Human–Robot Interaction (HRI) has emerged as a pivotal domain in robotics, centering on the interplay and collaboration between humans and robots to achieve complex tasks. Effective communication is a cornerstone of successful HRI, facilitating the exchange of critical information essential for joint decision-making and task execution. This article explores the intricate dynamics of collaborative communication in physical HRI (pHRI), specifically focusing on non-verbal cues. Within HRI, we assert that collaboration fundamentally hinges on communication, wherein agents share information to achieve common objectives. Information theory provides a rigorous mathematical framework for quantifying the flow of information within communicating agents. It serves as a unifying framework for evaluating the dynamic interplay of various communication channels in pHRI. This study introduces a non-parametric approach based on information entropy to assess communication between agents in pHRI scenarios and detect important behaviors, such as information flow, leadership, and coupling. Through a comprehensive experimental setup involving collaborative catching tasks, we demonstrate the versatility and applicability of the proposed methodology.

In this paper, we propose a motion, stiffness, and force learning framework based on an extended dynamic movement primitive (DMP) and quadratic programming (QP) optimization. The objective is to learn kinematic and dynamic operational parameters from a one-shot human demonstration, through measurement and estimation of the motion, 3-dimensional (3-D) endpoint stiffness, and applied forces of the human arm during manipulation tasks. To this end, first, the framework features an extended DMP to model the motion, stiffness, and force variations in Cartesian space and 2-D sphere manifold. Second, to account for collected errors and human-robot operation gaps, a QP optimization is applied to fine-tune the desired position of the controller. Finally, we validate the framework through two experiments in real scenarios on the Franka Emika Panda robot. Experimental results show that the robot can not only inherit the variation laws of motion, stiffness, and force in the human demonstration, but also exhibit certain generalization capabilities to other situations. The framework provides a reference for robots learning multiple skills via a one-shot human demonstration, which finds great potential application in human-robot cooperation, contact-rich scenarios, and skillful operations, where the motion, stiffness, and applied forces need to be considered simultaneously. Note to Practitioners—Fast programming in robotics through skill transfer plays a critical role in next-generation robots entering ordinary people’s lives. Existing research focuses more on skill learning at the kinematic level and lacks on the dynamic level, such as stiffness and contact force. The goal of this paper is to propose a novel framework for robots learning of motion, stiffness, and force variations from a one-shot human demonstration, simultaneously. To this end, a Riemannian-based DMP method is employed to model the variation laws of motion, stiffness, and force in Cartesian space and 2-D sphere manifold, respectively. In this way, the learning module needs to be run only once, and the patterns can also be generalized to other targets without repeated robot teaching and additional time-consuming processes. To accurately reproduce the learned skills, a human-like motion/stiffness/force controller combined with QP optimization is investigated. In this paper, rather than identifying real environmental parameters, we directly use interacted forces during the human demonstration to represent environmental effects and employ QP to update the desired position in a limited range to account for collected errors and human-robot operation gaps. Experiments on button pressing and polishing tasks by the Panda robot have achieved very good results. The work of this paper lays a foundation for multiple skills learning from human demonstration (LfHD).

To enhance robots’ applicability in real-world scenarios, it is essential to establish a complex and multi-tasking behaviour, inspired by human nature. To this purpose, from a hardware perspective, a high number of degrees of freedom is necessary, as is the case for humanoids and collaborative mobile manipulators. From a software standpoint instead, complex hierarchical strategies are often used to define a set of behaviours that the robot should reflect in strict hierarchical order. Their main issue however, is related to the lack of continuity when their stack of tasks is changed. Existing works that address this issue clearly present a trade-off between optimality assurance during transition and computational costs. Here, we employ a distributed processing approach that enables not only the minimization of computational costs, but also continuous optimality and constraints feasibility even under sharp transitions. The approach is tested during three task transitions, for different tasks such as constrained trajectory tracking, obstacle avoidance, and postural optimization. Two mobile manipulators are used, each having 10 DoF, and the results confirm the smoothness of the generated solutions.

To enable a comprehensive human-robot interaction, it is essential to refer to human-human collaboration and decode complex non-verbal communication aspects that are essential for adaptive decision-making and task success. Indeed, for a robust collaboration, it is useful to understand the intricacies and complexities of human communication during human-human interaction and to compare it with the human-robot interaction case. We study this communication exchange and information flow by evaluating the leader/follower behavior during physical interaction with different agents and different control types, focusing on non-verbal cues, to identify collaborative or competitive attitudes. To achieve this, we consider a dynamic task of collaboratively catching a falling object, which, by its nature, favors non-verbal communication channels. Multiple subjects performed the same task with different collaborative agents (i.e., human and robot) and with different control modalities, to evaluate the leadership roles and their implication on task success (successfully catching the object while minimizing impact forces). We analyze how the impact force minimization induced by the velocity matching optimal planner affects the catching success rate. The information flow is analyzed, and the leadership roles are identified. Further qualitative data is gathered from questionnaires and compared with respect to the analytic results.

This paper proposes a combined optimization and learning method for impact-friendly, non-prehensile catching of objects at non-zero velocity. Through a constrained Quadratic Programming problem, the method generates optimal trajectories up to the contact point between the robot and the object to minimize their relative velocity and reduce the impact forces. Next, the generated trajectories are updated by Kernelized Movement Primitives, which are based on human catching demonstrations to ensure a smooth transition around the catching point. In addition, the learned human variable stiffness (HVS) is sent to the robot's Cartesian impedance controller to absorb the post-impact forces and stabilize the catching position. Three experiments are conducted to compare our method with and without HVS against a fixed-position impedance controller (FP-IC). The results showed that the proposed methods outperform the FP-IC while adding HVS yields better results for absorbing the post-impact forces.

Impacts and other non-smooth behaviors are usually unwanted in robotic applications. However, several industrial tasks such as deburring, removing excess material, and assembling/fitting, involve impacts between objects, which can benefit from robotic automation due to the risks posed to human health. Towards this objective, in this paper, we propose a method for optimal impact planning and pre-configuration for torque-controlled robots. We thus employ a well-known impulsive contact model to plan the impact force and create a hierarchical quadratic programming based controller capable of minimizing the robot's peak torques by reconfiguring its joints optimally, before the impact occurs. The results obtained from multiple experiments during an industrial deburring task are discussed. Using a 7-DoF manipulator, we show consistent results, both in terms of accuracy of the impact force tracking with respect to the desired forces, and in terms of peak torques reduction and uniform torques distribution.

The recognition of actions performed by humans and the anticipation of their intentions are important enablers to yield sociable and successful collaboration in human-robot teams. Meanwhile, robots should have the capacity to deal with multiple objectives and constraints, arising from the collaborative task or the human. In this regard, we propose vision techniques to perform human action recognition and image classification, which are integrated into an Augmented Hierarchical Quadratic Programming (AHQP) scheme to hierarchically optimize the robot's reactive behavior and human ergonomics. The proposed framework allows one to intuitively command the robot in space while a task is being executed. The experiments confirm increased human ergonomics and usability, which are fundamental parameters for reducing musculoskeletal diseases and increasing trust in automation.

Today’s robots are expected to fulfill different requirements originated from executing complex tasks in uncertain environments, often in collaboration with humans. To deal with this type of multi-objective control problem, hierarchical least-square optimization techniques are often employed, defining multiple tasks as objective functions, listed in hierarchical manner. The solution to the Inverse Kinematics problem requires to plan and constantly update the Cartesian trajectories. However, we propose an extension to the classical Hierarchical Quadratic Programming formulation, that allows to optimally generate these trajectories at control level. This is achieved by augmenting the optimization variable, to include the Cartesian reference and allow for the formulation of an adaptive compliance controller, which retains an impedancelike behaviour under external disturbances, while switching to an admittance-like behavior when collaborating with a human. The effectiveness of this approach is tested using a 7-DoF Franka Emika Panda manipulator in three different collaborative scenarios.

Prolonged remote tele-locomanipulation of multi degrees-of-freedom mobile manipulators requires a compromise between the system’s performance and the operator’s ergonomics. Neglecting this demand can significantly affect either the task completion or the level of comfort to achieve it. However, the simultaneous consideration of these key factors has received less attention in the literature. To respond to this demand, in this work, we introduce a new teleoperation setup, which integrates the features of an ergonomic and a highly maneuverable interface into a unified solution. The ergonomic part of the interface implements a 3D mouse-like functionality, enabling the execution of long navigation tasks for the floating base. The highly manoeuvrable interface instead, enables the operator to perform dynamic or more precise manipulation by moving his/her arm in space. The locomotion and manipulation modes of the follower robot are controlled separately, which can be easily and seamlessly switched by the operator by pressing a button at any moment. Furthermore, due to the follower manipulator’s redundancy, this robot is controlled by a hierarchical quadratic programming technique which enables the definition of a set of secondary tasks to be executed in the robot’s nullspace. Finally, to demonstrate the advantages and disadvantages of the proposed user interfaces, five participants are asked to perform two different experiments: (i) target selection task on a moving surface and (ii) remote path tracking on a fixed surface. The quantitative and qualitative analyses show the effectiveness of the proposed interface during the teleoperation tasks, especially when it comes to the precise and dynamic task execution.