This paper presents a self-organizing neural network model for visuo-motor coordination of a redundant humanoid robot arm in reaching tasks. The proposed approach is based on a biologically-inspired model which replicates some characteristics of human control: learning occurs through an action-perception cycle and does not requires explicit knowledge of the geometry of the manipulator. The transformation learned is a mapping from spatial movement direction to joint rotation. During learning, the system creates relations between the motor data associated to endogenous movements performed by the robotic arm and the sensory consequences of such motor actions, i.e. the final position and orientation of the end effector. The learnt relations are stored in the neural map structure and are then used, after learning, for generating motor commands aimed at reaching a given point in 3D space. The work is an extension of (E. Guglielmelli, et al.) including the end-effector orientation control. Experimental trials confirmed the system capability to control the end effector position and orientation and also to manage the redundancy of the robotic manipulator in reaching the 3D target point even with additional constraints, such as one or more clamped joints without additional learning phases

This paper presents the application of a neural approach in the control of a 7-DOF robotic head. The inverse kinematics problem is addressed, for the control of the gaze fixation point of two cameras mounted on the robotic head. The proposed approach is based on a biologically-inspired model, which replicates the human brain capability of creating associations between motor and sensory data, by learning. The model is implemented here by self organizing neural maps. During learning, the system creates relations between the motor data associated to endogenous movements performed by the robotic head and the sensory consequences of such motor actions, i.e. the final position of the gaze fixation point. The learnt relations are stored in the neural map structure and are then used, after learning, for generating motor commands aimed at reaching a given fixation point. The approach proposed here allows to solve the inverse kinematics and joint redundancy problems for the ARTS robotic head, with good accuracy and robustness. Experimental trials confirmed the system capability to control the gaze direction and fixation point and also to manage the redundancy of the robotic head in reaching the target fixation point even with additional constraints, such as a clamped joint or two symmetric joint angles (e.g. eye joints).

This paper describes an experimental work conducted in order to estimate the conditions of applicability of expected perception (EP) based on a scheme for robot sensorimotor coordination. The starting hypothesis is that predictions of incoming sensory data can improve sensorymotor coordination respect to pure feedback loops. This implies that the environment presents a level of predictability, as in realistic environments. An implementation of the EP-based scheme has been realized on a platform composed by the Dexter 8-d.o.f. robotic arm and a color camera, for executing a pushing task in a real-world environment. Its performance, where defined as a combination of the error in the trajectory following and the computational effort, has been compared with that of a feedback-based system executing the same task in the same environmental conditions. The results have been put in relation with the degree of environmental predictability, which was controlled in the experimental trials. The experimental results give support and useful insights for analyzing the applicability of the EP-based scheme.