1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
Deep Koopman with Control: Spectral Analysis of Soft Robot Dynamics
Naoto Komeno, Brendan Michael, Katharina Küchler +2
Soft robots are challenging to model and control as inherent non-linearities (e.g., elasticity and deformation), often requires complex explicit physics-based analytical modeling (…
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…