Learning the dynamics of doors for robotic manipulation

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Deep visual foresight for planning robot motion (2016, methodology): “Learning and optimization-based methods have been applied to various parts of the state-estimation problem, such as object recognition [11], pose registration [12], and dynamics learning [13].”
Robobarista: Object Part Based Transfer of Manipulation Trajectories from Crowd-Sourcing in 3D Pointclouds (2015, background): “ferent controllers to accomplish complicated tasks such as baking cookies [8] and folding the laundry [33], or focus on specific motions such as learning to grasp [24] and open doors [11].”
Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions (2014, background): “Although there are many related works for each instruction (Anand et al., 2012; Bollini et al., 2011; Endres et al., 2013; Jiang et al., 2012; Lenz et al., 2013; Ratliff et al., 2009), it is still a very active area of research and it is quite challenging to execute these instructions reliably in a ”