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).

In recent years, advances and improvements in engineering and robotics have been strengthening interactions between biological science and robotics in the goal of mimicking the complexity of biological systems. In this paper, motor control paradigms inspired by human mechanisms of sensory-motor coordination are applied to a biologically-inspired, purpose-designed robotic platform. The goal was to define and implement a multi-network architecture and to demonstrate that progressive learning of object grasping and manipulation can greatly increase performance of a robotic system in terms of adaptability, flexibility, growing competences and generalization, while preserving the robustness of traditional control. The paper presents the neural approach to sensory-motor coordination and shows preliminary results of the integration with the robotic system by means of simulation tests and experimental trials.

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.

The paper proposes an anticipation mechanism to improve the perception-action loop of robots interacting with real-world environments. According to recent neuroscientific findings, sensory anticipation can increase the effectiveness of perception-action loops and reduce the delays in obtaining the sensory information, especially in case of complex sensory modalities like vision, that affect pure feed-back structures. In the proposed scheme, perception crucially involves comparison processes between incoming stimuli and expected perceptions (EPs), built from previous perceptions, current motor commands, and internal models of the robot and the environment. Background knowledge plays here a helpful role, as it reduces the computational burden of perception and motor coordination tasks in partially structured environments. In the work presented here, an EP mechanism has been applied in the visuo-motor coordination of an anthropomorphic 8 d.o.f. robotic manipulator equipped with a vision system, in order to evaluate the conditions of applicability of the proposed strategy, and to validate the viability and effectiveness of the initial hypothesis.

The development of humanoid robots and their application as personal robots introduce problems related to the interaction of robots with humans and with environments specifically designed for human beings. In tasks where human-robot cooperation is required, compliance of the robot arm can provide a helpful solution, not only to ensure safety, but especially to increase the robot functionality and usability. The concept of functional compliance is illustrated and an experimental validation is reported, where the functionality of an anthropomorphic robot arm is comparatively assessed with and without compliant control. The paper describes the implementation of a compliant control scheme on an anthropomorphic robotic manipulator, illustrates in detail the methodology adopted for the experimental comparative validation and reports the results obtained in the execution of a sample task by a set of potential users, showing the increased performance in the case of compliant control. Indications are also provided on the improvement of acceptability, which is also affected by the enhanced performance.

This paper presents a research work on compliant control of an anthropomorphic robot arm used as a personal robot. In personal applications of robotics, human-robot interaction represents a critical factor for a robot design and introduces strict requirements on its behavior and control, which has to ensure safety and effectiveness. In this work, the problem of controlling the Dexter anthropomorphic robot arm with variable compliance has been investigated, not only to ensure safety in the interaction with humans, but especially to increase the robot functionality in tasks of physical interaction, performed in co-operation with humans. Two different control schemes have been formulated and implemented, to compare their performance experimentally. Both schemes aim at realising a self-controlled compliant behavior without using information from force/torque sensors. The experimental comparison outlines how the performance of the two control systems are inverted with respect to the theoretical considerations, based on the classical control theory, on their accuracy and effectiveness.

The aim of the work is the integration of an anthropomorphic robotic platform, starting from a neurophysiological model of grasping, in order to provide tools for an experimental validation of the model and also to provide new anthropomorphic solutions for robot grasping. The resulting robotic system is composed of an anthropomorphic arm/hand system and visual and tactile sensors. Grasp planning, control and learning have been achieved by a neural approach, inspired by a model of the inter-connections among the brain areas involved in grasping, as formulated by neurophysiologists. After a description of objectives and specifications for the robotic platform, the system is illustrated and experimental results are reported. Finally, the results are discussed as a starting point for current activities, involving the development of novel human-like robotic components and the implementation of more sophisticated learning schemes.

A research work on the interaction control of a cable-actuated robot arm, the Dexter arm, is presented in this paper. Firstly, general considerations on the cable-actuated structures and their application potential are provided and then the Dexter structure peculiarities are accurately analyzed in order to develop proper control solutions. Starting from the analysis of the limitations of the compliance control schemes in Cartesian space and in joint space, previously implemented and experimentally validated on the Dexter arm, a novel control strategy, named impedance-compliance controller, is developed. The proposed control strategy tries to combine the benefits of a compliance control scheme in Cartesian space with the benefits of an impedance control scheme in the operational space by compensating the dynamics of the sole proximal joints. The impedance-compliance controller is capable to achieve accurate smooth motions while guaranteeing functional control of the whole structure, even though a greater computational complexity is required The last section of the paper, dedicated to the experimental results, points out the differences with the previously experimented control solutions anti provides some proofs of the increased Dexter functionality.

The research in the field of advanced robotics is turning its attention more and more to man and his assistance, by developing systems such as service robots, personal robots, and even humanoid robots. Interaction control of such robot manipulators is of paramount importance for an effective execution of manipulation and tracking and, over all, for a safe and effective interaction with the humans. The paper concerns the problem of the control of an 8 degree of freedom anthropomorphic arm named DEXTER, mounted on the mobile platform of the MOVAID System, a robotic system for household personal assistance. The goal is to realize a compliant control for this manipulator in tasks of assistance to disabled and elderly people. On the basis of the control theory applied to industrial robotics, a specific compliant control solution has been developed for the DEXTER peculiar mechanical structure and actuation system, which cause a coupled joint configuration. The solution provides the capability of regulating the robot compliance according to the level of stiffness of the interaction environment. The paper describes the theoretical model of the control system, the implementation on the MOVAID platform and the experimental results in the execution of a set of demonstration tasks.

The field of humanoids robotics is widely recognized as the current challenge for robotics research. Developing humanoids poses fascinating problems in the realization of manipulation capability, which is still one of most complex problem in robotics. The paper, starting from an overview of current activities in the development of humanoid robots, with special focus on manipulation, presents the authors' approach to the design and development of anthropomorphic sensorized hands and of anthropomorphic control and sensory-motor coordination schemes. Current achievements at the Scuola Superiore Sant'Anna and Centro INAIL RTR (Research Centre on Rehabilitation Bioengineering) in the development of a robotic human prosthesis are described, together with preliminary experimental results, as well as in the implementation of biologically-inspired schemes for control and sensory-motor co-ordination, derived from models of well-identified human brain areas.