3 citations · 3 across the 3 of their papers we have counts for
5 papers
Memory-based gaze prediction in deep imitation learning for robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Deep imitation learning is a promising approach that does not require hard-coded control rules in autonomous robot manipulation. The current applications of deep imitation learning…
Third-party Evaluation of Robotic Hand Designs Using a Mechanical Glove
Takayuki Kanai, Yoshiyuki Ohmura, Akihiko Nagakubo +1
A robotic hand design suitable for dexterity should be examined using functional tests. To achieve this, we designed a mechanical glove, which is a rigid wearable glove that enable…
Reinforced Imitation Learning by Free Energy Principle
Ryoya Ogishima, Izumi Karino, Yasuo Kuniyoshi
Reinforcement Learning (RL) requires a large amount of exploration especially in sparse-reward settings. Imitation Learning (IL) can learn from expert demonstrations without explor…
Identifying Critical States by the Action-Based Variance of Expected Return
Izumi Karino, Yoshiyuki Ohmura, Yasuo Kuniyoshi
The balance of exploration and exploitation plays a crucial role in accelerating reinforcement learning (RL). To deploy an RL agent in human society, its explainability is also ess…
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning
Izumi Karino, Kazutoshi Tanaka, Ryuma Niiyama +1
This paper proposes an exploration method for deep reinforcement learning based on parameter space noise. Recent studies have experimentally shown that parameter space noise result…