Akihiko Nagakubo

Training Robots Without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer

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 force feedback. However, existing demonstration methods have deficiencies; bilateral teleoperation requires a complex co