In scenarios of Human-Robot Interaction (HRI), it is often assumed that the robot should cooperate with the closest individual or that only one person is present. However, in real-life situations, such as shop floor operations, this assumption may not hold. Thus, it becomes necessary for a robot to recognize a specific target in a crowded environment. To address this problem, we propose a person re-identification module that uses continuous visual adaptation techniques. This module ensures that the robot can seamlessly cooperate with the appropriate individual despite its appearance changes or partial or total occlusions. We used both a laboratory environment and an HRI scenario where the robot followed a person to test our framework. During the test, the targets were asked to change their appearance and disappear from the camera’s field of view to test the module’s ability to handle challenging cases of occlusion and outfit variations. We compared our framework with a state-of-the-art Multi-Object Tracking (MOT) method, and the results showed that our module, shortly named CARPE-ID, accurately tracked each selected target throughout the experiments in all cases except for two cases. In contrast, the MOT had an average of 4 tracking errors for each video.

This work presents the computational design and validation of the Multi-Arm Relocatable Manipulator (MARM), a three-limb robot for space applications, with particular reference to the MIRROR (i.e., the Multi-arm Installation Robot for Readying ORUs and Reflectors) use-case scenario as proposed by the European Space Agency. A holistic computational design and validation pipeline is proposed, with the aim of comparing different limb designs, as well as ensuring that valid limb candidates enable MARM to perform the complex loco-manipulation tasks required. Moti-vated by the task complexity in terms of kinematic reachability, (self)-collision avoidance, contact wrench limits, and motor torque limits affecting Earth experiments, this work leverages on multiple state-of-art planning and control approaches to aid the robot design and validation. These include sampling-based planning on manifolds, non-linear trajectory optimization, and quadratic programs for inverse dynamics computations with constraints. Finally, we present the attained MARM design and conduct preliminary tests for hardware validation through a set of lab experiments.

The big potential of humanoid robots is not restricted to the ground, but these versatile machines can be successfully employed in unconventional scenarios, e.g. space, where contacts are not always present. In these situations, the robot’s limbs can be used to assist or even generate the angular motion of the floating base, as a consequence of the centroidal momentum conservation. In this paper, we propose to combine, in the same whole-body motion control, visual servoing and centroidal momentum conservation. The former dictates a rotation to the floating humanoid to achieve a task in the Cartesian space; the latter is exploited to realize the desired rotation by moving the robot’s articulations. Simulations in a space scenario are carried out using COMAN, a humanoid robot developed at the Istituto Italiano di Tecnologia.

Envisioning the employment of rope-assisted humanoid robots to reduce human intervention for operations in the heights, this preliminary work addresses the modeling and motion planning problems for a rope-assisted bipedal robot. The mathematical features of this system outnumber the ones of typical humanoid robots, including: under-actuation of the floating-base joints, the rope pulling effect and the passive connection between the robot body and the rope master-point. These characteristics render the study of a rope-assisted bipedal robot both fascinating and unexplored, raising motion planning challenges when attempting to plan dynamic suspended maneuvers, as rappelling. To this end, we first introduce a template three-mass model of a bipedal robot connected through passive joints to an extensible rope, which is in turn modeled as a two-mass body. Based on this, a family of optimal control problems is presented to plan rappelling maneuvers.

The kinematic features of a centaur-type humanoid platform, combined with a powerful actuation, enable the experimentation of a variety of agile and dynamic motions. However, the higher number of degrees-of-freedom and the increased weight of the system, compared to the bipedal and quadrupedal counterparts, pose significant research challenges in terms of computational load and real implementation. To this end, this work presents a control architecture to perform agile actions, conceived for torque-controlled platforms, which decouples for computational purposes offline optimal control planning of lower-body primitives, based on a template kinematic model, and online control of the upper-body motion to maintain balance. Three stabilizing strategies are presented, whose performance is compared in two types of simulated jumps, while experimental validation is performed on a half-squat jump using the CENTAURO robot.

This paper presents a framework providing a full pipeline to execute a complex physical interaction behaviour of a humanoid bipedal robot, both from a theoretical and a practical standpoint. Building from a multi-contact control architecture that combines contact planning and reactive force distribution capabilities, the main contribution of this work consists in the integration of a sample-based motion planning layer conceived for transitioning movements where obstacle and self-collisions avoidance is involved. To plan these motions we use Rapidly Exploring Random Tree (RRT) projected on the contacts manifold and validated through the Centroidal Statics (CS) model, to ensure static balance on non-coplanar surfaces. Finally, we successfully validate the presented planning and control architecture on the humanoid robot COMAN+ performing a wall-plank task.

This work introduces a framework for the Cartesian control of multi-legged, highly redundant robots. The proposed framework allows the untrained user to perform complex motion tasks with robotics platforms by leveraging a simple, auto-generated ROS-based interface. Contrary to other motion control frameworks (e.g. ROS MoveIt!), we focus on the execution of Cartesian trajectories that are specified online, rather than planned in advance, as it is the case, for instance, in tele-operation and locomotion tasks. Moreover, we address the problem of generating such motions within a hard real-time (RT) control loop. Finally, we demonstrate the capabilities of our framework both on the COMAN + humanoid robot, and on the hybrid wheeled-legged quadruped CENTAURO.

In this paper, we look at the challenge of human robot interaction in locomotion. We consider a hand-in-hand interaction scenario where a human compliantly interacts with the upper-body of an impedance controlled humanoid. By exploring the velocity transmission of the robot arms, and the interaction in terms of robot arms manipulation quality evaluated through the monitoring of their manipulability the proposed method derives suitable reactive steps in appropriate directions to ensure that the robot manipulation ability is maintained with the robot arms providing high capacity of motion along the different directions. The proposed approach can be combined with different walking pattern generators and is not tailored to a specific one used in this work. The results of the proposed method are experimentally validated on the COMAN + humanoid robot showing the efficacy of the method to generate reactive stepping driven by the interaction and manipulation motion of the human operator. Besides, the work also provides a real-time software architecture to control humanoid COMAN+, but it is also flexible to be used for the control of other robot platforms.

In this paper we propose a novel hybrid walking pattern generator which combines results from the virtual constraints and the preview control theories for bipedal locomotion. This choice is motivated by findings in biomechanics that show how the dynamic motion of the human walk is mainly generated by the sagittal component of the stepping. Thus, we choose the conservative preview control to generate the lateral motion while we pick a more dynamical framework such as the virtual constraints for the sagittal motion. We investigate how the time-dependent preview control and the time-independent virtual constraints approach can be integrated together in a humanoid locomotion and finally we show promising results on COMAN+, the new humanoid robot from Istituto Italiano di Tecnologia.

This work presents a new attitude balancing strategy implemented and validated on a quadrupedal robot equipped with a custom hybrid wheel-legged mobility system. The proposed method uses an inverse kinematics solution scheme based on Quadratic Programming optimization to generate full body motions that ensure the desired balancing performances. The strategy generates a compliant behaviour to cope with the applied external forces resulting in a stable and smooth reaction response. Furthermore, the method takes advantage of the robot hybrid wheeled-legged mobility system to provide new motion capabilities and balancing reactions as it will be shown through the paper. Extensive simulation studies on the Centauro robot are presented. Results show the efficiency of the propose method demonstrating significant contribution in the rejection of the applied external disturbances.

In this work we introduced a prioritized Cartesian impedance control under the framework of the Quadratic Programming (QP) optimization. In particular, we present a formulation which is simpler than full inverse dynamics, avoids any matrix pseudo-inversion, inverse kinematics computation and considers strict priorities among tasks. Our formulation is based on QP optimization permitting to take into account also explicit inequality constraints. We compare in simulation the tracking results obtained with a classical algebraic implementation against those derived from the proposed QP implementation taking into account joint torque limits. We consider the classical Cartesian impedance controller and a simplified version, also known as Virtual Model Control. Finally the proposed method was implemented and validated on a humanoid upper-body torque controlled robot. Experimental trials involving various physical interaction conditions were executed to demonstrate the performance of the proposed method.

Robotics teleoperation has been extensively studied and considered in the past in several task scenarios where direct human intervention is not possible due to the hazardous environments. In such applications, both communication degradation and reduced perception of the remote environment are practical issues that can challenge the human operator while controlling the robot and attempting to physically interact within the remote workspace. To address this challenge, we introduce a novel shared-autonomy Tele-Interaction control approach that blends the motion commands from the pilot (master side) with locally (slave side) executed autonomous motion and impedance modulators. This enables a remote robot to handle and autonomously avoid physical obstacles during manoeuvring, reduce interaction forces during contacts, and finally accommodate different payload conditions while at the same time operating with a “default” low impedance setting. We implemented and experimentally validated the proposed method both on simulation and on a real robot platform called CENTAURO. A series of tasks, such as maneuvering through the physical constraints of the remote environment in an autonomous manner, pushing and lifting heavy objects with autonomous impedance regulation and colliding with the rigid geometry of the remote environment were executed. The obtained results demonstrate the effectiveness of the shared-autonomy control principles that eventually aim to reduce the level of attention and stress of human pilot while manoeuvring the slave robot, and at the same time to enhance the robustness of the robot during physical interactions even if accidentally occurred.

The present work addresses the generation of a walking gait with automatic footstep placement for a quadrupedal robot, within a Linear Model Predictive Control framework. Existing work has shown how this is only possible within a non-convex programming framework, finding a solution of which is well-known to be very hard. We propose a way to formulate the joint optimization problem as an approximate QP with linear constraints, whose global optimum can be quickly found with off-the-shelf solvers. More specifically, this is done by introducing auxiliary states and control inputs, each of which is subject to linear constraints that are inspired from the literature on bipedal locomotion. Finally, we validate our method on the CENTAURO robot, a hybrid wheeled-legged quadruped with a humanoid upper-body.

This paper proposes the implementation of an impedance controller on the ankle level of COMAN+, a robot with parallel kinematics ankles actuated by a dual four-bar mechanism. The main contribution of the work is a realization of said control scheme that grants a less abrupt and safer robot response in case of system failures, that would cause the local joint torque controllers to lose their torque reference inputs. In particular, we propose a semi-centralized impedance control implementation which eliminates the instability of the pure joint torque control schemes used in the classical fully centralized methods when torque reference interruptions occur. Finally, we present experimental results, proving the effectiveness of our method and demonstrating how it ensures a safer behaviour compared to a fully centralized impedance control implementation when the communication to the ankle joints is interrupted. This paper is a follow-up work of [1], which presented and analyzed the parallel kinematics ankles.

A fundamental aspect of controlling humanoid robots lies in the capability to exploit the whole body to perform tasks. This work introduces a novel whole body control library called OpenSoT. OpenSoT is combined with joint impedance control to create a framework that can effectively generate complex whole body motion behaviors for humanoids according to the needs of the interaction level of the tasks. OpenSoT gives an easy way to implement tasks, constraints, bounds and solvers by providing common interfaces. We present the mathematical foundation of the library and validate it on the compliant humanoid robot COMAN to execute multiple motion tasks under a number of constraints. The framework is able to solve hierarchies of tasks of arbitrary complexity in a robust and reliable way.