Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks

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Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects (2018, background): “Domain randomization has proved successful at detecting colored geometric shapes on a table [7], flying a quadcopter indoors [8], or learning visuomotor control for reaching [9] or pick-and-place [10] of a brightly colored cube.”
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization (2018, methodology): “[35] was used to position a robot near a blue cube.”
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation (2018, background): “Many research efforts have been devoted to tackling this problem by either domain adaption (DA) [Rusu et al. , 2016; Ghadirzadeh et al. , 2017; Zhang et al. , 2017] or domain randomization (DR) [Sadeghi and Levine, 2017; Peng et al. , 2018; Tobin et al. , 2017; Zhang et al. , 2017].”