54 citations · 147 across the 10 of their papers we have counts for
10 papers
Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Long-horizon dexterous robot manipulation of deformable objects, such as banana peeling, is a problematic task because of the difficulties in object modeling and a lack of knowledg…
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…
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer
Heecheol Kim, Yoshiyuki Ohmura, Akihiko Nagakubo +1
Deep imitation learning is promising for robot manipulation because it only requires demonstration samples. In this study, deep imitation learning is applied to tasks that require…
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…
Transformer-based deep imitation learning for dual-arm robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Deep imitation learning is promising for solving dexterous manipulation tasks because it does not require an environment model and pre-programmed robot behavior. However, its appli…
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…
Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
A high-precision manipulation task, such as needle threading, is challenging. Physiological studies have proposed connecting low-resolution peripheral vision and fast movement to t…
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…
Designing spontaneous behavioral switching via chaotic itinerancy
Katsuma Inoue, Kohei Nakajima, Yasuo Kuniyoshi
Chaotic itinerancy is a frequently observed phenomenon in high-dimensional and nonlinear dynamical systems, and it is characterized by the random transitions among multiple quasi-a…
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…