Soft robots (SRs) represent one of the most significant recent evolutions in robotics. Designed to embody safe and natural behaviors, they rely on compliant physical structures purposefully designed to embody desirable and sometimes variable impedance characteristics. This article discusses the problem of controlling SRs. We start by observing that most of the standard methods of robotic control-e.g., high-gain robust control, feedback linearization, backstepping, and active impedance control-effectively fight against or even completely cancel the physical dynamics of the system, replacing them with a desired model. This defeats the purpose of introducing physical compliance. After all, what is the point of building soft actuators if we then make them stiff by control?

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses.

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human-robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user-feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.

This paper proposes a teleimpedance controller with tactile feedback for more intuitive control of the Pisa/IIT SoftHand. With the aim to realize a robust, efficient and low-cost hand prosthesis design, the SoftHand is developed based on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. Due to the built-in flexibility of the hand joints, as the SoftHand grasps, it follows a synergistic path while allowing grasping of objects of various shapes using only a single motor. The DC motor of the hand incorporates a novel teleimpedance control in which the user's postural and stiffness synergy references are tracked in real-time. In addition, for intuitive control of the hand, two tactile interfaces are developed. The first interface (mechanotactile) exploits a disturbance observer which estimates the interaction forces in contact with the grasped object. Estimated interaction forces are then converted and applied to the upper arm of the user via a custom made pressure cuff. The second interface employs vibrotactile feedback based on surface irregularities and acceleration signals and is used to provide the user with information about the surface properties of the object as well as detection of object slippage while grasping. Grasp robustness and intuitiveness of hand control were evaluated in two sets of experiments. Results suggest that incorporating the aforementioned haptic feedback strategies, together with user-driven compliance of the hand, facilitate execution of safe and stable grasps, while suggesting that a low-cost, robust hand employing hardware-based synergies might be a good alternative to traditional myoelectric prostheses.

While it is known that softness discrimination relies on both kinesthetic and cutaneous information, relatively little work has been done on the realization of haptic devices replicating the two cues in an integrated and effective way. In this paper, we first discuss the ambiguities that arise in unimodal touch, and provide a simple intuitive explanation in terms of basic contact mechanics. With this as a motivation, we discuss the implementation and control of an integrated device, where a conventional kinesthetic haptic display is combined with a cutaneous softness display. We investigate the effectiveness of the integrated display via a number of psychophysical tests and compare the subjective perception of softness with that obtained by direct touch on physical objects. Results show that the subjects interacting with the integrated haptic display are able to discriminate softness better than with either a purely kinesthetic or a purely cutaneous display.