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This manuscript introduces a mobile cobot equipped with a custom-designed high payload arm called RELAX combined with a novel unified multimodal interface that facilitates Human–Robot Collaboration (HRC) tasks requiring high-level interaction forces on a real-world scale. The proposed multimodal framework is capable of combining physical interaction, Ultra Wide-Band (UWB) radio sensing, a Graphical User Interface (GUI), verbal control, and gesture interfaces, combining the benefits of all these different modalities and allowing humans to accurately and efficiently command the RELAX mobile cobot and collaborate with it. The effectiveness of the multimodal interface is evaluated in scenarios where the operator guides RELAX to reach designated locations in the environment while avoiding obstacles and performing high-payload transportation tasks, again in a collaborative fashion. The results demonstrate that a human co-worker can productively complete complex missions and command the RELAX mobile cobot using the proposed multimodal interaction framework.

Legged and wheeled locomotion are two standard methods used by robots to perform navigation. Combining them to create a hybrid legged-wheeled locomotion results in increased speed, agility, and reconfigurability for the robot, allowing it to traverse a multitude of environments. The CENTAURO robot has these advantages, but they are accompanied by a higher-dimensional search space for formulating autonomous economical motion plans, especially in cluttered environments. In this article, we first review our previously presented legged-wheeled footprint reconfiguring global planner. We describe the two incremental prototypes, where the primary goal of the algorithms is to reduce the search space of possible footprints such that plans that expand the robot over the low-lying wide obstacles or narrow into passages can be computed with speed and efficiency. The planner also considers the cost of avoiding obstacles versus negotiating them by expanding over them. The second part of this article presents our new work on local obstacle pushing, which further increases the number of tight scenarios the planner can solve. The goal of the new local push-planner is to place any movable obstacle of unknown mass and inertial properties, obstructing the previously planned trajectory from our global planner, to a location devoid of obstruction. This is done while minimising the distance traveled by the robot, the distance the object is pushed, and its rotation caused by the push. Together, the local and global planners form a major part of the agile reconfigurable navigation suite for the legged-wheeled hybrid CENTAURO robot.

This paper presents Horizon, an open-source framework for trajectory optimization tailored to robotic systems that implements a set of tools to simplify the process of dynamic motion generation. Its user-friendly Python-based API allows designing the most complex robot motions using a simple and intuitive syntax. At the same time, the modular structure of Horizon allows for easy customization on many levels, providing several recipes to handle fixed and floating-base systems, contact switching, variable time nodes, multiple transcriptions, integrators and solvers to guarantee flexibility towards diverse tasks. The proposed framework relies on direct simultaneous methods to transcribe the optimal problem into a nonlinear programming problem that can be solved by state-of-the-art solvers. In particular, it provides several off-the-shelf solvers, as well as two custom-implemented solvers, i.e. GN-SQP and Iterative Linear-Quadratic Regulator. Solutions of optimized problems can be stored for warm-starting, and re-sampled at a different frequency while enforcing dynamic feasibility. The proposed framework is validated through a number of use-case scenarios involving several robotic platforms. Finally, an in-depth analysis of a specific case study is carried out, where a highly dynamic motion (i.e., a twisting jump using the quadruped robot Spot® from BostonDynamics1) is generated, in order to highlight the main features of the framework and demonstrate its capabilities.

Most of the locomotion and contact planners for multi-limbed robots rely on a reduction of the search space to improve the performance of their algorithm. Posture generation plays a fundamental role in these types of planners providing a collision-free, statically stable whole-body posture, projected onto the planned contacts. However, posture generation becomes particularly tedious for complex robots moving in cluttered environments, in which feasibility can be hard to accomplish. In this work, we take advantage of the kinematic structure of a multi-limbed robot to present a posture generator based on hierarchical inverse kinematics and contact force optimization, called the null-space posture generator (NSPG), able to efficiently satisfy the aforementioned requisites in short times. A new configuration of the robot is produced through conservatively altering a given nominal posture exploiting the null-space of the contact manifold, satisfying geometrical and kinetostatics constraints. This is achieved through an adaptive random velocity vector generator that lets the robot explore its workspace. To prove the validity and generality of the proposed method, simulations in multiple scenarios are reported employing different robots: a wheeled-legged quadruped and a biped. Specifically, it is shown that the NSPG is particularly suited in complex cluttered scenarios, in which linear collision avoidance and stability constraints may be inefficient due to the high computational cost. In particular, we show an improvement of performances being our method able to generate twice feasible configurations in the same period. A comparison with previous methods has been carried out collecting the obtained results which highlight the benefits of the NSPG. Finally, experiments with the CENTAURO platform, developed at Istituto Italiano di Tecnologia, are carried out showing the applicability of the proposed method to a real corridor scenario.

This paper presents a novel omnidirectional walking pattern generator for bipedal locomotion combining two structurally different approaches based on the virtual constraints and the preview control theories to generate a flexible gait that can be modified on-line. The proposed strategy synchronizes the displacement of the robot along the two planes of walking: the zero moment point based preview control is responsible for the lateral component of the gait, while the sagittal motion is generated by a more dynamical approach based on virtual constraints. The resulting algorithm is characterized by a low computational complexity and high flexibility, requisite for a successful deployment to humanoid robots operating in real world scenarios. This solution is motivated by observations in biomechanics showing how during a nominal gait the dynamic motion of the human walk is mainly generated along the sagittal plane. We describe the implementation of the algorithm and we detail the strategy chosen to enable omnidirectionality and on-line gait tuning. Finally, we validate our strategy through simulation experiments using the COMAN + platform, an adult size humanoid robot developed at Istituto Italiano di Tecnologia. Finally, the hybrid walking pattern generator is implemented on real hardware, demonstrating promising results: the WPG trajectories results in open-loop stable walking in the absence of external disturbances.

A software and control architecture for a humanoid robot is a complex and large project, which involves a team of developers/researchers to be coordinated and requires many hard design choices. If such project has to be done in a very limited time, i.e., less than 1 year, more constraints are added and concepts, such as modular design, code reusability, and API definition, need to be used as much as possible. In this work, we describe the software architecture developed for Walk-Man, a robot participant at the Darpa Robotics Challenge. The challenge required the robot to execute many different tasks, such as walking, driving a car, and manipulating objects. These tasks need to be solved by robotics specialists in their corresponding research field, such as humanoid walking, motion planning, or object manipulation. The proposed architecture was developed in 10 months, provided boilerplate code for most of the functionalities required to control a humanoid robot and allowed robotics researchers to produce their control modules for DRC tasks in a short time. Additional capabilities of the architecture include firmware and hardware management, mixing of different middlewares, unreliable network management, and operator control station GUI. All the source code related to the architecture and some control modules have been released as open source projects.

Recent studies of human arm movements have suggested that the control of stiffness may be important both for maintaining stability and for achieving differences in movement accuracy. Several studies in the robotic field demonstrated that grasp stiffness is useful for modelling and controlling manipulators but, even though it is accredited that having models of the human finger impedance would be very desirable for the control of anthropomorphous robot's hands, relatively few studies have focused on finger and hand stiffness. To allow the measurement of such entities at the finger level, an appropriate device capable of applying fast force transients while at the same time be able to monitor the finger movements is required. The work presented in this paper is a very detailed report about the design of a new hand exoskeleton system that will be used in our future works to investigate the finger stiffness range in different grasping postures and conditions.

Human neuromotor capabilities guarantee a wide variety of motions. A full understanding of human motion can be beneficial for rehabilitation or performance enhancement purposes, or for its reproduction on artificial systems like robots. This work aims at describing the complexity of human motion in a reduced dimensionality, by means of kinematic Motion Primitives (kMPs). A set of five invariant kMPs are identified for periodic motions, and a set of two kMPs for discrete motions. It is shown how these two sets of kMPs can be combined to synthesize more complex motion as the simultaneous execution of the periodic and the discrete motions. The results reported are an evidence of the theory of Central Pattern Generators (CPG), showing its effects on the kinematics, and are related to what presented in the literature on the Motor Primitives extracted from EMG signals.Experimental tests with the COmpliant huMANoid (COMAN) were performed to show that the kMPs extracted from human subjects can be used to transfer the features of human locomotion to the gait of a robot.