The body schema concept is revisited in the context of embodied cognition, further developing the theory formulated by Marc Jeannerod that the motor system is part of a simulation network related to action, whose function is not only to shape the motor system for preparing an action (either overt or covert), but also to provide the self with information on the feasibility and the meaning of potential actions. The proposed computational formulation is based on a dynamical system approach, which is linked to an extension of the Equilibrium Point Hypothesis, called Passive Motor Paradigm: this dynamical system generates goal-oriented, spatio-temporal, sensorimotor patterns, integrating a direct and inverse internal model in a multi-referential framework. The purpose of such computational model is to operate at the same time as a general synergy formation machinery for planning whole-body actions in humanoid robots and/or for predicting coordinated sensory-motor patterns in human movements. In order to illustrate the computational approach, the integration of simultaneous, even partially conflicting tasks will be analyzed in some detail with regard to postural-focal dynamics, which can be defined as the fusion of a focal task, namely reaching a target with the whole-body, and a postural task, namely maintaining overall stability.

Postural instability is one of the major symptoms of Parkinson’s disease. Here, we assimilated a model of intermittent delay feedback control during quiet standing into postural sway data from healthy young and elderly individuals as well as patients with Parkinson’s disease to elucidate the possible mechanisms of instability. Specifically, we estimated the joint probability distribution of a set of parameters in the model using the Bayesian parameter inference such that the model with the inferred parameters can best-fit sway data for each individual. It was expected that the parameter values for three populations would distribute differently in the parameter space depending on their balance capability. Because the intermittent control model is parameterized by a parameter associated with the degree of intermittency in the control, it can represent not only the intermittent model but also the traditional continuous control model with no intermittency. We showed that the inferred parameter values for the three groups of individuals are classified into two major groups in the parameter space: one represents the intermittent control mostly for healthy people and patients with mild postural symptoms and the other the continuous control mostly for some elderly and patients with severe postural symptoms. The results of this study may be interpreted by postulating that increased postural instability in most Parkinson’s patients and some elderly persons might be characterized as a dynamical disease.

Even in unperturbed upright standing of healthy young adults, body sway involves concurrent oscillations of ankle and hip joints, thus suggesting to using biomechanical models with at least two degrees of freedom, namely, a double inverted pendulum (DIP) framework. However, in a previous study, it was demonstrated that the observed coordinated ankle–hip patterns do not necessarily require the independent active control of the two joints but can be explained by a simpler hybrid control system, with a single active component (intermittent, delayed sensory feedback of the ongoing sway) applied to the ankle joint and a passive component (stiffness control) applied to the hip joint. In particular, the proposed active component was based on the internal representation of a virtual inverted pendulum (VIP) that links the ankle to the current position of the global center of mass (CoM). This hybrid control system, which can also be described as an ankle strategy, is consistent with the known kinematics of the DIP and, in particular, with the anti-phase correlation of the acceleration profiles of the two joints. The purpose of this study is to extend the hybrid control model in order to apply to both the ankle and hip strategy, clarifying as well the rationale of mixed strategies. The extension consists of applying the hybrid control scheme to both joints: a passive stiffness component and an active intermittent component, based on the same feedback signals derived from the common VIP but with independent parameter gains for the two joints. Thus, the hip gains are null in the pure ankle strategy, the ankle gains are null in the pure hip strategy, and both ankle and hip gains are specifically tuned in mixed strategies. The simulation of such an extended model shows that it can reproduce both strategies; moreover, the pure ankle strategy is more robust than the hip strategy, because the range of variation (RoV) of the intermittent control gains is larger in the former case than in the latter, and the pure ankle strategy is also more energy efficient. Generally, the simulations suggest that there is no advantage to employ mixed strategies, except in borderline situations in which the control gains are just outside the RoV that provides stable control for either pure strategy: in this case, a stable mixed strategy can emerge from the combination of two marginally unstable pure strategies.

Several neurodevelopmental disorders and brain injuries in children have been associated with proprioceptive dysfunction that will negatively affect their movement. Unfortunately, there is lack of reliable and objective clinical examination protocols and our current knowledge of how proprioception evolves in typically developing children is still sparse. Using a robotic exoskeleton, we investigated proprioceptive acuity of the wrist in a group of 49 typically developing healthy children (8–15 years), and a group of 40 young adults. Without vision participants performed an ipsilateral wrist joint position matching task that required them to reproduce (match) a previously experienced target position. All three joint degrees-of-freedom of the wrist/hand complex were assessed. Accuracy and precision were evaluated as a measure of proprioceptive acuity. The cross-sectional data indicating the time course of development of acuity were then fitted by four models in order to determine which function best describes developmental changes in proprioception across age. First, the robot-aided assessment proved to be an easy to administer method for objectively measuring proprioceptive acuity in both children and adult populations. Second, proprioceptive acuity continued to develop throughout middle childhood and early adolescence, improving by more than 50% with respect to the youngest group. Adult levels of performance were reached approximately by the age of 12 years. An inverse-root function best described the development of proprioceptive acuity across the age groups. Third, wrist/forearm proprioception is anisotropic across the three DoFs with the Abduction/Adduction exhibiting a higher level of acuity than those of Flexion/extension and Pronation/Supination. This anisotropy did not change across development. Proprioceptive development for the wrist continues well into early adolescence. Our normative data obtained trough this novel robot-aided assessment method provide a basis against which proprioceptive function of pediatric population can be compared. This may aid the design of more effective sensorimotor intervention programs.

The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the software-hardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a brain-inspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction.