Morphology Specific Stepwise Learning of In-Hand Manipulation With a Four-Fingered Hand

Funabashi, Satoshi; Schmitz, Alexander; Ogasa, Shun; Sugano, Shigeki

doi:10.1109/tii.2019.2893713

Cited by 10 publications

(1 citation statement)

References 22 publications

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“…A novel fingertip is design with the dual ability of tactile sensing and active shape-changing, which is controlled by pneumatic hardware (He et al , 2020). A low-cost learning method based on tactile sensing enables the multifingered robot hand to manipulate objects of varying sizes and shapes (Funabashi et al , 2019). The criteria to quantify grasp robustness and stability is defined taking into account contact force, which can be applied to a wide set of grasps (Pozzi et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

Cooperative distributed model predictive control for robot in-hand manipulation

Chen

Luo²,

Sun³

2023

RIA

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Purpose This study aims to process multi-agent system with kinds of limitations and constraints, and consider the robot in-hand manipulation as a problem of coordination and cooperation of multi-fingered hand. Design/methodology/approach A cooperative distributed model predictive control (MPC) algorithm is proposed to perform robot in-hand manipulation. Findings A cooperative distributed MPC approach is formulated for robot in-hand manipulation problem, which enables address complex limitation and constraint conditions in object motion planning, and realizes tracking trajectory of the object more than tracking position of the object. Originality/value This method to implement the moving object task uses the kinematic parameters without the knowledge of dynamic properties of the object. The cooperative distributed MPC scheme is designed to guarantee the movement of the object to a desired position and trajectory at algorithmic level.

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Section: Introductionmentioning

confidence: 99%