This article presents the design of soft material robots with tunable damping properties. This study derives from the investigation of an under-actuated dynamic approach involving multi-chamber pneumatic systems. The co-design of the mechanical parameters (stiffness and damping) of the system along with the time profile of the input allows to obtain different behaviors using a reduced number of feeding line. In this work we analyze via simulations and experiments several approaches to tune the damping of soft robots. The most effective solution employs a layer of granular material immersed in viscous oil within the chamber wall. This method has been employed to realize bending actuators with a continuous deformation pattern. Finally, we show an application involving a two-fingered gripper fed by a single pneumatic line, which is able to perform pinch and power grasp.

Thanks to the growing interest in soft robotics, hydropneumatics and inflatable system dynamics are attracting renewed attention from the scientific community. Typical fluidic systems are composed of several chambers and require a complex and bulky network of active components for their control. This paper presents a novel approach to fluidic actuation, which consists in the co-design of both the mechanical parameters of the system and of custom input signals, to enable the elicitation of different behaviors of the system with fewer control components. The principle is presented in theory and simulation and then experimentally validated through the application to a case study, an in-pipe inchworm-like robot. It is shown that it is possible to obtain forward and backward movements by modulating a unique input.

In this paper a quasi-static framework for optimally controlling the contact force distribution is experimentally verified with the full-size compliant humanoid robot Walk-Man. The proposed approach is general enough to cope with multi-contact scenarios, i.e. robot-environment interactions occurring on feet and hands, up to the more general case of whole-body loco-manipulation, in which the robot is in contact with the environment also with the internal limbs, with a consequent loss of contact force controllability. Experimental tests were conducted with the Walk-Man robot (i) standing on flat terrain, (ii) standing on uneven terrain and (iii) interacting with the environment with both feet and a hand touching a vertical wall. Moreover, the influence of unmodeled weight on the robot, and the combination with a higher priority Cartesian tasks are shown. Results are presented also in the attached video.

The term whole-body loco-manipulation refers to the case in which a humanoid robot exploits contacts with the environment, both with the end-effectors and with its internal limbs, in order to balance, move and/or manipulate the environment. In such a situation, high degree of redundancy may not be sufficient to completely control the robot movements and/or the forces applied on the environment. This problem is tackled in this work by means of quasi-static analysis tools. The reduction of mobility and manipulability is studied introducing the Fundamental Loco-Manipulation Matrix (FLMM) and its canonical form (cFLMM). Relevant information on the system can be extracted from those, obtaining, e.g., the space of the controllable contact forces, and the controllable displacements of the center of mass. Furthermore, the best contact force distribution able to meet the friction cone constraints is demonstrated to be the solution of a convex optimization problem. The validity of the proposed methods is verified in two numerical examples, where internal contacts affects the controllability of both forces and displacements. Numerical results show that is crucial to consider the correlations between contact forces in order to exert target actions on the environment while coping with friction limits on the whole set of contacts.

In this paper, we present a method to reconstruct the configurations of kinematic trees of rigid bodies not using measurements of relative angles (such as, e.g. rotary encoders at joints) but absolute posture sensors (such as IMUs) along with suitable filter algorithms. We argue that the relatively larger inaccuracies shown by absolute sensors can be compensated by suitable processing, such as a passive complementary filters exploiting the Mahony-Hamel formulation. The proposed method is applicable to systems where measurements of relative angles is not feasible or convenient, or where the joint kinematics are not lower pairs: for example, human body parts or soft robotic devices. In the paper, we make explicit reference to the reconstruction of posture of the compliant, underactuated Pisa/IIT SoftHand. Quantitative comparisons with ground truth data in grasping tests are used to validate the proposed method. The resulting hardware design is mechanically robust, cheap and can be easily adapted to robotic hands with different structures, as well as to sensorizing gloves for studying human grasping strategies.

In this paper, we present a method to plan grasps for soft hands. Considering that soft hands can easily conform to the shape an the object, with preference to certain types of basic geometries and dimensions, we decompose the object into one type of these geometries, particularly into Minimal Volume Bounding Boxes (MVBBs), which are proved to be efficiently graspable by the hand we use. A set of hand poses are then generated using geometric information extracted from such MVBBs. All hand postures are used in a dynamic simulator of the PISA/IIT Soft Hand and put on a test to evaluate if a proposed hand posture leads to a successful grasp. We show, through a set of numerical simulations, that the probability of success of the hand poses generated with the proposed algorithm is very good and represents an evident improvement with respect to our previous results published in [1].

Incorporation of human motor control principles in the motion control architectures for humanoid robots or assistive and prosthesis devices will permit these systems not only to look anthropomorphic and natural at the body ware level but also to generate natural motion profiles resembling those executed by humans during manipulation and locomotion. In this work, relying on the observations on human bimanual coordination, a novel realtime motion control strategy is proposed to regulate the desired Cartesian stiffness profile during the execution of bimanual tasks. The novelty of the proposed control scheme relies on the use of common mode stiffness (CMS) and configuration dependent stiffness (CDS) to regulate the size and directionality of the task space stiffness ellipsoid. Thanks to the CDS control, the proposed scheme is not only proved to be effective in regulating the desired stiffness ellipsoid but also permits to resolve the manipulator redundancy in a natural manner. The effectiveness of the controller is evaluated in an experimental setup in which two cooperating robotic arms are executing an assembly task. Experimental results demonstrate that the proposed dual-arm CDS-CMS controller is effective in tracking the desired stiffness ellipsoids as well as in producing human-like natural motions for the two robotic arms.

This work proposes a representation that comprises both shape and friction, as well as the exploration strategy to gather them from an object. The representation is developed under a common probabilistic framework, particularly it uses a Gaussian Process to approximate the distribution of the friction coefficient over the surface, also represented as a Gaussian Process. The surface model is exploited to compute straight lines (geodesic flows) that guide the exploration. The exploration follows these flows by employing an impedance controller in pursuance of safety, shape accommodation and contact enforcement, while measuring the necessary data to estimate the friction coefficient. The exploratory probes consist of an RGBD camera and an Intrinsic Tactile sensor (ITs) mounted on a robotic arm. Experimental results give evidence for the effectiveness of the algorithm in the friction coefficient gathering and enrichment of the object representation.

In this paper we report on the development of a modular multi-DoF F/T sensor and its use in the implementation of a sensorized object capable of multi-touch detection. The sensor is composed of six 6-axis F/T sensors spatially organized on the faces of a cube. Different calibration methods are presented to directly tackle the coupling phenomena inherent to the spatial organization of the faces and the lightweight construction of the sensor which would have, otherwise, degraded its accuracy. To assess the performances of the calibration methods, a comparison is reported with respect to the measurements obtained with a commercial force/torque sensor considered as ground truth (ATI Delta). Thanks to the modular design and the possibility to cover the sensitive faces with surface patches of different geometry, a variety of sensorized objects with different shapes can be realized. The peculiar feature that all the components of the contact wrench can be measured on each face with high accuracy, renders it a unique tool in the study of grasp force distribution in humans, with envisioned use both in neuroscience investigations and robotic applications.

Humans incorporate and switch between learnt neuromotor strategies while performing complex tasks. Towards this purpose, kinematic redundancy is exploited in order to achieve optimized performance. Inspired by the superior motor skills of humans, in this paper, we investigate a combined free motion and interaction controller in a certain class of robotic manipulation. In this bimodal controller, kinematic degrees of redundancy are adapted according to task-suitable dynamic costs. The proposed algorithm attributes high priority to minimum-effort controller while performing point to point free space movements. Once the robot comes in contact with the environment, the Tele-Impedance, common mode and configuration dependent stiffness (CMS-CDS) controller will replicate the human's estimated endpoint stiffness and measured equilibrium position profiles in the slave robotic arm, in real-time. Results of the proposed controller in contact with the environment are compared with the ones derived from Tele-Impedance implemented using torque based classical Cartesian stiffness control. The minimum-effort and interaction performance achieved highlights the possibility of adopting human-like and sophisticated strategies in humanoid robots or the ones with adequate degrees of redundancy, in order to accomplish tasks in a certain class of robotic manipulation.

In this paper, we propose a general method to achieve a desired grasp compliance acting both on the joint stiffness values and on the hand configuration, also in the presence of restrictions caused by synergistic underactuation. The approach is based on the iterative exploration of the equilibrium manifold of the system and the quasi-static analysis of the governing equations. As a result, the method can cope with large commanded variations of the grasp stiffness with respect to an initial configuration. Two numerical examples are illustrated. In the first one, a simple 2D hand is analyzed so that the obtained results can be easily verified and discussed. In the second one, to show the method at work in a more realistic scenario, we model grasp compliance regulation for a DLR/HIT hand II grasping a ball.

This paper presents a methodology to accurately record human finger postures during grasping. The main contribution consists of a kinematic model of the human hand reconstructed via magnetic resonance imaging of one subject that (i) is fully parameterized and can be adapted to different subjects, and (ii) is amenable to in-vivo joint angle recordings via optical tracking of markers attached to the skin. The principal novelty here is the introduction of a soft-tissue artifact compensation mechanism that can be optimally calibrated in a systematic way. The high-quality data gathered are employed to study the properties of hand postural synergies in humans, for the sake of ongoing neuroscience investigations. These data are analyzed and some comparisons with similar studies are reported. After a meaningful mapping strategy has been devised, these data could be employed to define robotic hand postures suitable to attain effective grasps, or could be used as prior knowledge in lower-dimensional, real-time avatar hand animation.

To adapt to many different objects and tasks, hands are very complex systems with many degrees of freedom (DoFs), sensors, and actuators. In robotics, such complexity comes at the cost of size and weight of the hardware of devices, but it strongly affects also the ease of their programming. A possible approach to simplification consists in coupling some of the DOFs, thus affording a reduction of the number of effective inputs, and eventually leading to more efficient, simpler, and reliable designs. Such coupling can be at the software level, to achieve faster, more intuitive programmability or at the hardware level, through either rigid or compliant physical couplings between joints. Physical coupling between actuators and simplification of control through the reduction of independent inputs is also an often-reported interpretation of human hand movement data, where studies have demonstrated that few “postural synergies” explain most of the variance in hand configurations used to grasp different objects. Together with beneficial simplifications, the reduction of the number of independent inputs to a few coupled motions or “synergies” has also an impact on the ability of the hand to dexterously control grasp forces and in-hand manipulation. This paper aims to develop tools that establish how many synergies should be involved in a grasp to guarantee stability and efficiency, depending on the task and on the hand embodiment. Through the analysis of a quasi-static model, grasp structural properties related to contact force and object motion controllability are defined. Different compliant sources are considered, for a generalization of the discussion. In particular, a compliant model for synergies assumed, referred to as “soft synergies,” is discussed. The controllable internal forces and motions of the grasped object are related to the actuated inputs. This paper investigates to what extent a hand with many joints can exploit postural synergies to control force and motion of the grasped object.

In dexterous grasping, the development of simple but practical hands with reduced number of actuators, designed to perform some manipulation tasks, is both attractive and challenging. To carefully synthesize inter- and intra-finger couplings a rigorous way to establish grasping and manipulation properties of an underactuated hand is of paramount importance. In this paper, we propose a general approach to characterize the structural properties of underactuated hands focusing on their kinematic and force analysis. A complete kinostatic characterization of a given grasp (pure squeeze, spurious squeeze, kinematic grasp displacements and so on) is introduced. The analysis is quasi-static but it is not limited to rigid-body motions, encompassing also essential elastic motions, statically indeterminate configurations, and pre-loaded initial conditions. The introduction of generalized compliance at contacts and in the actuation mechanism is included, as it is an essential feature of safe and dependable modern hands. Efficient algorithms to characterize the system behavior are presented and applied in two different numerical examples.

This work proposes a solution to the grasp synthesis problem, which consist of finding the best hand configuration to grasp a given object for a specific manipulation task while satisfying all the necessary constraints. This problem had been divided into sequential sub-problems, including contact region determination, hand inverse kinematics and force distribution, with the particular constraints of each step tackled independently. This may lead to unnecessary effort, such as when one of the problems has no solution given the output of the previous step as input. To overcome this issue, we present a kinestatic formulation of the grasp synthesis problem that introduces compliance both at the joints and the contacts. This provides a proper framework to synthesize a feasible and prehensile grasp by considering simultaneously the necessary grasping constraints, including contact reachability, object restraint, and force controllability. As a consequence, a solution of the proposed model results in a set of hand configurations that allows to execute the grasp using only a position controller. The approach is illustrated with experiments on a simple planar hand using two fingers and an anthropomorphic robotic hand using three fingers.

Biomorphic structures for robotic manipulation based on tendon-driven mechanisms have been considered in robotic design for several decades, since they provide lightweight end-effectors with high dynamics. Following this trend, many new robot designs have being proposed based on tendon driven systems. Quite noticeably, the most advanced ones include also higher kinematic pairs and unilateral types of constraints. In this paper, we present a general framework for modeling the above class of mechanical systems for robotic manipulation. Such systems, including biomorphically designed devices, consist of articulated limbs with redundant tendinous actuation and unilateral rolling constraints. Methods based on convex analysis are applied to attack this broader class of mechanisms, and are shown to provide a basis for the dynamic control of co-contraction and internal forces that guarantee the correct operation of the system, despite limited friction between contacting surfaces or object fragility. An algorithm is described and tested that integrates a computed torque law, and allows to control tendon actuators to "optimally" comply with the prescribed constraints.