Learning strategies in table tennis using inverse reinforcement learning

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Visual Imitation Made Easy (2020, methodology): “Kinesthetic demonstrations, in which the expert actively controls the robot arm by exerting force on it, is an effective method [25, 10] of collecting robot manipulation demonstrations for playing ping pong [26] and cutting vegetables [27].”
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization (2016, background): “…a robotic ball-in-cup game (Boularias et al., 2011), learning table tennis with features that include distance of the ball to the opponent’s elbow (Muelling et al., 2014), providing the goal position as a known constraint for robotic grasping (Doerr et al., 2015), and learning highway driving…”
Learning from Demonstration for Shaping through Inverse Reinforcement Learning (2016, background): “Depending on the task, if demonstrating the goal state carries importance one could prefer to append a repetition of the goal state in the set of demonstrated observations, or otherwise prune the observations.”