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.

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot’s end-effector. These components, receiving a goal from the user, traverse a collision-free set of waypoints in a coordinated manner, while avoiding static and dynamic obstacles through an obstacle avoidance unit and a novel human guidance planner. With this aim, we also present a legs tracking algorithm that utilizes 2D LiDAR sensors integrated into the mobile base to monitor the human pose. Additionally, we introduce an adaptive pulling planner responsible for guiding the individual back to the intended path if they veer off course. This is achieved by establishing a target arm end-effector position and dynamically adjusting the impedance parameters in real-time through a impedance tuning unit. To validate the framework we present a set of experiments both in laboratory settings with 12 healthy blindfolded subjects and a proof-of-concept demonstration in a real-world scenario.

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.

In this study, an adaptive object deformability-agnostic human-robot collaborative transportation framework is presented. The proposed framework enables to combine the haptic information transferred through the object with the human kinematic information obtained from a motion capture system to generate reactive whole-body motions on a mobile collaborative robot. Furthermore, it allows rotating the objects in an intuitive and accurate way during co-transportation based on an algorithm that detects the human rotation intention using the torso and hand movements. First, we validate the framework with the two extremities of the object deformability range (i.e., purely rigid aluminum rod and highly deformable rope) by utilizing a mobile manipulator which consists of an Omni-directional mobile base and a collaborative robotic arm. Next, its performance is compared with an admittance controller during a co-carry task of a partially deformable object in a 12-subjects user study. Quantitative and qualitative results of this experiment show that the proposed framework can effectively handle the transportation of objects regardless of their deformability and provides intuitive assistance to human partners. Finally, we have demonstrated the potential of our framework in a different scenario, where the human and the robot co-transport a manikin using a deformable sheet.Note to Practitioners—Transportation of objects which requires the cooperation of multiple partners, is a common task in industrial settings such as factories and warehouses. The existing human-robot collaboration solutions for this task have focused only on purely rigid objects, although deformable objects need to be carried frequently in real-world applications. In this paper, we introduce a human-robot collaborative transportation framework that can handle objects with different deformability ranging from purely rigid to highly deformable. In particular, the proposed framework generates whole-body movements on a mobile collaborative robot by combining of the haptic information transmitted through the object and the human motion information obtained from a motion capture system. Moreover, the framework includes an intuitive way to rotate the object during the execution based on human hand and torso motion. The results of the experiments where objects with various deformability characteristics were transported in collaboration with a mobile manipulator demonstrated the high potential of the proposed approach in a laboratory setting. In the future, we plan to employ a less expensive vision-based human motion tracking system instead of the IMU-based system used in this study. With this change, we will be able to eliminate the need for wearable sensors from the framework presented, which would enhance its usability in real-world scenarios.

This manuscript introduces an object deformability-agnostic framework for co-carrying tasks that are shared between a person and multiple robots. Our approach allows the full control of the co-carrying trajectories by the person while sharing the load with multiple robots depending on the size and the weight of the object. This is achieved by merging the haptic information transferred through the object and the human motion information obtained from a motion capture system. One important advantage of the framework is that no strict internal communication is required between the robots, regardless of the object size and deformation characteristics. We validate the framework with two challenging real-world scenarios: co-transportation of a wooden rigid closet and a bulky box on top of forklift moving straps, with the latter characterizing deformable objects. In order to evaluate the generalizability of the proposed framework, a heterogenous team of two mobile manipulators that consist of an Omni-directional mobile base and a collaborative robotic arm with different DoFs is chosen for the experiments. The qualitative comparison between our controller and the baseline controller (i.e., an admittance controller) during these experiments demonstrated the effectiveness of the proposed framework especially when co-carrying deformable objects. Furthermore, we believe that the performance of our framework during the experiment with the lifting straps offers a promising solution for the co-transportation of bulky and ungraspable objects.

In this work, we introduce an adaptive control framework for human-robot collaborative transportation of objects with unknown deformation behaviour. The proposed framework takes as input the haptic information transmitted through the object, and the kinematic information of the human body obtained from a motion capture system to create reactive whole-body motions on a mobile collaborative robot. In order to validate our framework experimentally, we compared its performance with an admittance controller during a co-transportation task of a partially deformable object. We additionally demonstrate the potential of the framework while co-transporting rigid (aluminum rod) and highly deformable (rope) objects. A mobile manipulator which consists of an Omni-directional mobile base, a collaborative robotic arm, and a robotic hand is used as the robotic partner in the experiments. Quantitative and qualitative results of a 12-subjects experiment show that the proposed framework can effectively deal with objects of unknown deformability and provides intuitive assistance to human partners.

In today's automation driven manufacturing environments, emerging technologies like cobots (collaborative robots) and augmented reality interfaces can help integrating humans into the production workflow to benefit from their adaptability and cognitive skills. In such settings, humans are expected to work with robots side by side and physically interact with them. However, the trade-off between stability and transparency is a core challenge in the presence of physical human robot interaction (pHRI). While stability is of utmost importance for safety, transparency is required for fully exploiting the precision and ability of robots in handling labor intensive tasks. In this work, we propose a new variable admittance controller based on fractional order control to handle this trade-off more effectively. We compared the performance of fractional order variable admittance controller with a classical admittance controller with fixed parameters as a baseline and an integer order variable admittance controller during a realistic drilling task. Our comparisons indicate that the proposed controller led to a more transparent interaction compared to the other controllers without sacrificing the stability. We also demonstrate a use case for an augmented reality (AR) headset which can augment human sensory capabilities for reaching a certain drilling depth otherwise not possible without changing the role of the robot as the decision maker.

With the recent advances in cobot (collaborative robot) technology, we can now work with a robot side by side in manufacturing environments. The collaboration between human and cobot can be enhanced by detecting the intentions of human to make the production more flexible and effective in future factories. In this regard, interpreting human intention and then adjusting the controller of cobot accordingly to assist human is a core challenge in physical human-robot interaction (pHRI). In this study, we propose a classifier based on Artificial Neural Networks (ANN) that predicts intended direction of human movement by utilizing electromyography (EMG) signals acquired from human arm muscles. We employ this classifier in an admittance control architecture to constrain human arm motion to the intended direction and prevent undesired movements along other directions. The proposed classifier and the control architecture have been validated through a path following task by utilizing a KUKA LBR iiwa 7 R800 cobot. The results of our experimental study with 6 participants show that the proposed architecture provides an effective assistance to human during the execution of task and reduces undesired motion errors, while not sacrificing from the task completion time.