Grasping unknown objects in clutter remains challenging due to partial occlusions, self-occlusions, and frequent object interactions during execution. In this paper we present Grasp It Like a Pro 3.0 (GILP 3.0), a lightweight learning-from-demonstration pipeline for closed-loop clutter clearing with unknown objects. The method segments the scene from raw RGB-D point clouds, approximates candidate regions via MVBB decomposition, and predicts grasp pose and interaction wrench from compact MVBB descriptors using two histogram-based gradient-boosted decision-tree regressors trained from a limited number of human demonstrations. Grasp candidates are ranked with execution-oriented feasibility and collision-aware scoring, and the pipeline iteratively reacquires and replans after each attempt to handle object motion and occlusions in clutter. Real-robot experiments on a Franka Emika Panda with Franka Hand in 12 cluttered scenes (47 objects) achieve 95.7% perobject success and a 91.7% scene clearing rate, demonstrating a replicable and data-efficient solution for grasping unknown everyday objects in cluttered environments.

The robotics research community has developed several effective techniques for quadrupedal locomotion. Most of these methods ease the modeling and control problem by assuming a rigid contact between the feet and the terrain. However, in the case of compliant terrain or robots equipped with soft feet, this assumption no longer holds, as the contact point moves and the reaction forces experience a delay. This article presents a novel approach for quadrupedal locomotion in the presence of soft contacts. The control architecture consists of two blocks: 1) upstream, the motion planner (MP) computes a feasible trajectory using model predictive control (MPC) and 2) downstream, the tracking controller (TC) employs hierarchical optimization (HO) to achieve motion tracking. This choice allows the control architecture to employ a large time horizon without heavily compromising the model’s accuracy. For the first time, both blocks consider the contact compliance: in the MP, the classic linear inverted pendulum model is extended by proposing the soft bilinear inverted pendulum (SBIP) model; conversely, the TC is a whole-body controller (WBC) that considers the full dynamics model, including the soft contacts. Simulations with multiple quadrupedal robots demonstrate that the proposed approach enables traversing soft terrains with improved stability and efficiency. Furthermore, the performance benefits of including the compliance in the MP and TC are evaluated. Finally, experiments on the SOLO12 robot walking on soft terrain validate the proposed approach’s effectiveness.

One of the primary challenges in quadrupedal locomotion pertains to the robot’s ability to adapt its gait to the surrounding environment and the desired task. This capability allows quadrupedal robots to select suitable foothold locations and adjust their gait for optimal performance. We address the problem of gait adaptation using trajectory optimization (TO), which takes into account the simplified switched system’s dynamics and optimizes the different phases of motion in which we split the robot’s movement. The robot dynamic model is a single rigid body (SRB) with a rigid contact model and foot positions. We apply contact and friction cone constraints to ensure a physically feasible motion of the real robot. We tackle the optimization using the direct multiple shooting (DMS) method. Leveraging kinematic inversion to map the base and feet positions into joint positions, velocities, and accelerations, we design a controller that combines iterative learning control (ILC) and proportional derivative (PD) feedback control. The iterative controller compensates for the sim-to-real gap, allowing the real robot to learn the task during the execution of the latter. We evaluate the performance of the proposed approach on two different quadrupedal robots and on different terrains.

Mastering rough terrain locomotion is a tough challenge for robots due to its dynamic, unpredictable nature and frequent physical contact. Traditionally, robots rely on carefully planned foot placements to maintain grip and stability. Recent advancements in quadruped robot feet offer diverse shapes and high grip for various terrains. However, control systems and planners often struggle to leverage these varied capabilities, relying instead on simplified foot models e.g., ball-like, flat. The simplified feet models committed to the single shape of the foot can not be used on robots equipped with diverse feet or modern adaptive feet. This work proposes a novel foothold optimization method that efficiently searches for optimal contact points for different foot shapes using a polynomial approximation. The system leverages a Convolutional Neural Network (CNN) trained on simulated data to predict a cost for each candidate foothold. We show that a single neural network can work with different and new foot mechanical designs without retraining the system. We experimentally validate our system on the ANYmal robot using both ball feet and adaptive soft feet, in indoor and outdoor environments, finding that our system improves stability, in terms of pitch and roll angles of the base, with respect to a state-of-the-art method.

Power consumption is a frequently over-looked aspect in robotics, especially in the context of legged robots. Nevertheless, improving the efficiency of walking robots is crucial to overcome the current limitations in runtime. This work proposes a novel method for precisely estimating actuator power consumption based on LSTM neural networks. The performance of this approach is benchmarked against currently employed models and validated on real hardware using certified instruments. The proposed method is integrated into the Isaac Gym framework and utilized to train a power-efficient policy. Instead of optimizing for handcrafted cost functions, such as the often used torque-square minimization, our approach for the first time trains RL policies that minimize the effective energy consumption. Hardware results demonstrate a reduction of approximately 25% in the robot’s total power consumption, with a notable 50% decrease observed for the knee actuator. Additionally, the newly developed policy generates significantly smoother and quieter motions.

Soft robots are deformable, compliant, and underactuated systems. During any task, due to their enormous capability of body deformation, the relative degree may not be well-defined. Since the applicability of the large majority of the state-of-the-art control techniques depends on this property, they frequently encounter singularities. This fact can jeopardize the system’s safety, prevent the correct task execution, or reduce performance. In this work, we investigate the relative degree of dependence for a class of compliant underactuated arms. Our method leverages the well-known strong inertial coupling hypothesis that, if holds, guarantees a constant relative degree of two. We generalize it by introducing coupling conditions where the relative degree is assured to be piecewise constant and greater than two. Relying on the design parameters, we analyze the dynamic evolution of the coupling conditions, which are then used to synthesize a classic input-output feedback controller. We also prove the stability of the closed-loop system. Finally, we validate the efficacy of the approach in simulation and on real hardware using a two and three degrees of freedom underactuated compliant arms with varying stiffness profiles, tasks, and disturbances.

In recent years, robotics has been largely applied to improve the efficiency of logistic processes. Pallets cover a crucial role in the logistic flow, since they represent the main way to store and ship items. When put onto pallets, the items are wrapped with plastic films to protect them and prevent them from falling. Despite being the first and necessary operation for handling the stacked goods, unwrapping—the task of removing the plastic films wrapped around the goods—has not yet been satisfactorily automated. We propose the first robotic solution for autonomous unwrapping of generally shaped pallets, including both homogeneous and heterogeneous pallets. Force and torque measurements are exploited to retrieve information on the collisions between the end-effector and the wrapped items or the plastic film. Based on the contact information, we design a novel reactive planning strategy that makes the unwrapping task effective and robust on pallets with uncertain position or shape. We present the results of an extensive experimental campaign to validate the proposed method. Note to Practitioners—This work is motivated by the fact that unwrapping machines are not yet common on the market. The few commercial examples are usually bulky machines that lack the flexibility to adapt to different and irregularly shaped pallets. Thus, the crucial operation of removing the plastic film around palletized goods is still mainly performed by hand. Blade handling, ladders, and electrostatic shocks are sources of potential injury. We propose a flexible, autonomous unwrapping robot suitable for both cuboid and irregularly shaped pallets. The robot is composed of a robotic arm, a custom cutting end-effector, a vision module, and a suitable planning and control unit. The reduced dimensions allow it to be mounted on a mobile base. The robot has been successfully tested on different pallet configurations. However, extensive testing in real-world scenarios should be carried out to assess both reliability and time efficiency in more realistic working conditions. Moreover, real pallets can reach considerable heights. Thus, a prismatic joint should be integrated to address such cases. Finally, unwrapping in the presence of typical plastic straps and different types of film, e.g., the shrink one, is to be evaluated.

Soft robots can execute tasks with safer interactions. However, control techniques that can effectively exploit the systems' capabilities are still missing. Differential dynamic programming (DDP) has emerged as a promising tool for achieving highly dynamic tasks. But most of the literature deals with applying the DDP to articulated soft robots by using numerical differentiation, in addition to using pure feed-forward control to perform explosive tasks. Further, underactuated compliant robots are known to be difficult to control and the use of DDP-based algorithms to control them is not yet addressed. We propose an efficient DDP-based algorithm for trajectory optimization of articulated soft robots that can optimize the state trajectory, input torques, and stiffness profile. We provide an efficient method to compute the forward dynamics and the analytical derivatives of series elastic actuators (SEA)/variable stiffness actuators (VSA) and underactuated compliant robots. We present a state-feedback controller that uses locally optimal feedback policies obtained from the DDP. We show through simulations and experiments that the use of feedback is crucial in improving the performance and stabilization properties of various tasks. We also show that the proposed method can be used to plan and control underactuated compliant robots with varying degrees of underactuation effectively.

With the improvements in their computational and physical intelligence, robots are now capable of operating in real-world environments. However, manipulation and grasping capabilities are still areas that require significant improvements. To address this, we introduce a new data-driven grasp planning algorithm called Grasp it Like a Pro 2.0. This algorithm utilizes a small number of human demonstrations to teach a robot how to grasp arbitrary objects. By decomposing objects into basic shapes, our algorithm generates candidate grasps that can generalize to different object's geometry. The algorithm selects the grasp to execute based on a selection policy that maximizes a novel grasp quality metric introduced in this article. This metric considers the complex interdependencies between the predicted grasp, the local approximation produced by the basic shape decomposition, and the gripper used. We evaluate our approach against multiple baselines using different grippers and objects. The results demonstrate the effectiveness of our method in generating and selecting high-quality and reliable grasps. With a soft underactuated robotic hand, our algorithm achieves a 94.0% success rate in 150 grasps across 30 different objects. Similarly, with a rigid gripper, it achieves an 85.0% success rate in 80 grasps across 16 different objects.

The attention given to impedance control in recent years does not match a similar focus on the choice of impedance values that the controller should execute. Current methods are hardly general and often compute fixed controller gains relying on the use of expensive sensors. In this article, we address the problem of online impedance planning for Cartesian impedance controllers that do not assign the closed-loop inertia. We propose an optimization-based algorithm that, given the Cartesian inertia, computes the stiffness and damping gains without relying on force/torque measurements and so that the effects of perturbations are less than a maximum acceptable value. By doing so, we increase robot resilience to unexpected external disturbances while guaranteeing performance and robustness. The algorithm provides an analytical solution in the case of impedance-controlled robots with diagonally dominant inertia matrix. Instead, established numerical methods are employed to deal with the more common case of nondiagonally dominant inertia. Our work attempts to create a general impedance planning framework, which needs no additional hardware and is easily applicable to any robotic system. Through experiments on real robots, including a quadruped and a robotic arm, our method is shown to be employable in real time and to lead to satisfactory behaviors.

Operations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments.

Energy consumption is an important issue in robotics. This article deals with the problem of reducing the energy consumption of compliant electro-mechanical systems while performing periodic tasks. After deriving performance indices to quantify the energy consumption of a mechanical system, we propose a method to determine both the optimal compliant actuation parameters, and link trajectories to minimize energy consumption. We show how this problem can be cast in a simpler one where the optimization regards only parameters that define the shape of periodic trajectories to be subsequently determined by using numerical optimization tools. Indeed, in our framework, the optimal stiffness and spring preload can be analytically obtained as a function of the desired link trajectories. We then provide simulations and experimental validations of the obtained results on a two-link compliant manipulator platform which performs a repetitive pick-and-place task. Our experiments show that the use of compliant actuators instead of rigid ones and the optimization of their compliant parameters give rise to an energy saving up to 62% with respect to rigid actuation. Moreover, the simultaneous optimization of the compliant parameters and link trajectories provide an additional energy saving up to 20%.

Embedding elastic elements into legged robots through mechanical design enables highly efficient oscillating patterns that resemble natural gaits. However, current trajectory planning techniques miss the opportunity of taking advantage of these natural motions. This work proposes a locomotion planning method that aims to unify traditional trajectory generation with modal oscillations. Our method utilizes task-space linearized modes for generating center of mass trajectories on the sagittal plane. We then use nonlinear optimization to find the gait timings that match these trajectories within the Divergent Component of Motion planning framework. This way, we can robustly translate the modes-aware centroidal motions into joint coordinates. We validate our approach with promising results and insights through experiments on a compliant quadrupedal robot.

The vast majority of state-of-the-art walking robots employ flat or ball feet for locomotion, presenting limitations while stepping on obstacles, slopes, or unstructured terrain. Moreover, traditional feet for quadrupeds lack sensing systems that are able to provide information about the environment and about the foot interaction with the surroundings. This further diminishes their value. Inspired by our previous work on soft feet for bipedal robots, we present the SoftFoot-Q, an articulated adaptive foot for quadrupeds. This device is conceived to be robust and able to overcome the limitations of currently employed feet. The core idea behind our adaptive foot design is first introduced and validated through a simplified mathematical formulation of the problem. Subsequently, we present the chosen mechanical implementation to attempt overcoming current limitations. The realized prototype of adaptive foot is integrated and tested on the compliantly actuated quadrupedal robot ANYmal together with an ROS-based real-time foot pose reconstruction software. Both extensive field tests and indoor experiments show noticeable performance improvements, in terms of reduced slippage of the robot, with respect to both flat and ball feet.

In this paper, we consider a problem of foothold selection for the quadrupedal robots equipped with compliant adaptive feet. Starting from a model of the foot we compute the quality of the potential footholds considering also kinematic constraints and collisions during evaluation. Since terrain assessment and constraints checking are computationally expensive we applied a Convolutional Neural Network (CNN) to evaluate the potential footholds on the elevation map. We propose an efficient strategy for data clustering and segmentation with CNN. The data for training the neural network is collected off-line but the inference works on-line when the robot walks on rough terrains and allows for efficient adaptation to the terrain and exploitation of the properties of the soft adaptive feet.

Trajectory tracking of flexible link robots is a classical control problem. Historically, the link elasticity was considered as something to be removed. Hence, the control performance was guaranteed by adopting high-gain feedback loops and, possibly, a dynamic compensation with the result to stiffen up the dynamic behavior of the robot. Nowadays, robots are pushed more and more towards a safe physical interaction with a less and less structured environment. Hence, the design and control of the robots moved to an on-purpose introduction of highly compliant elements in the robot bodies, the so-called soft robotics, and towards control approaches that aim to provide the tracking performance without a substantial change in the robot dynamic behavior. Following this approach, we present an iterative learning control that relies mainly on a feedforward component, hence preserves the robot dynamics, for trajectory tracking of a one-link flexible arm. We provide a condition, based on the system dynamics and similar to the Strong Inertially Coupled property, that ensures the applicability of the proposed control method. Finally, we report simulation and experimental tests to validate the theoretical results.

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.

The deployment of robotics in real-world scenarios, which may involve harsh and irregular physical interactions with the environment, such as those when robots operating in a disaster scenario, or the interactions that prosthetic devices may experience, demands hardware, which is physically resilient. The end-effectors, as the main media of interaction, are probably the parts at the highest risk. The capability of robotic hands to survive severe impacts is thus a necessity for the effective deployment of reliable robotic solutions in real-world tasks. Although, this robustness capability has been noted and discussed in the robotics community for long time, the literature does not provide a systematic study nor there is any proposal of standardized test or metric to evaluate hand resilience. In this work, inspired by the works of Charpy and Izod for the systematic definition of resilience and toughness of materials through impact tests, we consider extending the standard test to robot hands. We introduce a resilience evaluation framework, including a precisely defined experimental set-up and test procedure. As an example of application of the procedure, we apply it to experimentally characterize two robot hands, with a similar conceptual architecture but different size and material. From these tests we obtain several insights, including the observation that the dominant factor in hand resilience is their compliance and actuation principle, and that the use, under certain design conditions, of lightweight materials, such as plastic instead of aluminum, may not necessarily reduce the mechanical strength of the overall system.

Real world applications require robots to operate in unstructured environments. This kind of scenarios may lead to unexpected environmental contacts or undesired interactions, which may harm people or impair the robot. Adjusting the behavior of the system through impedance control techniques is an effective solution to these problems. However, selecting an adequate impedance is not a straightforward process. Normally, robot users manually tune the controller gains with trial and error methods. This approach is generally slow and requires practice. Moreover, complex tasks may require different impedance during different phases of the task. This paper introduces an optimization algorithm for online planning of the Cartesian robot impedance to adapt to changes in the task, robot configuration, expected disturbances, external environment and desired performance, without employing any direct force measurements. We provide an analytical solution leveraging the mass-spring-damper behavior that is conferred to the robot body by the Cartesian impedance controller. Stability during gains variation is also guaranteed. The effectiveness of the method is experimentally validated on the quadrupedal robot ANYmal. The variable impedance helps the robot to tackle challenging scenarios like walking on rough terrain and colliding with an obstacle.

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.

Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using high-gain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness.

The deployment of robots to assist in environments hostile for humans during emergency scenarios require robots to demonstrate enhanced physical performance, that includes adequate power, adaptability and robustness to physical interactions and efficient operation. This work presents the design and development of the lower body of the new high performance humanoid WALK-MAN, a robot developed recently to assist in disaster response scenarios. The paper introduces the details of the WALK-MAN lower-body, highlighting the innovative design optimization features considered to maximize the leg performance. Starting from the general lower body specifications the objectives of the design and how they were addressed are introduced, including the selection of the leg kinematics, the arrangement of the actuators and their integration with the leg structure to maximize the range of motion, reduce the leg mass and inertia, and shape the leg mass distribution for better dynamic performance. Physical robustness is ensured with the integration of elastic transmission and impact energy absorbing covers. Experimental walking trials demonstrate the correct operation of the legs while executing a walking gait.

State of the art of hand prosthetics is divided between simple and reliable gripper-like systems and sophisticate hi-tech poly-articular hands which tend to be complex both in their design and for the patient to operate. In this paper, we introduce the idea of decoding different movement intentions of the patient using the dynamic frequency content of the control signals in a natural way. We move a step further showing how this idea can be embedded in the mechanics of an underactuated soft hand by using only passive damping components. In particular we devise a method to design the hand hardware to obtain a given desired motion. This method, that we call of the dynamic synergies, builds on the theory of linear descriptor systems, and is based on the division of the hand movement in a slow and a fast components. We use this method to evolve the design of the Pisa/IIT SoftHand in a prototype prosthesis which, while still having 19 degrees of freedom and just one motor, can move along two different synergistic directions of motion (and combinations of the two), to perform either a pinch or a power grasp. Preliminary experimental results are presented, demonstrating the effectiveness of the proposed design.

In this paper a model-free approach for damping control of Variable Stiffness Actuators is proposed. The idea is to take advantage of the possibility to change the stiffness of the actuators in controlling the damping. The problem of minimizing the terminal energy for a one degree of freedom spring-mass model with controlled stiffness is first considered. The optimal bang-bang control law uses a maximum stiffness when the link gets away from the desired position, i.e. the link velocity is decreasing, and a minimum one when the link is going towards it, i.e. the link velocity is increasing. Based on Lyapunov stability theorems the obtained law has been proved to be stable for a multi-DoF system. Finally, the proposed control law has been tested and validated through experimental tests.

One interesting field of robotics technology is related to the entertainment industry. Performing a musical piece using a robot is a difficult task because music presents many features like melody, rhythm, tone, harmony and so on. Addressing these tasks with a robot is not trivial to implement. Most of approaches which related to this specific field lacks of quality to perform in front of human audience. Implementation of human-like motions can not be properly achieved with a conventional robot actuator. Consequently, we exploit a new type of actuator which simplifies the drawbacks of a conventional one. We used Variable Stiffness Actuator(VSA) instead of using conventional actuator. We can control position, force, and stiffness, simultaneously by using VSA. The most important novel feature is its controllable stiffness. When the stiffness of the actuator is changed, the characteristics of the actuator's response also changes. We implemented the specific stroke which is called “double stroke” using one of variable stiffness actuator. Although the double stroke is known as a special stroke which could be performed by human only, double stroke is successfully implemented by stiffness variation.

The CompAct™ actuator features a clutch mechanism placed in parallel with its passive series elastic transmission element and can therefore benefit from the advantages of both series elastic actuators (SEA) and rigid actuators. The actuator is capable of effectively managing the storage and release of the potential energy of the compliant element by the appropriate control of the clutch subsystem. Controlling the timing of the energy storage/release in the elastic element is exploited for improving motion control in this research. This paper analyses how this class of actuation systems can be used to maximize the link velocity of the joint. The dynamic model of the joint is derived and an optimal control strategy is proposed to identify optimal input reference profiles for the actuator (motor position/velocity and clutch activation timing) which permit the link velocity maximization. The effect of compliance of the joint on the performance of the system is studied and the optimal stiffness is analyzed.

A properly designed elastic actuation can increase the jumping height that a legged robot can reach. In this paper we compare the two most popular conceptual soft actuator designs, parallel elastic (PEA) and series elastic (SEA), in the task of maximizing the jumping height. Such task is translated into an optimal control problem. For a simplified version of the problem an analytical solution is provided, while a problem with more realistic constraints (e.g. the linear torque-speed motor characteristic is taken into account) is stated as a convex optimization problem and numerically solved. The results show that: (i) given the power of the motor there exists an optimal constant stiffness that maximizes the performance for both the SEA and the PEA; (ii) the optimal stiffness depends on the task terminal time, the inertial parameters of the system and the reduction ratio of the motor; (iii) in the condition considered the SEA behaves better than the PEA.

Since the introduction of the first prototypes of robotic end-effectors showing manipulation capabilities, much research focused on the design and control of robot hand and grippers. While many studies focus on enhancing the sensing capabilities and motion agility, a less explored topic is the engineering of the surfaces that enable the hand to contact the object. In this paper we present the prototype of the Velvet Fingers smart gripper, a novel concept of end-effector combining the simple mechanics and control of under-actuated devices together with high manipulation possibilities, usually offered only by dexterous robotic hands. This enhancement is obtained thanks to active surfaces, i.e. engineered contact surfaces able to emulate different levels of friction and to apply tangential thrusts to the contacted object. Through the paper particular attention is dedicated to the mechanical implementation, sense drive and control electronics of the device; some analysis on the control algorithms are reported. Finally, the capabilities of the prototype are showed through preliminary grasps and manipulation experiments.

Industrial grippers are often used for grasping, while in-hand re-orientation and positioning are dealt with by other means. Contact surface engineering has been recently proposed as a possible mean to introduce dexterity in simple grippers, as in the Velvet Fingers smart gripper, a novel concept of end-effector combining simple under-actuated mechanics and high manipulation possibilities, thanks to conveyors which are built in the finger pads. This paper undergoes the modeling and control of the active conveyors of the Velvet Fingers gripper which are rendered able to emulate different levels of friction and to apply tangential thrusts to the contacted objects. Through the paper particular attention is dedicated to the mechanical implementation, sense drive and control electronics of the device. The capabilities of the prototype are showed in some grasping and manipulation experiments.

Recent robotic research recognized the advantages that Variable Impedance Actuators would yield to a new generation of robots, rendering them adapt to many different tasks of everyday life.

The importance of Variable Stiffness Actuators (VSA) in safety and performance of robots has been extensively discussed in the last decade. It has also been shown recently that a VSA brings performance advantages with respect to common actuators. For instance, the solution of the optimal control problem of maximizing the speed of a VSA for impact maximization at a given position with free final time is achieved by applying a control policy that synchronizes stiffness changes with link speed and acceleration. This problem can be regarded as the formalization of the performance of a soccer player's free kick.

This work presents an example of the application of passive impedance control of a variable stiffness manipulator, which shows the actual benefits of variable stiffness in rejecting disturbances without resorting to the closure of a high level feedback loop. In the experiment a 4-DOF manipulator arm, built with the VSA-CubeBot platform, is controlled to hold a pen and draw a circle on an uneven surface. The control is designed calculating joint and stiffness trajectories with a Cartesian approach to the problem, thus designing the optimal workspace stiffness at first. Then, the joint stiffness yielding the closest workspace stiffness is searched for. Experimental results are reported, which agree with the theoretical outcomes, showing that the sub-optimal joints stiffness settings allow the arm to follow the circular trajectory on the uneven surface at best.

The design of grasping and manipulation systems is one of the most investigated topics in recent robotic and automation engineering. It is a process that has to take into account many development possibilities and to face different trade offs, as that between application possibilities and design complexity. In this work we present the design of a novel end-effector that merges the essential mechanics and control simplicity of underactuated devices, together with the high levels of manipulability usually featured in dexterous robotic hands. To obtain this enhancement, the proposed gripper considers the possibility offered by active surfaces, i.e. engineered contact surfaces able to simulate different levels of friction and to apply tangential thrust to the contacted object. The actual dexterity enhancement is evaluated by an analytical manipulability analysis and some examples of in hand manipulations and grasps are taken into account. A mechanical solution is presented, which implements the proposed idea through the adoption of one DoF active surfaces mounted on the fingers. The proposed solution presents a manipulability index one order of magnitude higher than common grippers.

Most of today's robots have rigid structures and actuators requiring complex software control algorithms and sophisticated sensor systems in order to behave in a compliant and safe way adapted to contact with unknown environments and humans. By studying and constructing variable impedance actuators and their control, we contribute to the development of actuation units which can match the intrinsic safety, motion performance and energy efficiency of biological systems and in particular the human. As such, this may lead to a new generation of robots that can co-exist and co-operate with people and get closer to the human manipulation and locomotion performance than is possible with current robots.

We propose a prototype of a Variable Stiffness Actuator (VSA) conceived with low cost as its first goal. This approach was scarcely covered in past literature. Many recent works introduced a large number of actuators with adjustable stiffness, optimized for a wide set of applications. They cover a broad range of design possibilities, but their availability is still limited to small quantities. This work presents the design and implementation of a modular servo-VSA multi-unit system, called VSA-CubeBot. It offers a customizable platform for the realization and test of variable stiffness robotic structures with many degrees of freedom. We present solutions relative to the variable stiffness mechanism, embedded electronics, mechanical and electrical interconnections. Characteristics, both theoretic and experimental, of the single actuator are reported and, finally, five units are interconnected to form a single arm, to give an example of the many possible applications of this modular VSA actuation unit.

The control of a robot's mechanical impedance is attracting increasing attention of the robotics community. Recent research in Robotics has recognized the importance of Variable Stiffness Actuators (VSA) in safety and performance of robots. An important step in using VSA for safety has been to understand the optimality principles that regulate the synchronized variation of stiffness and velocity when moving in the shortest time while limiting possible impact forces (the safe brachistochrone problem). In this paper, we follow a similar program of understanding the use of VSA in performance enhancement, looking at very dynamic tasks where impacts are maximized. To this purpose we address a new optimization problem that consists in choosing the inputs for maximizing the velocity of a link at a given final position, such as, e.g., for maximizing the effect of a hammer impact. We first study the problem with fixed stiffness, and show that, under realistic modeling assumptions, there does exist an optimal linear spring for the given inertia and motor. We then study optimal control of VSA and show that varying the spring stiffness during the execution of the hammering task improves the final performance substantially. The optimal control law is obtained analytically, thus providing insight in the optimality principles underpinning general VSA control. Finally, we show the practicality of our theoretical results with experimental tests.