Dynamic object manipulation using a virtual frame by a triple soft-fingered robotic hand

By K. Tahara, S. Arimoto, Morio Yoshida. 2010

97 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Control and Systems Engineering

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In Biomedical Engineering

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Citations in the corpus

Learning dexterous in-hand manipulation (2018, background): “12 Other approaches take a closed-loop approach to dexterous manipulation and incorporate sensor feedback during execution, e.g. tactile sensing [60, 34, 35, 36].”
Learning dexterous manipulation for a soft robotic hand from human demonstrations (2016, methodology): “Apart from purely planning-based methods, reactive control methods [10], [11] have been proposed.”
Learning robot in-hand manipulation with tactile features (2015, methodology): “Apart from providing information on the object’s pose and contact normals [11–17], tactile sensing can provide greater robustness to variations in object properties [18–21], perturbations [22, 23], and sensing errors [10, 18].”
Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation (2016, background): “an object-level impedance controller [3] is used to regulate grasp forces.”
Learning of grasp adaptation through experience and tactile sensing (2014, background): “The transition between position controller and force controller can result in various different discrete contact states, each of which usually needs to be treated separately [14], [15].”
On rolling contact motion by robotic fingers via prescribed performance control (2013, background): “Although many proposed controllers in object manipulation by robotic fingers consider rolling contacts, they base their design on robotic system models which are subject to con tact rolling constraints [4], [5].”
Rotary object dexterous manipulation in hand: a feedback-based method (2013, methodology): “Following concepts from [17] the central idea is to plan the force”
Learning Dexterous In-Hand Manipulation. (2018, unknown): “tactile sensing [60, 34, 35, 36].”
Learning object-level impedance control for robust grasping and dexterous manipulation (2014, background): “Another group uses impedance control to regulate the interaction force implicitly by specifying the impedance of the grasped object [4], [5], [6].”