Autonomous acquisition of pushing actions to support object grasping with a humanoid robot

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Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning (2018, neutral): “More recent data-driven methods explore the prospects of training model-agnostic deep grasping policies [26], [7], [27], [9], [10], [8] that detect grasps by exploiting learned visual features, and without explicitly using object specific knowledge (i.”
Push planning for object placement on cluttered table surfaces (2011, methodology): “Section VI places our planner in the broader context of clutter manipulation.”
Learning Visual Affordances for Robotic Manipulation (2019, methodology): “Other methods [84, 48] explore the model-free planning of pushing motions to move objects to target positions that are more favorable for pre-designed grasping algorithms – the behaviors of which are typically handcrafted, fixed, and well-known in advance.”
A Planning Framework for Non-Prehensile Manipulation under Clutter and Uncertainty (2012, unknown): “Omrcen et al (2009) propose a method to learn the effect of pushing actions on objects and then use these actions to bring an object to the edge of a table for successful grasping.”