Learning-Based Planner for Unknown Object Dexterous Manipulation Using ANFIS

Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands co...

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Bibliographic Details
Published in:Machines (Basel) 2024-06, Vol.12 (6), p.364
Main Authors: Sheikhsamad, Mohammad, Suárez, Raúl, Rosell, Jan
Format: Article
Language:English
Subjects:
Adaptive systems
Allegro Hand
ANFIS planner
Anthropomorphism
Artificial neural networks
Comparative analysis
dexterous manipulation
End effectors
Fuzzy logic
Machine learning
Material properties
motion planning
Neural networks
Robot learning
Robotics
Robots
Sensors
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Summary:Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands compared to human hands. This paper introduces a data-driven approach that provides a learning-based planner for dexterous manipulation employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) fed by data obtained from an analytical manipulation planner. ANFIS captures the complex relationships between inputs and optimal manipulation parameters. Moreover, during a training phase, it is able to fine-tune itself on the basis of its experiences. The proposed planner enables a robot to interact with objects of various shapes, sizes, and material properties while providing an adaptive solution for increasing robotic dexterity. The planner is validated in a real-world environment, applying an Allegro anthropomorphic robotic hand. A link to a video of the experiment is provided in the paper.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines12060364