The Robot Operating System (ROS) has become the de facto standard middleware in robotics, widely adopted across domains ranging from education to industrial applications. The RoboStack distribution, a conda-based packaging system for ROS, has extended ROS's accessibility by facilitating installation across all major operating systems and architectures, integrating seamlessly with scientific tools such as PyTorch and Open3D. This paper presents ROS2WASM, a novel integration of RoboStack with WebAssembly, enabling the execution of ROS 2 and its associated software directly within web browsers, without requiring local installations. ROS2WASM significantly enhances the reproducibility and shareability of research, lowers barriers to robotics education, and leverages WebAssembly's robust security framework to protect against malicious code. We detail our methodology for cross-compiling ROS 2 packages into WebAssembly, the development of a specialized middleware for ROS 2 communication within browsers, and the implementation of www.ros2wasm.dev, a web platform enabling users to interact with ROS 2 environments. Additionally, we extend support to the Robotics Toolbox for Python and adapt its Swift simulator for browser compatibility. Our work paves the way for unprecedented accessibility in robotics, offering scalable, secure, and reproducible environments that have the potential to transform educational and research paradigms.

The optimization of hardware and control of humanoid robots for multiple tasks is still an open challenge due to the competing objectives of different behaviors and the complexity of considering control architectures at the design level of a humanoid robot. In this work, we propose a unified multi-objective optimization framework that jointly optimizes both hardware and hierarchical control architectures of a humanoid robot to enhance performance in multiple tasks. Our method employs a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify optimal robot morphology and control parameters while balancing trade-offs between diverse task requirements. By leveraging genetic algorithms, we enable the integration of discrete search spaces while overcoming the local minima limitations associated with classical nonlinear optimization techniques. Furthermore, the proposed approach directly incorporates the simulation results, ensuring that hardware optimization is performed considering the system dynamics. We validate our approach by optimizing a humanoid robot for two distinct tasks: walking and payload lifting, leveraging MuJoCo to evaluate the task performances. The proposed framework successfully identifies Pareto-optimal tradeoffs, providing a set of design solutions adaptable to different operational requirements.

This paper presents a Non-Linear Model Predictive Controller for humanoid robot locomotion with online step adjustment capabilities. The proposed controller considers the Centroidal Dynamics of the system to compute the desired contact forces and torques and contact locations. Differently from bipedal walking architectures based on simplified models, the presented approach considers the reduced centroidal model, thus allowing the robot to perform highly dynamic movements while keeping the control problem still treatable online. We show that the proposed controller can automatically adjust the contact location both in single and double support phases. The overall approach is then tested with a simulation of one-leg and two-leg systems performing jumping and running tasks, respectively. We finally validate the proposed controller on the position-controlled Humanoid Robot iCub. Results show that the proposed strategy prevents the robot from falling while walking and pushed with external forces up to 40 Newton for 1 second applied at the robot arm.

Extended Kalman filtering is a common approach to achieve floating base estimation of a humanoid robot. These filters rely on measurements from an Inertial Measurement Unit (IMU) and relative forward kinematics for estimating the base position-and-orientation and its linear velocity along with the augmented states of feet position-and-orientation. We refer to such filters as flat-foot filters. However, the availability of only partial measurements often poses the question of consistency in the filter design. In this paper, we perform an experimental comparison of state-of-the-art flat-foot filters based on the representation choice of state, observation, matrix Lie group error and system dynamics evaluated for filter consistency and trajectory errors. The comparison is performed over simulated and real-world experiments conducted on the iCub humanoid platform. It is observed that filters on Lie groups that exploit properties of invariant filtering tend to perform better as consistent estimators while discrete-time filters in general provide higher accuracy along observable directions.

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.

The paper introduce a robotics software control architecture suitable for the development of complete robotic industrial applications. The architecture fuse the state-of-the-art software technologies in a single standalone platform to provide an easy integration between all the software components necessary to control a robotic application, i.e. PLC logic, robot motion program. The main goal is to provide an architecture as much as possible hardware agnostic to develop easily portable software.

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 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 in situ calibration of six axis force torque sensors method is extended to perform temperature compensation.

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.

This paper presents a proof of concept to calibrate iCub's skin using vacuum bags. The main idea of the method consists of inserting the skin, made of distributed capacitive sensors, in a vacuum bag and then decreasing the pressure in the bag to create a uniform pressure distribution on the skin surface. The capacitance changes of each sensor were then related to the applied differential pressure using a least square fitting with a fifth order polynomial model. After calibration, integration of the pressure distribution over the skin geometry provides us with the net normal force applied to the skin. Experiments were conducted using the forearm skin of the iCub humanoid robot and the calibration results were validated using standard weights. The validation results indicate acceptable average errors in force prediction.

This work explores the stabilization of desired dynamic motion tasks involving hard impacts at non-negligible speed for humanoid robots. To this end, a so-called reference spreading hybrid control law is designed showing promising results in simulation. The simulations are performed employing a dynamical model of an existing humanoid robot and impacts are assumed to be inelastic. The desired motion task consists of having the robot balancing on one foot while repeatedly making and breaking contact with a wall by means of one hand. The simulation results illustrate that the considered controller is suited to control humanoid robot motions with impacts.

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. The result is 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.

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.

Classically robot force control passes through joint torques measurement or estimation. Within this context, classical torque sensing technologies rely on current sensing on motor windings and on torsion sensing on motor shaft. An alternative approach was recently proposed in [1] and combines whole-body distributed 6-axis force/torque (F/T) sensors, gyroscopes, accelerometers and tactile sensors (i.e. artificial skin). A further advantage of this method is that it simultaneously estimates (internal) joint torques and (external) contact forces with no need of joint redesign. As a drawback, the method relies on a model of the robot dynamics, as it consists on reordering the classical recursive Newton-Euler algorithm (RNEA). In this paper we consider the problem of the parametric identification of the robot dynamic model from embedded F/T sensors. We extend recent results on parametric identification [2] by considering an arbitrary reordering of the classical RNEA. The theoretical framework is validated on the iCub humanoid, which is equipped with both 6-axis F/T sensors and joint torque sensors. We estimated the system inertial parameters using only one F/T sensor. We used the obtained parameters to estimate the joint torques (as proposed in [1]) and compared the results with direct joint torque measurements, used in this context only as a ground truth.

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.

Given an articulated rigid body, we define the problem of estimating its dynamics as the problem of computing all the forces and accelerations acting on the bodies which constitute the articulated system. Similarly, we define the state estimation problem as the problem of computing the system positions and velocities. In the present paper we propose a framework for simultaneous state and dynamics estimation. The estimation is framed in a Bayesian framework and a suitable Bayesian prior is defined to guarantee the physical consistency of the obtained estimation. The Bayesian posterior makes use of all available measurements which include encoders, gyroscopes, accelerometers, force and torque sensors. The proposed theoretical framework is validated both on simulation and on the iCub humanoid. The software that implements the theoretical framework is realised with an open-source license.