Own body perception based on visuomotor correlation

This work proposes a plausible approach for a humanoid robot to define its own body parts based on the correlation of two different sensory signals: vision and proprioception. The high correlation between the motions in vision and proprioception informs the robot that the visually attractive object is related to the motor function of its own body. When the robot finds the highly motor-correlated object during head-arm movements, the visuomotor cues such as the body posture and visual features are stored in a visuomotor memory. Then, developmentally, the robot defines the motor-correlated objects as the own-body parts without prior knowledge on the body appearances and kinematics. It is also adaptable to extended body parts such as a grasped tool. The body movements are generated by stochastic motor babbling. The visuomotor memory biases the babbling to keep the own-body parts in sight. This memory-based bias towards the own-body parts helps the robot explore the large head-arm joint space. The acquired visuomotor memory is also used to anticipate the own-body image from the motor commands in advance of the body movement. The proposed approach was evaluated with two humanoid platforms; iCub and James.