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A Neural Architecture for Performing Actual and Mentally Simulated Movements During Self-Intended and Observed Bimanual Arm Reaching Movements


Dexterous reaching, pointing, and grasping play a critical role in human interactions with tools and the environment, and it also allows individuals to interact with one another effectively in social settings. Developing robotic systems with mental simulation and imitation learning abilities for such tasks seems a promising way to enhance robot performance as well as to enable interactions with humans in a social context. In spite of important advances in artificial intelligence and smart robotics, current robotic systems lack the flexibility and adaptability that humans so naturally exhibit. Here we present and study a neural architecture that captures some critical visuo-spatial transformations that are required for the cognitive processes of mental simulation and imitation. The results show that our neural model can perform accurate, flexible and robust 3D unimanual and bimanual actual/imagined reaching movements while avoiding extreme joint positions and generating kinematics similar to those observed with humans. In addition, using visuo-spatial transformations, the neural model was able to observe/imitate bimanual arm reaching movements independently of the viewpoint, distance and anthropometry between the demonstrator and imitator. Our model is a first step towards developing a more advanced neurally-inspired hierarchical architecture that integrates mental simulation and sensorimotor processing as it learns to imitate dexterous bimanual arm movements.

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This research was supported by the Office of Naval Research (ONR; N000141310597), USA.

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Correspondence to Rodolphe J. Gentili.

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Gentili, R.J., Oh, H., Huang, DW. et al. A Neural Architecture for Performing Actual and Mentally Simulated Movements During Self-Intended and Observed Bimanual Arm Reaching Movements. Int J of Soc Robotics 7, 371–392 (2015).

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  • Mental simulation
  • Movement imitation
  • Bimanual reaching movements
  • Motor learning
  • Internal models
  • Inverse kinematics
  • Neural network models