Acquisition of Probabilistic Behavior Decision Model based on the Interactive Teaching Method

By Tetsunari Inamura Masayuki Inaba Hirochika. 2001

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Citations in the corpus

Visual Imitation Made Easy (2020, methodology): “This method has been successfully applied to a large range of robotic tasks including ﬂying a robotic helicopter [17], grasping objects [18, 19], navigating robots through cluttered environments [20, 21, 22], and even driving cars [23].”
Multiagent Collaborative Task Learning through Imitation (2007, methodology): “[13] present a similar method based on Bayesian Networks [20] limited to a discretely-”
Interactive Policy Learning through Confidence-Based Autonomy (2014, methodology): “Similarly motivated techniques have been used in robotics to identify situations in which a robot should request a demonstration from its teacher (Grollman & Jenkins, 2007; Lockerd & Breazeal, 2004; Nicolescu, 2003; Inamura et al., 1999).”
International Conference on Intelligent Robots and Systems Robot Behavior Adaptation for Human-Robot Interaction based on Policy Gradient Reinforcement Learning * (2005, unknown): “*This research was supported by the National Institute of Information and Communications Technology of Japan Incremental learning of behavior decision through interaction with a human [6] has been proposed.”
A survey of robot learning from demonstration (2009, unknown): “Demonstrations recorded through human teleoperation via a joystick are used in a variety of applications, including flying a robotic helicopter [6], robot kickingmotions [7], object grasping [8, 9], robotic arm assembly tasks [10] and obstacle avoidance and navigation [11,12].”