5 citations · 8 across the 5 of their papers we have counts for
6 papers
Leveraging Demonstrator-perceived Precision for Safe Interactive Imitation Learning of Clearance-limited Tasks
Hanbit Oh, Takamitsu Matsubara
Interactive imitation learning is an efficient, model-free method through which a robot can learn a task by repetitively iterating an execution of a learning policy and a data coll…
Strong pairing from small Fermi surface beyond weak coupling: Application to LaNiO
Hui Yang, Hanbit Oh, Ya-Hui Zhang
The studies of high-temperature superconductors raise a fundamental question: Can a small Fermi surface phase, which violates the Luttinger theorem, exist and give rise to supercon…
Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh +2
Partial Automation (PA) with intelligent support systems has been introduced in industrial machinery and advanced automobiles to reduce the burden of long hours of human operation.…
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation
Hanbit Oh, Hikaru Sasaki, Brendan Michael +1
Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery ac…
Disturbance-Injected Robust Imitation Learning with Task Achievement
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh +2
Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that p…
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies
Hanbit Oh, Hikaru Sasaki, Brendan Michael +1
Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an…