Incremental Semiparametric Inverse Dynamics Learning

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

Yarp Based Plugins for Gazebo Simulator

This paper presents a set of plugins for the Gazebo simulator that enables the interoperability between a robot, controlled using the YARP framework, and Gazebo itself. Gazebo is an open-source simulator that can handle different Dynamic Engines (ODE, DART, Bullet, SimBody), backed up by the Open Source Robotics Foundation (OSRF) and supported by a very large community. Since our plugins conform with the YARP layer used on the real robot, applications written for our robots, COMAN and iCub, can be run on the simulator with no changes. Our plugins have two main components: a YARP interface with the same API as the real robot interface, and a Gazebo plugin which handles simulated joints, encoders, IMUs, force/torque sensors and synchronization. The robot model is provided to the simulator by means of an SDF file, which describes all the geometric, dynamic and visual characteristics of a robot. Once the SDF is read from Gazebo, our plugins are loaded and associated with the simulated robot model and the simulated world. Different modules for COMAN and iCub have been developed using Gazebo and our plugins as a testbed: joint impedance control plus gravity compensation, dual arm Cartesian control for manipulation tasks, dynamic walking, etc. This work has been developed as part of a joint effort between three different European Projects “WALKMAN”, “CoDyCo” and “SoftHands” aiming at implementing a common simulation platform to develop and test algorithms for our robotic platforms. This work is available as open-source to all the researchers in the YARP community (https://github.com/robotology/gazebo_yarp_plugins)

A Control Architecture with Online Predictive Planning for Position and Torque Controlled Walking of Humanoid Robots

IROS 2018A 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

Multibody dynamics notation (version 2)

This document provides a revision of the notation originally introduced in [20] for describing kinematics and dynamics quantities of mechanical systems composed by several rigid bodies. Relative to the first edition, this new version includes an expanded section on frame acceleration (Section 5.4), the correction of a few typos, and the change of the fonts used in the notation from single face to bold face.\u3cbr/\u3eThe notation detailed in this document is inspired by the well-known Featherstone notation introduced in [7], also used, with small adaptations, in the Handbook of Robotics [16]. Featherstone’s notation, while being extremely compact and pleasant for the eye, is not fully in accordance with Lie group formalism, with the potential of creating a misunderstanding between the robotics and geometric mechanics communities.\u3cbr/\u3eThe Lie group formalism is well established in the robotics literature [13, 14,\u3cbr/\u3e10]. However, it is less compact than Featherstone’s notation [7], leading to long expressions when several rigid bodies are present as in the case of a complete dynamic model of humanoid or quadruped robots. This report aims, therefore, at getting the best from these two worlds. The notation strives to be compact, precise, and in harmony with Lie Group formalism. The document furthermore introduces a flexible and unambiguous notation\u3cbr/\u3eto describe the Jacobians mapping generalized velocities of an arbitrary frame to Cartesian linear and angular velocities, expressed with respect to a reference frame of choice

On the emergence of whole-body strategies from humanoid robot push-recovery learning

none7Balancing 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.openFerigo D.; Camoriano R.; Viceconte P.M.; Calandriello D.; Traversaro S.; Rosasco L.; Pucci D.Ferigo, D.; Camoriano, R.; Viceconte, P. M.; Calandriello, D.; Traversaro, S.; Rosasco, L.; Pucci, D

On centroidal dynamics and integrability of average angular velocity

\u3cp\u3eIn 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 frame that 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 with 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.\u3c/p\u3

ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots

Human-like trajectory generation and footstep planning represent challenging problems in humanoid robotics. Recently, research in computer graphics investigated machine-learning methods for character animation based on training human-like models directly on motion capture data. Such methods proved effective in virtual environments, mainly focusing on trajectory visualization. This letter presents ADHERENT, a system architecture integrating machine-learning methods used in computer graphics with whole-body control methods employed in robotics to generate and stabilize human-like trajectories for humanoid robots. Leveraging human motion capture locomotion data, ADHERENT yields a general footstep planner, including forward, sideways, and backward walking trajectories that blend smoothly from one to another. Furthermore, at the joint configuration level, ADHERENT computes data-driven whole-body postural reference trajectories coherent with the generated footsteps, thus increasing the human likeness of the resulting robot motion. Extensive validations of the proposed architecture are presented with both simulations and real experiments on the iCub humanoid robot, thus demonstrating ADHERENT to be robust to varying step sizes and walking speeds

Control of humanoid robot motions with impacts: numerical experiments with reference spreading control

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