Learning grasping affordances from local visual descriptors

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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, methodology): “Our approach is most closely related to recent work on self-supervised learning of grasp poses by Pinto and Gupta (2016), as well as earlier work on learning from autonomous experimentation (Detry et al., 2011; Montesano and Lopes, 2009).”
Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions (2012, methodology): “This paper extends the beta–binomial regression algorithm of [13], shows its relation to kernel regression and develops active learning strategies for such a model.”
Abstraction Levels for Robotic Imitation: Overview and Computational Approaches (2010, background): “Perception-action maps have appeared in several forms such as the Q-function of a reinforcement learning algorithm [155]; a pure regression map from object features to image points based labelled examples [125, 126] and self-experimentation [39, 92]; or as the correct position of a”
Learning of grasp selection based on shape-templates (2013, methodology): “These require large amounts of labeled training data which can be acquired either by evaluating grasps on a real robotic sys-tem (Montesano and Lopes 2009; Detry et al. 2010; Erkan et al. 2010) or from synthetic sensor data with a manually chosen label (Boularias et al. 2011; Bohg and Kragic 2009;…”