Exploring Saliency for Learning Sensory-Motor Contingencies in Loco-Manipulation Tasks

The objective of this paper is to propose a framework for a robot to learn multiple Sensory-Motor Contingencies from human demonstrations and reproduce them. Sensory-Motor Contingencies are a concept that describes intelligent behavior of animals and humans in relation to their environment. They hav...

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Veröffentlicht in:Robotics (Basel) Jg. 13; H. 4; S. 58
Hauptverfasser: Stefanini, Elisa, Lentini, Gianluca, Grioli, Giorgio, Catalano, Manuel Giuseppe, Bicchi, Antonio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.04.2024
Schlagworte:
Algorithms
Behavior
Collaboration
Control systems
Decision making
Feedback
human–robot interaction
intelligent robotics
Machine learning
Motion
Perception
Perceptions
Robot arms
Robot control
Robot learning
robot programming and interfaces
Robotics
Robots
sensorimotor learning
Sensors
Italy
ISSN:2218-6581, 2218-6581
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Zusammenfassung:The objective of this paper is to propose a framework for a robot to learn multiple Sensory-Motor Contingencies from human demonstrations and reproduce them. Sensory-Motor Contingencies are a concept that describes intelligent behavior of animals and humans in relation to their environment. They have been used to design control and planning algorithms for robots capable of interacting and adapting autonomously. However, enabling a robot to autonomously develop Sensory-Motor Contingencies is challenging due to the complexity of action and perception signals. This framework leverages tools from Learning from Demonstrations to have the robot memorize various sensory phases and corresponding motor actions through an attention mechanism. This generates a metric in the perception space, used by the robot to determine which sensory-motor memory is contingent to the current context. The robot generalizes the memorized actions to adapt them to the present perception. This process creates a discrete lattice of continuous Sensory-Motor Contingencies that can control a robot in loco-manipulation tasks. Experiments on a 7-dof collaborative robotic arm with a gripper, and on a mobile manipulator demonstrate the functionality and versatility of the framework.
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ISSN:2218-6581
2218-6581
DOI:10.3390/robotics13040058