Perceiving, learning, and exploiting object affordances for autonomous pile manipulation

105 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours (2015, background): “We ﬁnally demonstrate evaluation in the real robot settings for grasping objects in isolation and show results on clearing a clutter of objects.”
Learning to push by grasping: Using multiple tasks for effective learning (2016, methodology): “Pushing also offers a method of moving objects without needing to explicitly grasp them [20].”
Learning task-oriented grasping for tool manipulation from simulated self-supervision (2018, unknown): “The notion of affordances introduced by Gibson [14] characterizes the functional properties of objects and has been widely used in the robotics community as a framework of reasoning about objects [26, 50].”
Learning Grasping Interaction with Geometry-aware 3D Representations (2017, background): “In addition to general robotic grasping, several recent work investigated the semantic or task-speciﬁc grasping Dang and Allen [2014], Katz et al. [2014], Nikandrova and Kyrki [2015].”
Learning to Singulate Objects using a Push Proposal Network (2017, unknown): “o detect splitting locations between potential objects to apply pushes in those regions respectively, but does not include learned features and does not take into account stacked objects. Katz et al. [14] present an interactive segmentation algorithm to singulate cluttered objects using pushing and ”