A data-driven statistical framework for post-grasp manipulation

By Robert Paolini, Alberto Rodriguez, Siddhartha S. Srinivasa, Matthew Thomas Mason. 2014

57 citations recorded by the source corpus.

Grasping an object is usually only an intermediate goal for a robotic manipulator. To finish the task, the robot needs to know where the object is in its hand and what action to execute. This paper presents a general statistical framework to address these problems. Given a novel object, the robot learns a statistical model of grasp state conditioned on sensor values. The robot also builds a statistical model of the requirements for a successful execution of the task in terms of uncertainty in the state of the grasp. Both of these models are constructed by offline experiments. The online process then grasps objects and chooses actions to maximize likelihood of success. This paper describes the framework in detail, and demonstrates its effectiveness experimentally in placing, dropping, and insertion tasks. To construct statistical models, the robot performed over 8,000 grasp trials, and over 1,000 trials each of placing, dropping, and insertion.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Control and Systems Engineering

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Citations in the corpus

The Curious Robot: Learning Visual Representations via Physical Interactions (2016, background): “Most initial work focused on using human annotators [21] but in this work we are more interested in building a self-supervision system [22,23,24,25].”
A probabilistic data-driven model for planar pushing (2017, methodology): “[17] use Gaussian processes to learn both the transition dynamics and the observation model of prehensile manipulation tasks.”
Extrinsic dexterity: In-hand manipulation with external forces (2014, methodology): “The following subsections explore those three categories.”