masashi Okada, Tadahiro Taniguchi
Link: https://arxiv.org/abs/1907.04202
IEEE International Conference on Robotics and Automation (ICRA) 2018 (Acceptance Rate:40.6% (1,030/2539))

Acceleration of Gradient-based Path Integral Method for Efficient Optimal and Inverse Optimal Control

Masashi Okada, Tadahiro Taniguchi
Link: https://arxiv.org/abs/1710.06578
arXiv

Path Integral Networks: End-to-End Differentiable Optimal Control

Masashi Okada, Luca Rigazio, Takenobu Aoshima
Link: https://arxiv.org/abs/1706.09597