The adaptability and intuitiveness of Human-Computer-Interaction systems are enhanced by emotion recognition capabilities, whose rapid advancement asks for updated and more complete surveys. In this comprehensive work, papers using at least one of three peripheral physiological signals (galvanic skin response, heart rate and respiration signals) were identified, resulting in 386 papers and 448 studies that were reviewed according to the entire emotion recognition pipeline, and not just based on types of signals and recognition methods as done in related work. Accordingly, this review identifies trends, challenges and opportunities across different aspects of the emotion recognition literature. Our investigation showed that multimodal approaches, benefitting from complementary physiological information, dominate the literature. Emotion-inducing methods tend to be dynamic and to progress towards real-life applications. To facilitate such applications, building novel datasets should be considered. For instance, there is room for novel continuously annotated datasets to facilitate the development of dynamic emotion models—which is also crucial for reliable real-life applications. At the same time, to guarantee a reliable continuous annotation, the combination of stimuli and assessment/report method should not be too overwhelming for the studies’ participants. Our results showed that support vector machines remain prevalent among traditional machine learning methods, but the growth of deep learning methods used either for feature extraction or end-to-end recognition is evident—both in number of studies and advanced developed techniques. Although a balance between algorithms’ performance and interpretability is essential in emotion recognition, there is a noticeable gap in integrating emotion theory into algorithms, which would improve such balance. Besides bringing to light a broad panorama of the literature, this work offers a digital table with the analysis of all studies and a filter possibility, allowing researchers to take advantage of it to accelerate and/or get inspiration for their own work.

In this paper, a data-driven Model Predictive Controller (MPC) is presented, in which an off-policy Reinforcement Learning (RL) method called Deep Double Expected Sarsa is employed to update the weights of its cost function. While the parameterized MPC cost function is used as the current action-value function estimator, a Neural Network is used as the subsequent action-value function approximator. The target Neural Network is trained based on inputs and outputs of the primary MPC obtained at previous sampling times, whereby the training is performed either within each sampling time by sharing the time slots with the main algorithm or in parallel to the main algorithm as a whole. The latter reduces the required real-time computations per time slot. To compute the action of the target policy, two strategies are employed: Once a greedy policy using a minimization of the Neural Network model with respect to the action, and once the second element of the MPC vector related to the previous sampling time. Results show that there is no significant difference between the final control performance and training speed of both methods, whereas the real-time computational cost can be significantly reduced for the latter approach since the optimization related to the Neural Network can be omitted.

This paper presents a constructive heuristic approach to solve the live sawing optimization problem, which involves cutting rectangular boards from cylindrical logs with circular cross sections. The problem is defined as a two-dimensional strip bin-packing problem, where the objective is to orthogonally pack a given set of rectangular items into a set of strips inside a circle, while considering feasibility and technical constraints. The proposed approach uses a list of ordered rectangles and a combination of sorting, placement, and searching policies to build a feasible cutting, and presents a live sawing pattern generation that makes guillotine cuts possible for sawing machines. Our approach surpasses traditional methods, including a mathematical programming model and a state-of-the-art heuristic approach, in terms of computational effort, memory usage, and search requirements. Furthermore, it maintains superior cutting yield efficiency, as evidenced by the presented simulations and comparisons.

In this paper, we aim to demonstrate the potential for wider-ranging capabilities and ease of transferability of our recently developed decision-making architecture for human-robot collaboration. To this end, a somewhat related but different application-specific example from the generic one used in its development is chosen, a toy car assembling task in which a participant works together with a robot to perform the assembly task. In a “Wizard of Oz” fashion, a comparison is made between the participant’s reactions to working with the robot being controlled either by our architecture or by a human “Wizard” who is hidden from view. With regard to the generalisability of the architecture, we also wish to investigate whether specific models trained on the observed human behaviour in a generic assembly task also transfer to this more complex task. Therefore, pre-trained interaction models from a prior generic pick-and-place task are used again in this new application without any re-training. The architecture was implemented on a robotic arm. Participants worked with the robotic arm to perform the task of picking toy car parts one by one and assembling the car while collaborating with the robot. Each participant repeated the task 3 times for each condition, Model or Wizard, in a random order. At the end of each trial participants completed a PeRDITA questionnaire. First, a test to rule out significant differences was performed, which yielded no significant results for any of the subjective and objective measures. As not having a significant difference does not necessarily mean similarity of conditions, to check for similarity, a Bayesian comparison of the conditions was performed next, which indicated a high probability of similarity between the model and Wizard performance. The high similarity to human-like performance observed for this more complex task supports the claim for the transferability of the models trained on a more generic task.

Rescue missions in mountain environments are hardly achievable by standard legged robots—because of the high slopes—or by flying robots—because of limited payload capacity. We present a concept for a rope-aided climbing robot which can negotiate up-to-vertical slopes and carry heavy payloads. The robot is attached to the mountain through a rope, and it is equipped with a leg to push against the mountain and initiate jumping maneuvers. Between jumps, a hoist is used to wind/unwind the rope to move vertically and affect the lateral motion. This simple (yet effective) two-fold actuation allows the system to achieve high safety and energy efficiency. Indeed, the rope prevents the robot from falling while compensating for most of its weight, drastically reducing the effort required by the leg actuator. We also present an optimal control strategy to generate point-to-point trajectories overcoming an obstacle. We achieve fast computation time ($16\ m$long jump, showing the effectiveness of the proposed approach, and confirming the interest of our concept. Finally, we performed a reachability analysis showing that the region of achievable targets is strongly affected by the friction properties of the foot-wall contact.

Enhancing transparency of a teleoperation system by increasing the command-following bandwidth has not received lots of attention so far. This is considered a challenging task since in a teleoperation system the command-following bandwidth of the slave robot motion controller cannot be increased with a conventional motion controller as the desired trajectory is instantaneously commanded by the human user and thus, cannot be considered to be given in a pre-computed, smooth second order derivative form. We propose a method to increase the command-following bandwidth by extending the previously introduced Successive Stiffness Increment (SSI) approach to bilateral teleoperation. The approach allows realizing a very high motion controller gain, which cannot be realized with a conventional bilateral teleoperation controller as confirmed by experimental results.

While collision detection and contact-related injury reduction in physical human-robot interaction has been studied intensively, safety issues in physical human-robot collaboration (pHRC) with continuous coupling of human and robot(s) has received little attention so far. We develop an energy monitoring control system that observes energy flows among the different subsystems involved in pHRC, shaping them to improve human safety according to selected metrics. Port-Hamiltonian formalisms are used to model each sub-system and their interconnection. An energy-based compliance controller that enhances safety by adapting the robot behavior is proposed and validated through extensive simulations.

In manipulation tasks interaction forces are often decomposed to be able to control robustness-reflective and accelerating forces separately. While this decomposition is typically performed for the synthesis of interaction forces to be applied for example in the context of robotic grasping, less attention has been paid to the analysis of measured, human interaction forces. Here, we introduce a physically-motivated bounding constraint, based on the law of energy conservation, and present a new decomposition approach for interaction force analysis with rigid objects. The decomposition extends the intuitive solution known in literature for the two finger grasp by maximizing robustness-reflective forces while respecting the bounding constraint. Advantages of our approach are illustrated in numerical examples and experiments and by comparing it to existing decomposition approaches. In contrast to existing approaches, our new approach is not limited in the number of interaction points and incorporates only individual interaction forces which are physically plausible.

Dynamically controlling the shape of an object by means of a 3-D shape interface offers many new opportunities in the fields of virtual reality and design as direct exploration of objects becomes feasible. Using a parallel kinematics as 3-D shape interface comes with features such as high stiffness and speed, but requires a series of points to be controlled simultaneously and loop constraints to be met. In this paper we present an approach of controlling the shape of a 3-D parallel kinematics. Shape, loop constraints, and constraints resulting from the user interaction are considered in the form of a hierarchical framework and joint limitations are taken into account. Two alternative control modes for stiff and compliant objects are presented. Implicit surfaces as well as polygon sets represent the input for the virtual shape to be rendered. Results show that the calculation of the inverse kinematics can be achieved with a sampling rate of 1 kHz, independent of the representation of the shape as implicit surface or polygonal set, guaranteeing a sufficiently high sampling rate typical for haptic devices.

This paper presents a haptic assistant that enhances task performance and human-machine interaction via a gain-scheduled impedance controller. The assistance strategy proposed builds on decision-making studies and models first proposed in the field of cognitive science and combines these models with a gain-scheduled impedance control technique in order to enhance human machine interaction in a tracking task with environmental uncertainties. This paper explores the Drift-Diffusion Model as decision making model and proposes an adaptive impedance control strategy that enhances both, task performance and human-machine interaction.

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.

Emotion recognition from physiological signals like electroencephalography (EEG) can be performed using different underlying emotion models. While dimensional emotion models have recently gained attention, measures to evaluate recognition methods that are based on these models differ from study to study. This paper offers an analysis of proposed evaluation measures by comparing recognition results achieved on a self recorded dataset. Emotions are estimated using ridge regression and estimation results are compared using different evaluation measures. Additionally, three different baselines are studied, two types of random regression as well as naive estimation. Among the investigated evaluation measures, bandwidth accuracy was found to have many desirable characteristics.

Interacting with 3D-shape objects using bare hands represents a very intuitive way to explore object shapes and offers lots of new opportunities in the field of virtual reality and design. Only a few 3D-shape interfaces are known, but their resolution is rather low and/or they are not actuated. Thus, a new design for an actuated 3D-shape interface with a comparatively high resolution is presented, which can be further extended to cover larger interaction areas. The kinematics of the device is analyzed and a numerical solution for the inverse kinematics is presented. The ability to form predefined shapes is analyzed. The presented interface can display basic shapes like cylinders or spheres, and due to its specific kinematic design, can easily be mounted as end-effector to kinesthetic haptic interfaces.

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of the user which is considered an important factor in Human-Machine-Interaction. Given the vast amount of possible features from scalp recordings and the high variance between subjects, a major challenge is to select electrodes and features that separate classes well. In most cases, this decision is made based on neuro-scientific knowledge. We propose a statistically-motivated electrode/feature selection procedure, based on Cohen's effect size f2. We compare inter- and intra-individual selection on a self-recorded database. Classification is evaluated using quadratic discriminant analysis (QDA). We found both feature selection versions based on f2yield comparable results. While highest accuracies up to 57,5% (5 classes) are reached by applying intra-individual selection, inter-individual analysis successfully finds features that perform with lower variance in recognition rates across subjects than combinations of electrodes/features suggested in literature.

Dynamically rendering the shape of an object offers a lot of new opportunities in the fields of virtual reality, design, and prototyping as bare hand interaction represents a very intutive way to explore objects. We propose a 3D-shape interface formed by a parallel kinematics connecting multiple nodes and present an inverse kinematics based on a retargeting algorithm to control these nodes. The shape is formed by simultaneously taking into account loop constraints, joint limits and user interaction points. Two alternative control modes for stiff and compliant objects are introduced and compared with each other in simulations.

We present the design, analysis, control and characterization of a general purpose, six degrees-of-freedom (DoF) haptic interface for the rendering of elements and interior equipment like sliders, drawers, eyeglass holders or gloveboxes in a car. One of the major challenges in the design of six DoF haptic interfaces is the trade-off between workspace size, stiffness, output capability, and bandwidth. In order to meet this challenge we propose a new kinematic structure that allows to cover a relatively large workspace in both rotational and translational degrees of freedom, while ensuring high velocity and force output capabilities to render a wide range of impedances. An admittance controller with model-based gravity and Coriolis compensation is implemented to render different types of elements and interior equipment in a car. Hardware-related performance measures like dexterous workspace and output capabilities are derived based on kinematic and dynamic simulations and are compared to state-of-the-art devices.

In the BEAMING project we have been extending the scope of collaborative mixed reality to include the representation of users in multiple modalities, including augmented reality, situated displays and robots. A single user (a visitor) uses a high-end virtual reality system (the transporter) to be virtually teleported to a real remote location (the destination). The visitor may be tracked in several ways including emotion and motion capture. We reconstruct the destination and the people within it (the locals). In achieving this scenario, BEAMING has integrated many heterogeneous systems. In this paper, we describe the design and key implementation choices in the Beaming Scene Service (BSS), which allows the various processes to coordinate their behaviour. The core of the system is a light-weight shared object repository that allows loose coupling between processes with very different requirements (e.g. embedded control systems through to mobile apps). The system was also extended to support the notion of presence awareness. We demonstrate two complex applications built with the BSS.

Perceiving the material properties of objects through touch is generally superior to the perception of shape. We review major material properties accessible through haptic interaction, along with theoretical accounts of the underlying perceptual processes. These include roughness, friction, compliance, and thermal properties. Subsequently, we describe algorithms that have been used to render these same material properties on haptic devices. We then point to applications that have capitalized on the accessibility of material through touch, including tactile displays, simulation of mechanical mechanisms in the automobile, and medical training simulators.

This paper introduces a computer-assisted teleoperation system, where the control over the teleoperator is shared between a human operator and computer assistance in order to improve the overall task performance. Two units, an action recognition and an assistance unit are introduced to provide context-specific assistance. The action recognition unit can evaluate haptic data, handle high sampling rates, and deal with human behavior changes caused by the actived haptic assistance. Repairing of a broken hard drive is selected as scenario and three different task-specific assistance functions are designed. The overall computer-assisted teleoperation system is evaluated in two steps: first, the performance of the action recognition unit is evaluated and then, the performance of the integrated computer-assisted teleoperation system is compared with an unassisted system by means of a user study with 15 participants. Overall action recognition rates of about 65% are achieved. Multivariate paired comparisons show that the computer-assisted teleoperation system significantly reduces the human effort and damage possibility compared with a teleoperation system without assistance.

In this video we present a teleoperation system which is capable of solving complex tasks in human-sized wide area environments. The system consists of two mobile teleoperators controlled by two operators, and offers haptic, visual, and auditory feedback. The task examined here, consists of repairing a robot by removing a computer and replacing a defective hard-drive. To cope with the complexity of such a task, we go beyond classical teleoperation by integrating several advanced software algorithms into the system.

Early stages in the development of a Brain-and-Body-Computer Interface controlled robot avatar are presented. The robot is aimed at performing well-defined daily tasks upon the choice and on behalf of a user. We built on recent advances in neuroscience, robotics and machine learning to demonstrate that it is possible to control a robot, accurately and reliably, by decoding scalp-recorded non-invasive Electroencephalographic (EEG) potentials into user intentions.

In a haptic shared control system, a virtual assistant and a human share the control over performed actions to facilitate execution of manipulation tasks. The assistance level determines the amount of support provided by the assistant. It should be adapted autonomously such that task performance and human effort are optimized. The effect of the assistance level on task performance and human effort may, however, be different depending on whether human and assistant agree on the actions or not. In this paper, we investigate the effect of the assistance level on task performance and effort for a scenario, in which human and assistant agree and for a scenario, where they disagree. We present a force-based criterion for distinguishing between the two scenarios and introduce an approach to optimize the assistance levels for each of the scenarios. Finally we sketch, how the results can be used to develop novel assistance adaptation schemes.

Human-machine collaborative teleoperation systems were introduced to overcome limitations of state-of-the-art teleoperation systems by using a virtual assistant that supports the human operator in the execution of a task. Since assistances are highly task-dependent a correct classification of the currently performed task is paramount. In this paper, we present a novel approach for improving task classification for a human-machine collaborative teleoperation system. Starting from a classical HMM-based classifier implemented in our previous research, we introduce a method for correcting erroneous task classifications by evaluating an agreement criterion. This criterion is based on interactive forces and is used to distinguish between situations in which human and assistant agree/disagree in their execution of the task. Using disagreement as indicator for the activation of an unsuitable/suboptimal assistance, erroneous task classifications are identified and the original classification result is revised. The proposed approach shows significant improvements in task classification coming along with a comparable low implementation effort.

While haptic interaction with real, virtual, and remote environments is in the focus of the haptic community already since many years and implies the exchange of motion and force signals between two systems, haptic collaboration additionally requires the communication and negotiation of intentions, the building of mental models as well as the mutual adaptation of collaborating partners. Typical applications for haptic collaboration range from the manipulation and handling of heavy and large objects as required in industrial settings, the guidance of human limbs in rehabilitation, the assistance of humans in walking or performing complex manipulation tasks, the teaching of motor skills, to social interaction with virtual agents. This workshop will focus on haptic collaboration in human-X constellations where X stands for human, robot, or agent. Topics like haptic guidance, haptic shared control, mutual adaptation, learning as well as intention recognition and negotiation in haptically collaborating human-human, human-robot, and human-agent dyads will be discussed.

Telerobotics is one of the most traditional fields of robotics and it played a crucial role in the history of robotics and of the mankind, especially in the areas of space and undersea exploration and of remote material handling. On the other hand, teleoperation is still a very active research area and many problems are still open. In particular, the design of the control strategy for coupling local and remote site is of paramount importance for implementing telepresence, namely the feeling of being directly interacting with the remote environment. The IEEE RAS Technical Committee on Telerobotics would like to propose a half-day tutorial for illustrating several successful control strategies for implementing high performance bilateral teleoperation systems.

Imitation learning, also known as Programming by Demonstration, allows a non-expert user to teach complex skills to a robot. While so far researchers focused on abstracting kinematic relations, only little attention has been paid to force information. In this work we study imitation learning of human grasping skills from motion and force data. For this purpose a teleoperation system is realized that allows a human to control a simulated robotic hand and to grasp objects in a virtual environment. Haptic rendering algorithms are implemented to calculate interaction forces that occur when touching the virtual object. While learning of fingertip interaction forces is shown to result in physical inconsistency compared to the demonstrations, we show that learning of internal tensions leads to stable reproductions of the demonstrated grasping skill. Obtained results further indicate an enlarged generalisation capability of grasping skills learnt on the basis of motion and force data compared to grasping skills that encode kinematic relations only.

In a haptic teleoperation system, the incorporation of knowledge about the remote environment in the controller design can improve stability and performance. Model-mediated teleoperation adopts this idea by rendering an estimated model of the remote environment on local site instead of transmitting force/velocity flows. Thus, the user perceives locally generated forces corresponding to the estimated and transmitted model parameters and the control loop between master and slave is opened. Less conservative stability boundaries and the applicability to teleoperation systems with arbitrary time delay are the main advantages of this approach. In order to guarantee a high fidelity, the estimation has to fit well with the measurements. In this paper, we extend the approach of model-mediated teleoperation to a full 6 degrees-of-freedom (DOF) teleoperation system with negligible time delay. We furthermore propose a hybrid approach for the estimation of the remote environment by combining the classical Kelvin-Voigt model and the nonlinear Hunt-Crossley model. Persistent excitation and device-dependent limitations of the estimation algorithm are discussed. Experimental results show stability and accuracy of the estimation technique as well as a superior fidelity of the proposed approach compared to a position-based admittance controller with fixed parameters even with negligible time delay.

The collaborative research center SFB453 (www.sfb453.de) aims to realize high-fidelity telepresence and teleaction systems. Telepresence and teleaction systems extend the human workspace to remote locations in order to overcome barriers like distance, scaling, danger or the human skin. Using a human-system interface the human operator controls a remotely located teleoperator. Multi-modal feedback in form of visual, auditory, and haptic data is used to increase the feeling of telepresence. Different application areas including minimally invasive surgery, on-orbit servicing, microassembly as well as tele-manufacturing and tele-maintenance are targeted.

When robots leave industrial settings, they have to be designed allowing intuitive communication with the humans they interact with. The current paper focuses on collaboration in kinesthetic tasks. Herein, we investigate decision situations. This way, the need of communication between partners can be addressed. The current paper introduces for the first time an experimental paradigm which allows studying the effect of decision making in haptic collaboration. Because reciprocal haptic feedback is challenging to provide, we analyze its efficiency in human-human collaboration to understand when it is worth to invest in this additional modality. A one degree of tracking experiment with two human partners revealed that the additional physical effort accompanying reciprocal haptic feedback is directly transformed into higher performance (compared to a control condition without reciprocal haptic feedback). Thus, the presented results motivate further research on the nature of the haptic negotiation between human partners to achieve the same performance benefits in kinesthetic collaboration with robotic partners.

Despite the great diversity of teleoperator designs and applications, their underlying control systems have many similarities. These similarities can be exploited to enable inter-operability between heterogeneous systems. We have developed a network data specification, the Interoperable Telerobotics Protocol, that can be used for Internet based control of a wide range of teleoperators. In this work we test interoperable telerobotics on the global Internet, focusing on the telesurgery application domain. Fourteen globally dispersed telerobotic master and slave systems were connected in thirty trials in one twenty four hour period. Users performed common manipulation tasks to demonstrate effective master-slave operation. With twenty eight (93%) successful, unique connections the results show a high potential for standardizing telerobotic operation. Furthermore, new paradigms for telesurgical operation and training are presented, including a networked surgery trainer and upper-limb exoskeleton control of micro-manipulators.

An online intention recognition algorithm for computer-assisted teleoperation is introduced. The algorithm is able to distinguish between phases of a typical object manipulation task. It adopts a new advanced feature extraction algorithm which extracts features from haptic data and uses a Hidden Markov Model for stochastic classification. The method is implemented and validated on a real hardware setup. The obtained results reveal a robust and fast intention recognition.

A classification of model-mediated teleoperation systems according to the model type and its application is introduced. While models of the human operator estimating position trajectories have been already applied in teleoperation, in the present paper, we propose the incorporation of force-based human haptic interaction models. This new approach allows to transfer the strength of advanced model-mediated teleoperation, i.e. increased stability and fidelity, to scenarios where forces applied to the remote environment are of importance. As an application example we present a tele-rehabilitation scenario which was implemented on a 1 DoF teleoperation system. Position and force fusion algorithms integrating human haptic interaction models are defined for time delay and packet loss compensation. The results demonstrate clearly the benefit of incorporating force-based human haptic interaction models into teleoperation systems.

Knowledge about the remote environment can be used in the control law to improve robustness and fidelity of haptic teleoperation systems. Model-mediated teleoperation adopts this idea by rendering an estimated model of the remote environment on local site instead of transmitting force/velocity flows. In this paper, we extend the original model-mediated teleoperation approach to multi-operator multi-robot teleoperation systems. A theoretic robustness and fidelity analysis is conducted. The theoretical results show a superior performance of the proposed method compared to a classic bilateral approach. Experimental results confirm the practical efficiency of the presented approach.

This paper studies dancing as a haptic interaction task with the aim of replacing one of the human dancing partners by an interactive robot. Starting from haptic recordings of human dancing couples, the interactive behavior of a male dancing partner is synthesized. For this purpose recorded dancing steps are approximated using multiple vector fields and faded into each other by simply switching between them. Adaptation to the female style of dancing is realized by implementing an adaptation law which changes the step size of the robot. Experimental results performed on a mobile haptic interface show the validity of the presented approach.

We are interested in whether humans create a model of their partner when they jointly manipulate an object in a virtual task without haptic feedback. In such a scenario the partner is perceived as a disturbance because she/he is responsible for inconsistencies between the visual and proprioceptive feedback of the individual. To gain basic knowledge on the predictability of such disturbances we compare a pre-recorded human partner with predictable (time delay) and unpredictable (random) disturbances and two additional control conditions in a pursuit tracking task. Results indicate that the influence of the pre-recorded partner is partly predictable, therefore we assume that a model of the partner's behavior is built by the human.

This paper is the continuation of our previous work in intercontinental, collaborative teleoperation with a humanoid robot. Our new achievement consists in an extension of the former single-arm bilateral teleoperation setting to include bimanual manipulation and walking. A coupling scheme for simultaneous manipulation and locomotion is developed. Furthermore, a task-based control framework, including a force-based control for the arms as well as a walking pattern generation, is presented to realize stable whole-body motions of the highly redundant humanoid robot. Experiments have been performed to assess the proposed control scheme. They bring to light additional scientific challenges that remain in order to reach a smooth and natural telepresent collaboration.

Although it is reported in the literature that haptic feedback leads to improved performance in kinesthetic collaborative tasks, it has not been investigated so far whether this advantage is accompanied by a higher physical workload. This paper is an initial effort to examine efficiency in haptic interaction: We relate physical effort to a performance outcome in a virtual pursuit tracking task. An experimental study is conducted to compare efficiency in a collaborative mutual haptic feedback condition to three control conditions, where participants either acted alone or collaboratively without haptic feedback from the partner. Results show that reciprocal haptic feedback does not improve efficiency, although participants' performance was generally improved when doing the task with a partner, relative to executing it alone. This is due to the greater effort associated with physical connection between partners. However, the effort is more fairly distributed between partners when haptic feedback from the partner is provided. Haptic feedback may be more efficient when the amount of necessary communication between partners increases compared to the task studied here.

Achieving natural and intuitive interaction is one of the main challenges in physical human-robot interaction. We approach this challenge by modeling haptic human-human interaction with the final goal of transferring found relationships to human-robot interaction. The focus of this paper is on two human operators performing collaboratively a joint object manipulation, i.e. a pursuit tracking task. McRuer's crossover model is a well established method to describe the behavior of one human operator performing such a task. In this paper, we extent McRuer's approach to two human operators performing the task collaboratively. Results based on experimetally gained data show that the interacting partners adapt their behavior to each other and to the task in such a way that the crossover model can still be applied to the interacting dyad. It is also shown that the individual's behavior changes when interacting with a partner in contrast to performing the task alone.

Recent research focuses on developing robots that are meant to be partners of humans instead of pure machines. This makes enhanced communication necessary. Especially in scenarios embedding physical interaction between the two partners dominance is an urgent matter. To overcome one-sided dominance as in passive following or trajectory replay in favor of intuitive collaboration, human-human collaboration and the involved dominance distribution needs to be addressed. Even though some attempts are reported in literature, to our best knowledge no experimental analysis of dominance distribution in a kinesthetic task reports actual values of dominance. Therefore, the current paper discusses dominance measures appropriate in haptic interaction and investigates the dominance distribution in a tracking-task experiment. In the analysis we focus on the influence of mutual haptic feedback between the partners on dominance distribution by contrasting this condition to vision-only partner feedback trials. Furthermore, this paper investigates the consistency of dominance behavior across different partners based on methodologies transferred from social psychology. Results show that participants work with a dominance distribution, whereby the feedback condition does not effect this distribution. A high amount of variability in individual dominance behavior can be considered person dependent. Here, feedback has an effect as the dominance behavior is even more stable across partners when mutual haptic feedback is provided.

The video shows a rich multi-modal multi-user telepresence system, which was developed within the SFB453 funded by the German Research Foundation (www.sfb453.de). As a complex application scenario, the remote repairing of a broken pipe is presented in this paper. The system basically consists of two operator-teleoperator- pairs. While one of the operators interacts with a stationary human- system-interface, the other operator uses a mobile one. Both systems provide visual, auditory, and haptic feedback and enable to control the motion of head, arms, and grippers as well as the locomotion of the corresponding teleoperator.

This paper presents a new redundancy resolution approach based on the gradient projection method, which allows to avoid man-machine and machine-machine collisions as well as singularities of a redundant haptic interface. On this account cost functions for singularity and collision avoidance are formulated. Hereby the collision avoidance cost function is derived from a kinematic analysis of the human arm. Appropriate weighting factors are used to guarantee a simultaneous optimization of all defined side criteria. The performance of the presented method is analyzed by simulation.

One of the main challenges in telerobotics is the selection of control architectures and control parameters, which are able to robustly stabilize the overall teleoperation system despite of changing human operator and environment impedances. In this paper robust stability of different types of bilateral control algorithms for admittance-type devices is analyzed. Hereby stability of the system is investigated by using the parameter space approach, which allows the analysis of uncertain systems with varying plant parameters. Stability of the teleoperation system is analyzed by using linear models for human-system interface and teleoperator. The parameter space approach is adopted for controller design as well as for robustness analysis. Robust stability of the presented control architectures is evaluated for a real mechatronic teleoperation system.

In multimodal tele-cooperation as considered in this paper two humans in distant locations jointly perform a task requiring multimodal including haptic feedback. One human operator teleoperates a remotely placed humanoid robot which is collocated with the human cooperator. Time delay in the communication channel as destabilizing factor is one of the multiple challenges associated with such a tele-cooperation setup. In this paper we employ a control architecture with force-position exchange accounting for the admittance type of the haptic input device and the telerobot, which both are position-based admittance controlled. Llewellynpsilas stability criteria are employed for the parameter tuning of the virtual impedances in the presence of time delay. The control strategy is successfully validated in an intercontinental tele-cooperation experiment with the humanoid telerobot HRP-2 located in Japan/Tsukuba and a multimodal human-system-interface located in Germany/Munich, see also the corresponding video submission. The proposed setup gives rise to a large number of exciting new research questions to be addressed in the future.

The video shows an intercontinental cooperative telemanipulation task, whereby the operator site is located in Munich, Germany and the teleoperator site in Tsukuba, Japan. The human operator controls a remotely located teleoperator, which performs a task in the remote environment. Hereby the human operator is assisted by another person located at the remote site. The task consists in jointly grasping an object, moving it to a new position and finally releasing it, see Fig. 1.

If a teleoperation scenario foresees complex and fine manipulation tasks a multi-fingered telemanipulation system is required. In this paper a multi-fingered telemanipulation system is presented, whereby the human hand controls a three-finger robotic gripper and force feedback is provided by using an exoskeleton. Since the human hand and robotic grippers have different kinematic structures, appropriate mappings for forces and positions are applied. A point-to-point position mapping algorithm as well as a simple force mapping algorithm are presented and evaluated in a real experimental setup.

The concept of a mobile haptic interface for bimanual manipulations in 6 d.o.f. is presented. The design of this mobile haptic interface is based on a modular system consisting of two components: two haptic interfaces and a mobile platform. This work mainly addresses the design and control concepts of the haptic interfaces, which are planned to be mounted on the mobile platform. The interfaces dispose of a large workspace and a high force/torque capability. The design and control concepts of these new interfaces enable a decoupling of translational from rotational movements. Such a decoupling helps to simplify significantly the control algorithms which take care of the interaction between mobile platform and haptic interfaces. Evaluation results concerning the Cartesian position tracking performance and the impedance display fidelity provide an insight into the performance of the developed system.

The video shows a telepresence system which allows to perfom an assembly task in an extensive remote environment. It comprises a telemanipulator with two anthropomorphic 7-DOF arms controlled by a hyper-redundant 10-DOF haptic display. Dexterous manipulation capabilities are achieved by employing 3-finger grippers at the teleoperator site and data gloves at the operator site. Mounting the telemanipulator on an omnidirectional mobile platform additionally enables the teleoperator to freely move around in the remote environment. Thereby the locomotion is controlled via a specifically designed 3-DOF pedal. The video shows an assembly experiment with toy bricks which involves locomotion, navigation and dextrous manipulation tasks.

This work addresses some practical issues regarding development of a telerobotic system for 6 degrees of freedom (DoF) tasks. The system consists of a hyper redundant haptic input device ViSHaRD10, a redundant 7DoF manipulator and a stereo vision system. The redundancy of the devices is exploited to assure large convex workspace and singularity-free operation. The anthropomorphic construction of the telemanipulator enables intuitive manipulation and increases the "user-friendliness" of the overall system. As a practical benchmark an assembly experiment in 6DoF for a case of a negligible time delay was successfully performed. Issues regarding inverse kinematics, spatial interaction control, transparency, and intuitiveness of teleoperation are discussed.