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Hydrogels based on short peptide molecules are interesting biomaterials with wide present and prospective use in biotechnologies. A well-known possible drawback of these materials can be their limited mechanical performance. In order to overcome this problem, we prepared Fmoc-Phe3self-assembling peptides by a biocatalytic approach, and we reinforced the hydrogel with graphene oxide nanosheets. The formulation here proposed confers to the hydrogel additional physicochemical properties without hampering peptide self-assembly. We investigated in depth the effect of nanocarbon morphology on hydrogel properties (i.e. morphology, viscoelastic properties, stiffness, resistance to an applied stress). In view of further developments towards possible clinical applications, we have preliminarily tested the biocompatibility of the composites. Our results showed that the innovative hydrogel composite formulation based on FmocPhe3 and GO is a biomaterial with improved mechanical properties that appears suitable for the development of biotechnological applications.

This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image.The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.

This paper describes an experimental work conducted in order to estimate the conditions of applicability of Expected Perception (EP) based 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 presents a biologically-inspired perception-action scheme for robots interacting with real-world environments, grounded in the broad hypothesis that, like in humans, perception and action are improved in speed and accuracy by expectation mechanisms: perception crucially involves comparison processes between incoming stimuli and expected perceptions (EPs). In the proposed approach, EPs are built on the basis of previous perceptions, current motor commands, proprioception, and internal models of the robot and the environment. In contrast to pure reactive approaches, 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 validate the viability and effectiveness of the initial hypothesis.

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 paper presents a biologically-inspired perception-action scheme for robots interacting with real-world environments, grounded in the broad hypothesis that, like in humans, perception and action are improved in speed and accuracy by expectation mechanisms: perception crucially involves comparison processes between incoming stimuli and expected perceptions (EPs). In the proposed approach, EPs are built on the basis of previous perceptions, current motor commands, proprioception, and internal models of the robot and the environment. In contrast to pure reactive approaches, 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 validate the viability and effectiveness of the initial hypothesis.

Personal Robotics is widely recognized as a major challenge for current Robotics Research. Robots assisting humans and closely interacting with them have to meet acceptability requirements which in turn leads one to reconsider the concept of autonomy in robotics. This paper presents an abstract analysis of possible levels of semi-autonomy in Personal Robots and illustrates a case-study in which adaptable semi-autonomy is implemented and experimentally validated. Explanation modules, for human-robot communication in the planning phase, and user modeling techniques, that allow the system to adapt to its user's needs and preferences, are proposed as ways to achieve adaptive semi-autonomy

Personal Robotics is widely recognized as a major challenge for current Robotics Research. Robots assisting humans and closely interacting with them have to meet acceptability requirements which in turn leads one to reconsider the concept of autonomy in robotics. This paper presents an abstract analysis of possible levels of semi-autonomy in Personal Robots and illustrates a case-study in which adaptable semi-autonomy is implemented and experimentally validated. Explanation modules, for human-robot communication in the planning phase, and user modeling techniques, that allow the system to adapt to its user's needs and preferences, are proposed as ways to achieve adaptive semi-autonomy