Silvio Traversaro

Carlotta Sartore, Mohamed Elobaid, Lorenzo Rapetti et al.

Collaboration is central to human behavior, enabling tasks beyond individual capability. This ability arises from coordinating actions through internal representations of others, a concept known as shared intelligence. Additionally, humans are characterized by physical bodies and cognitive abilities that are optimized in response to their environment, a phenomenon referred to as embodied cognition. Designing humanoid robots that collaborate safely and effectively with people requires unifying these principles. Here we propose an architecture that integrates shared intelligence and embodied cognition to enable robots to physically collaborate with humans, where robot hardware and control are optimized for human metrics, using representations of the human body and motion intelligence. The ultimate goal is to achieve a form of shared embodied intelligence. Specifically, our architecture optimizes robot hardware and physical intelligence parameters with respect to human ergonomic metrics. This is accomplished by modeling human-robot interaction as a function of hardware configurations and embedding human models into the robot's physical intelligence. As a concrete implementation, we present the humanoid robot ergoCub, whose morphology and control have been optimized for collaborative tasks with humans. Our approach provides a framework for designing humanoid robots that prioritize human ergonomics at both the hardware and physical intelligence levels, with applications in industrial and assistive robotics.

Diego Ferigo, Silvio Traversaro, Francesco Romano et al.

The paper presents a software architecture to optimize the process of prototyping and deploying robot controllers that are synthesized using model-based design methodologies. The architecture is composed of a framework and a pipeline. Therefore, the contribution of the paper is twofold. First, we introduce an open-source actor-oriented framework that abstracts the common robotic uses of middlewares, optimizers, and simulators. Using this framework, we then present a pipeline that implements the model-based design methodology. The components of the proposed framework are generic, and they can be interfaced with any tool supporting model-based design. We demonstrate the effectiveness of the approach describing the application of the resulting synchronous dataflow architecture to the design of a balancing controller for the YARP-based humanoid robot iCub. This example exploits the interfacing with Simulink and Simulink Coder.

Prashanth Ramadoss, Giulio Romualdi, Stefano Dafarra et al.

This paper presents a contact-aided inertial-kinematic floating base estimation for humanoid robots considering an evolution of the state and observations over matrix Lie groups. This is achieved through the application of a geometrically meaningful estimator which is characterized by concentrated Gaussian distributions. The configuration of a floating base system like a humanoid robot usually requires the knowledge of an additional six degrees of freedom which describes its base position-and-orientation. This quantity usually cannot be measured and needs to be estimated. A matrix Lie group, encapsulating the position-and-orientation and linear velocity of the base link, feet positions-and-orientations and Inertial Measurement Units' biases, is used to represent the state while relative positions-and-orientations of contact feet from forward kinematics are used as observations. The proposed estimator exhibits fast convergence for large initialization errors owing to choice of uncertainty parametrization. An experimental validation is done on the iCub humanoid platform.

Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte et al.

Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on simplified physical models and hard-coded strategies. Although successful in specific scenarios, this approach requires demanding tuning of parameters and switching logic between specifically-designed controllers for handling more general perturbations. We apply model-free Deep Reinforcement Learning for training a general and robust humanoid push-recovery policy in a simulation environment. Our method targets high-dimensional whole-body humanoid control and is validated on the iCub humanoid. Reward components incorporating expert knowledge on humanoid control enable fast learning of several robust behaviors by the same policy, spanning the entire body. We validate our method with extensive quantitative analyses in simulation, including out-of-sample tasks which demonstrate policy robustness and generalization, both key requirements towards real-world robot deployment.

Tobias Fischer, Wolf Vollprecht, Silvio Traversaro et al.

We argue that it is beneficial to tightly couple the widely-used Robot Operating System with Conda, a cross-platform, language-agnostic package manager, and Jupyter, a web-based interactive computational environment affording scientific computing. We provide new ROS packages for Conda, enabling the installation of ROS alongside data-science and machine-learning packages with ease. Multiple ROS versions (currently ROS1 Melodic and Noetic, as well as ROS2 Foxy and Galactic) can run simultaneously on one machine, with pre-compiled binaries available for Linux, Windows and OSX, and the ARM architecture (e.g. the Raspberry Pi and the new Apple Silicon). To deal with the large size of the ROS ecosystem, we significantly improved the speed of the Conda solver and build system by rewriting the crucial parts in C++. We further contribute a collection of JupyterLab extensions for ROS, including plugins for live plotting, debugging and robot control, as well as tight integration with Zethus, an RViz like visualization tool. Taken together, RoboStack combines the best of the data-science and robotics worlds to help researchers and developers to build custom solutions for their academic and industrial projects.

Joan Kangro, Anand Vazhapilli Sureshbabu, Silvio Traversaro et al.

In modern robotic applications, tactile sensor arrays (i.e., artificial skins) are an emergent solution to determine the locations of contacts between a robot and an external agent. Localizing the point of contact is useful but determining the force applied on the skin provides many additional possibilities. This additional feature usually requires time-consuming calibration procedures to relate the sensor readings to the applied forces. This letter presents a novel device that enables the calibration of tactile sensor arrays in a fast and simple way. The key idea is to design a plenum chamber where the skin is inserted, and then the calibration of the tactile sensors is achieved by relating the air pressure and the sensor readings. This general concept is tested experimentally to calibrate the skin of the iCub robot. The validation of the calibration device is achieved by placing the masses of known weight on the artificial skin and comparing the applied force against the one estimated by the sensors.

Prajval Kumar Murali, Ines Sorrentino, Angelo Rendiniello et al.

Hand-eye calibration of laser profile sensors is the process of extracting the homogeneous transformation between the laser profile sensor frame and the end-effector frame of a robot in order to express the data extracted by the sensor in the robot's global coordinate system. For laser profile scanners this is a challenging procedure, as they provide data only in two dimensions and state-of-the-art calibration procedures require the use of specialised calibration targets. This paper presents a novel method to extract the translation-part of the hand-eye calibration matrix with rotation-part known a priori in a target-agnostic way. Our methodology is applicable to any 2D image or 3D object as a calibration target and can also be performed in situ in the final application. The method is experimentally validated on a real robot-sensor setup with 2D and 3D targets.

Giulio Romualdi, Stefano Dafarra, Yue Hu et al.

This paper contributes towards the benchmarking of control architectures for bipedal robot locomotion. It considers architectures that are based on the Divergent Component of Motion (DCM) and composed of three main layers: trajectory optimization, simplified model control, and whole-body QP control layer. While the first two layers use simplified robot models, the whole-body QP control layer uses a complete robot model to produce either desired positions, velocities, or torques inputs at the joint-level. This paper then compares two implementations of the simplified model control layer, which are tested with position, velocity, and torque control modes for the whole-body QP control layer. In particular, both an instantaneous and a Receding Horizon controller are presented for the simplified model control layer. We show also that one of the proposed architectures allows the humanoid robot iCub to achieve a forward walking velocity of 0.3372 meters per second, which is the highest walking velocity achieved by the iCub robot.

Diego Ferigo, Silvio Traversaro, Giorgio Metta et al.

This paper presents Gym-Ignition, a new framework to create reproducible robotic environments for reinforcement learning research. It interfaces with the new generation of Gazebo, part of the Ignition Robotics suite, which provides three main improvements for reinforcement learning applications compared to the alternatives: 1) the modular architecture enables using the simulator as a C++ library, simplifying the interconnection with external software; 2) multiple physics and rendering engines are supported as plugins, simplifying their selection during the execution; 3) the new distributed simulation capability allows simulating complex scenarios while sharing the load on multiple workers and machines. The core of Gym-Ignition is a component that contains the Ignition Gazebo simulator and exposes a simple interface for its configuration and execution. We provide a Python package that allows developers to create robotic environments simulated in Ignition Gazebo. Environments expose the common OpenAI Gym interface, making them compatible out-of-the-box with third-party frameworks containing reinforcement learning algorithms. Simulations can be executed in both headless and GUI mode, the physics engine can run in accelerated mode, and instances can be parallelized. Furthermore, the Gym-Ignition software architecture provides abstraction of the Robot and the Task, making environments agnostic on the specific runtime. This abstraction allows their execution also in a real-time setting on actual robotic platforms, even if driven by different middlewares.

Giuseppe L'Erario, Luca Fiorio, Gabriele Nava et al.

The paper contributes towards the modeling, identification, and control of model jet engines. We propose a nonlinear, second order model in order to capture the model jet engines governing dynamics. The model structure is identified by applying sparse identification of nonlinear dynamics, and then the parameters of the model are found via gray-box identification procedures. Once the model has been identified, we approached the control of the model jet engine by designing two control laws. The first one is based on the classical Feedback Linearization technique while the second one on the Sliding Mode control. The overall methodology has been verified by modeling, identifying and controlling two model jet engines, i.e. P100-RX and P220-RXi developed by JetCat, which provide a maximum thrust of 100 N and 220 N, respectively.

Nuno Guedelha, Silvio Traversaro, Daniele Pucci

The estimation of the motor torque and friction parameters are crucial for implementing an efficient low level joint torque control. In a set of coupled joints, the actuators torques are mapped to the output joint torques through a coupling matrix, such that the motor torque and friction parameters appear entangled from the point of view of the joints. As a result, their identification is problematic when using the same methodology as for single joints. This paper proposes an identification method with an improved accuracy with respect to classical closed loop methods on coupled joints. The method stands out through the following key points: it is a direct open loop identification; it addresses separately each motor in the coupling; it accounts for the static friction in the actuation elements. The identified parameters should significantly improve the contribution of the feed-forward terms in the low level control of coupled joints with static friction.

Francisco Javier Andrade Chavez, Gabriele Nava, Silvio Traversaro et al.

It is well known that sensors using strain gauges have a potential dependency on temperature. This creates temperature drift in the measurements of six axis force torque sensors (F/T). The temperature drift can be considerable if an experiment is long or the environmental conditions are different from when the calibration of the sensor was performed. Other \textit{in situ} methods disregard the effect of temperature on the sensor measurements. Experiments performed using the humanoid robot platform iCub show that the effect of temperature is relevant. The model based \textit{in situ} calibration of six axis force torque sensors method is extended to perform temperature compensation.

Diego Romeres, Mattia Zorzi, Raffaello Camoriano et al.

This paper discusses online algorithms for inverse dynamics modelling in robotics. Several model classes including rigid body dynamics (RBD) models, data-driven models and semiparametric models (which are a combination of the previous two classes) are placed in a common framework. While model classes used in the literature typically exploit joint velocities and accelerations, which need to be approximated resorting to numerical differentiation schemes, in this paper a new `derivative-free' framework is proposed that does not require this preprocessing step. An extensive experimental study with real data from the right arm of the iCub robot is presented, comparing different model classes and estimation procedures, showing that the proposed `derivative-free' methods outperform existing methodologies.

Yeshasvi Tirupachuri, Silvio Traversaro, Francesco Nori et al.

Articulated objects like doors, drawers, valves, and tools are pervasive in our everyday unstructured dynamic environments. Articulation models describe the joint nature between the different parts of an articulated object. As most of these objects are passive, a robot has to interact with them to infer all the articulation models to understand the object topology. We present a general algorithm to estimate the inherent articulation models by exploiting the momentum of the articulated system along with the interaction wrench while manipulating the object. We validate our approach with experiments in a simulation environment.

Stefano Dafarra, Gabriele Nava, Marie Charbonneau et al.

A common approach to the generation of walking patterns for humanoid robots consists in adopting a layered control architecture. This paper proposes an architecture composed of three nested control loops. The outer loop exploits a robot kinematic model to plan the footstep positions. In the mid layer, a predictive controller generates a Center of Mass trajectory according to the well-known table-cart model. Through a whole-body inverse kinematics algorithm, we can define joint references for position controlled walking. The outcomes of these two loops are then interpreted as inputs of a stack-of-task QP-based torque controller, which represents the inner loop of the presented control architecture. This resulting architecture allows the robot to walk also in torque control, guaranteeing higher level of compliance. Real world experiments have been carried on the humanoid robot iCub.

Francisco Javier Andrade Chavez, Joan Kangro, Silvio Traversaro et al.

In this paper, we present algorithms to estimate external contact forces and joint torques using only skin, i.e. distributed tactile sensors. To deal with gaps between the tactile sensors (taxels), we use interpolation techniques. The application of these interpolation techniques allows us to estimate contact forces and joint torques without the need for expensive force-torque sensors. Validation was performed using the iCub humanoid robot.

Gabriele Nava, Daniele Pucci, Nuno Guedelha et al.

Forthcoming applications concerning humanoid robots may involve physical interaction between the robot and a dynamic environment. In such scenario, classical balancing and walking controllers that neglect the environment dynamics may not be sufficient for achieving a stable robot behavior. This paper presents a modeling and control framework for balancing humanoid robots in contact with a dynamic environment. We first model the robot and environment dynamics, together with the contact constraints. Then, a control strategy for stabilizing the full system is proposed. Theoretical results are verified in simulation with robot iCub balancing on a seesaw.

Daniele Pucci, Silvio Traversaro, Francesco Nori

The paper takes the first step towards the de- velopment of a control framework for underactuated flying humanoid robots. These robots may thus have the capacities of flight, contact locomotion, and manipulation, and benefit from technologies and methods developed for Whole-Body Control and Aerial Manipulation. As in the case of quadrotors, we as- sume that the humanoid robot is powered by four thrust forces. For convenience, these forces are placed at the robot hands and feet. The control objective is defined as the asymptotic stabilization of the robot centroidal momentum. This objective allows us to track a desired trajectory for the robot center of mass and keep small errors between a reference orientation and the robot base frame. Stability and convergence of the robot momentum are shown to be in the sense of Lyapunov. Simulations carried out on a model of the humanoid robot iCub verify the soundness of the proposed approach.

Alessandro Saccon, Silvio Traversaro, Francesco Nori et al.

In the literature on robotics and multibody dynamics, the concept of average angular velocity has received considerable attention in recent years. We address the question of whether the average angular velocity defines an orientation framethat depends only on the current robot configuration and provide a simple algebraic condition to check whether this holds. In the language of geometric mechanics, this condition corresponds to requiring the flatness of the mechanical connection associated to the robotic system. Here, however, we provide both a reinterpretation and a proof of this result accessible to readers with a background in rigid body kinematics and multibody dynamics but not necessarily acquainted with differential geometry, still providing precise links to the geometric mechanics literature. This should help spreading the algebraic condition beyond the scope of geometric mechanics,contributing to a proper utilization and understanding of the concept of average angular velocity.

Francesco Romano, Silvio Traversaro, Daniele Pucci et al.

In this paper, we propose a software abstraction layer to simplify the design and synthesis of whole-body controllers without making any preliminary assumptions on the control law to be implemented. The main advantage of the proposed library is the decoupling of the control software from implementation details, which are related to the robotic platform. Furthermore, the resulting code is more clean and concise than ad-hoc code, as it focuses only on the implementation of the control law. In addition, we present a reference implementation of the abstraction layer together with a Simulink interface to provide support to Model-Driven based development. We also show the implementation of a simple proportional-derivative plus gravity compensation control together with a more complex momentum-based bipedal balance controller.

Silvio Traversaro, Stanislas Brossette, Adrien Escande et al.

This paper presents a new condition, the fully physical consistency for a set of inertial parameters to determine if they can be generated by a physical rigid body. The proposed condition ensure both the positive definiteness and the triangular inequality of 3D inertia matrices as opposed to existing techniques in which the triangular inequality constraint is ignored. This paper presents also a new parametrization that naturally ensures that the inertial parameters are fully physical consistency. The proposed parametrization is exploited to reformulate the inertial identification problem as a manifold optimization problem, that ensures that the identified parameters can always be generated by a physical body. The proposed optimization problem has been validated with a set of experiments on the iCub humanoid robot.

Francisco Javier Andrade Chavez, Silvio Traversaro, Daniele Pucci et al.

This paper proposes and validates an in situ calibration method to calibrate six axis force torque (F/T) sensors once they are mounted on the system. This procedure takes advantage of the knowledge of the model of the robot to generate the expected wrenches of the sensors during some arbitrary motions. It then uses this information to train and validate new calibration matrices, taking into account the calibration matrix obtained with a classical Workbench calibration. The proposed calibration algorithm is validated on the F/T sensors mounted on the iCub humanoid robot legs.

Francesco Romano, Daniele Pucci, Silvio Traversaro et al.

Legged locomotion has received increasing attention from the robotics community. In this respect, contact stability plays a critical role in ensuring that robots maintain balance, and it is a key element for balancing and walking controllers. The Center of Pressure is a contact stability criterion that defines a point that must be kept strictly inside the support polygon in order to ensure postural stability. In this paper, we introduce the concept of the sensitivity of the static center of pressure: roughly speaking, the rate of change of the center of pressure with respect to the system equilibrium configurations. This new concept can be used as an additional criterion to assess the robustness of the contact stability. We show how the sensitivity of the center of pressure can also be used as a metric to assess balancing controllers by considering two state-of-the-art control strategies. The analytical analysis is performed on a simplified model, and validated during balancing tasks on the iCub humanoid robot.

Joan Kangro, Silvio Traversaro, Daniele Pucci et al.

The paper presents a proof of concept to calibrate iCub's skin using vacuum bags. The method's main idea consists in inserting the skin in a vacuum bag, and then decreasing the pressure in the bag to create a uniform pressure distribution on the skin surface. Acquisition and data processing of the bag pressure and sensors' measured capacitance allow us to characterize the relationship between the pressure and the measured capacitance of each sensor. After calibration, integration of the pressure distribution over the skin geometry provides us with the net normal force applied to the skin. Experiments are conducted using the forearm skin of the iCub humanoid robot, and validation results indicate acceptable average errors in force prediction.

Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco et al.

This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. This yields to an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.

Silvio Traversaro, Andrea Del Prete, Riccardo Muradore et al.

Identification of inertial parameters is fundamental for the implementation of torque-based control in humanoids. At the same time, good models of friction and actuator dynamics are critical for the low-level control of joint torques. We propose a novel method to identify inertial, friction and motor parameters in a single procedure. The identification exploits the measurements of the PWM of the DC motors and a 6-axis force/torque sensor mounted inside the kinematic chain. The partial least-square (PLS) method is used to perform the regression. We identified the inertial, friction and motor parameters of the right arm of the iCub humanoid robot. We verified that the identified model can accurately predict the force/torque sensor measurements and the motor voltages. Moreover, we compared the identified parameters against the CAD parameters, in the prediction of the force/torque sensor measurements. Finally, we showed that the estimated model can effectively detect external contacts, comparing it against a tactile-based contact detection. The presented approach offers some advantages with respect to other state-of-the-art methods, because of its completeness (i.e. it identifies inertial, friction and motor parameters) and simplicity (only one data collection, with no particular requirements).

Silvio Traversaro, Daniele Pucci, Francesco Nori

This paper proposes techniques to calibrate six-axis force-torque sensors that can be performed in situ, i.e., without removing the sensor from the hosting system. We assume that the force-torque sensor is attached to a rigid body equipped with an accelerometer. Then, the proposed calibration technique uses the measurements of the accelerometer, but requires neither the knowledge of the inertial parameters nor the orientation of the rigid body. The proposed method exploits the geometry induced by the model between the raw measurements of the sensor and the corresponding force-torque. The validation of the approach is performed by calibrating two six-axis force-torque sensors of the iCub humanoid robot.