Iterative Training of Dynamic Skills Inspired by Human Coaching Techniques

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Learning symmetric and low-energy locomotion (2018, methodology): “Apart from designing finite state machines or using motion data, reward engineering combined with trajectory optimization has also been frequently applied to generate physically-based character motions [Al Borno et al. 2013; Ha and Liu 2014; Mordatch et al. 2012, 2013; Wampler and Popović 2009].”
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications (2017, background): “A broad range of work has recently exploited physics based simulators for learning purposes, namely in animation and motion planning ([18; 15; 27; 24; 51; 16; 14]), scene understanding ([3; 38]), pedestrian detection ([29]), and identiﬁcation of 2D/3D objects ([17; 31; 39]).”
Teaching UAVs to Race Using UE4Sim (2017, background): “vehicle, animal, human, or UAV) to physically navigate through an unknown environment has traditionally been approached either through supervised learning (SL) [3,4,18,28,34,38,40], reinforcement learning (RL) [22,24, 25, 31, 32, 41], or a combination of the two [1, 5, 9, 10, 21].”
DeepMimic (2018, background): “Many such controllers are the products of an underlying simplified model and an optimization process, where a compact set of parameters are tuned in order to achieve the desired behaviors [Agrawal et al. 2013; Ha and Liu 2014; Wang et al. 2012].”