Gaussian Processes (GPs), as a nonparametric learning method, offer flexible modeling capabilities and calibrated uncertainty quantification for function approximations. Additionally, GPs support online learning by efficiently incorporating new data with polynomial-time computation, making them well-suited for safety-critical dynamical systems that require rapid adaptation. However, the inference and online updates of exact GPs, when processing streaming data, incur cubic computation time and quadratic storage memory complexity, limiting their scalability to large datasets in real-time settings. In this paper, we propose a streaming kernel-induced progressively generated expert framework of Gaussian processes (SkyGP) that addresses both computational and memory constraints by maintaining a bounded set of experts, while inheriting the learning performance guarantees from exact Gaussian processes. Furthermore, two SkyGP variants are introduced, each tailored to a specific objective, either maximizing prediction accuracy (SkyGP-Dense) or improving computational efficiency (SkyGP-Fast). The effectiveness of SkyGP is validated through extensive benchmarks and real-time control experiments demonstrating its superior performance compared to state-of-the-art approaches.

In this work, we model and control a cable-driven, remote-actuated system that includes both friction and compliance in its dynamics. The control objective is to solve a regulation problem using a Model Predictive Controller (MPC). Unlike the flexible-joint robot models, which typically assume frictionless compliant elements, the proposed model incorporates friction forces between two compliant cable-sheaths that connect the motor to the driven link. Three controllers are developed based on the cascade control principles integrated with the MPC framework. Their performance is evaluated through both simulations and experiments on a custom-designed testbed. The results demonstrate that the MPC-Cascade control scheme achieves the best overall performance, with fast convergence and low control effort.

In this paper, we extend the Unified Force-Impedance Control (UFIC) framework for the whole-body control of service mobile robots subject to non-holonomic constraints. This enables the robot to execute complex service tasks that demand both force and impedance control within its whole body workspace. The task space of the robot is defined as the pose of both end-effectors. Following the concept of UFIC, both impedance and force tracking commands are applied in the task space of the whole-body controller, with augmented energy tanks incorporated to ensure passivity. To enable smooth transitions between force tracking and impedance control—particularly in cases of contact loss—a shaping function is used to modulate the force control command. Additionally, the robot’s redundancy is exploited to shape the posture, while satisfying joint limits, avoiding singularities, and preventing self-collisions between the arms. The effectiveness of the proposed whole-body UFIC controller is validated through simulations and real-world experiments with the service robot GARMI performing several daily tasks.

Teleoperation with haptic feedback allows users to interact with remote environments while retaining a sense of touch. However, the stability and transparency of these systems are compromised under communication network delay. This paper presents an augmented Model-Mediated Teleoperation with 3D object and dynamic environment tracking (MMT-DET) by a vision-based algorithm, enabling users to receive haptic feedback in structured dynamic environments while maintaining robustness against network delays. A user study comparing the proposed method with teleoperation using the Time Domain Passivity Approach (TDPA) was conducted. The results demonstrate that our MMT-DET exhibits robustness to varying delays in task performance and outperforms TDPA at higher delay levels.

This paper presents GARMI’s successful outdoor demonstration during the Kandahar Ski World Cup, where it performed trophy handovers in sub-zero temperatures. The event highlighted challenges in deploying robots in extreme conditions, including fluctuating temperatures and uneven terrain. GARMI achieved and completed the trophy handover during the live event, streamed to 60 million viewers. This experience raised two key research questions: the feasibility of high-precision robotics in harsh weather and strategies to compensate for environmental effects. To address them, we extended our previous framework to estimate the mass of the lifted trophy in real-time, incorporating IMU data and conducting experiments under varying temperatures and orientations. Experimental results showed that even slight variations in the robot’s base orientation had a significant impact on the accuracy of mass estimation. For instance, a 5° tilt in the robot’s base orientation resulted in a more than 100% increase in mass estimation error. Online mass estimation, performed using a quasi-static model, demonstrated improved accuracy when incorporating IMU-based corrections for base orientation. Additionally, temperature variations were found to affect robot control performance, with tracking errors increasing outside the manufacturer’s recommended temperature range. The findings highlighted the need for real-time corrections and compensations for base orientation and temperature in robot dynamics, ensuring safe human-robot interaction.

Recent studies underscore the importance of the patient’s active contribution and voluntary effort in enhancing therapy outcomes in physical rehabilitation. This paper presents an adaptive control scheme to implement active robotic rehabilitation. The primary goal is to dynamically regulate robotic assistance based on the patient’s performance and individual conditions, encouraging active participation, and effective therapy. To achieve this, a Lyapunov-based adaptive algorithm is developed that dynamically adjusts the admittance parameters by balancing the error and effort minimization. A novel performance index based on human energy input enables real-time identification of the intended human sharing role. This index is used as an adaptive rate in the proposed algorithm to enhance the control system’s dynamic responsiveness to changes in human performance. The proposed approach achieves two main rehabilitation objectives. First, it encourages active and safe human participation. Second, it enhances the therapy by providing personalized assistance, tailored to individual abilities and conditions, and thus reduces the need for therapist intervention. The performance of the proposed approach is illustrated in experimental studies. The results demonstrate the adaptability of the algorithm, ensuring compliant and safe interaction and effective task completion. Note to Practitioners—In a human-robot cooperation (HRC) framework, the automatic adaptation of the robot’s role as well as safe and stable interaction are crucial. These aspects are amplified in the context of robotic rehabilitation due to the special conditions of the human participants. Classic control methods, in shared control, lack system intelligence and automation in role allocation. However, the shared role of humans in HRC, particularly in rehabilitation applications, introduces real-time and unpredictable variations. This study addresses the shortcomings of classic control methods, by integrating intelligence into the control system through an adaptive Neural Network algorithm in shared autonomy. To emulate human-like adaptability, two crucial aspects are considered. Firstly, it incorporates safety assurance embedded in the adaptive algorithm via Lyapunov-based adaptation. Secondly, it detects the human’s role within the control loop through a novel energy-based performance index, which views the human as an active contributor to the system’s dynamic energy flow. This ensures robust behavior by dynamically adjusting the trade-off between task completion and minimal robot intervention. A standout feature of our algorithm lies in its expendability to exoskeleton systems, making it highly versatile for use in robotic rehabilitation and assistive technologies. The algorithm’s design allows for straightforward integration with exoskeletons, requiring only interaction force measurements in the joint space. It facilitates monitoring of a patient’s performance in each joint using the proposed performance index based on the human energy entry into the system. Beyond rehabilitation, the algorithm’s ability to adjust autonomy levels through adaptation makes it applicable to a wide range of Human-Robot Cooperation scenarios where automatic role allocation is necessary. Preliminary experiments underscore the adaptive algorithm’s robust responsiveness to changes in human performance. Future investigations should involve clinical experiments addressing real-life challenges associated with various movement deficiencies and responding to real-time issues that may arise during rehabilitation sessions.

This paper addresses the migration of high-frequency robot controllers to remote computing services, which are connected via a communication channel prone to delays and packet loss. The stability of the networked system is guaranteed by ensuring passivity of each subcomponent in the interconnection, as well as the Time-Domain-Passivity-Approach (TDPA) for the communication channel. We reduce conservatism of the TDPA using the model knowledge on both sides of the communication system to identify passivity excesses. This is further used to avoid over-dissipation of energy in the passivity controller by augmentation of a tolerable passivity-shortage. Tracking offsets are eliminated with a position drift compensation algorithm, for which convergence guarantees are provided. The experimental validation of the results conducted on a 7-DoF Franka Research 3 robot demonstrates a substantial enhancement in tracking performance due to the proposed modifications, particularly in scenarios with high communication delays.

Flexible manufacturing lines are required to meet the demand for customized and small batch-size products. Even though state-of-the-art tactile robots may provide the versatility for increased adaptability and flexibility, their potential is yet to be fully exploited. To support robotics deployment in manufacturing, we propose a task-based tactile robot programming paradigm that uses an object-centric tactile skill definition that directly links identified object constraints of the task to the definition of constraint-based unified force-impedance control. In this study, we first explain the basic concept of abstracting the task constraints experienced by the object and transferring them to the robot’s operational space frame. Second, using the object-centric tactile skill definition, we synthesize unified force-impedance control and formalized holonomic constraints to enable flexible task execution. Later, we propose the quantified analysis metrics for the process by analyzing them as a typical example of flexible manipulation disassembly skills, e.g., levering and unscrew-driving regarding their object requirements. Supported by realistic experimental evaluation using a Franka Emika robot, our tactile robot programming approach for the direct translation between task-level constraints and robot control parameter design is shown to be a viable solution for increased robotic deployment in flexible manufacturing lines.

Bowden cables serve as essential components in various mechanical systems, facilitating power transmission from remote actuators to specific destinations. The pretension of Bowden cables profoundly influences system performance, notably in terms of friction. This study investigates the effects of cable pretension and shape on friction and torque efficiency. A custom self-designed testbed, comprising integrated actuator units, pulleys, and a novel pretension mechanism connected by Bowden cables, is utilized to conduct experimental tests under varying parameters. This work adopts an integrated approach of experimentation, modeling, and validation, offering preliminary insights into the torque transmission characteristics of tendon driven actuator systems. Additionally, the precise model exhibits excellent conformity across a broad range of shapes and provides initial insights into hysteresis modeling attributable to cable material properties.

In this work, an upgraded exoskeleton design is presented with enhanced trajectory tracking and mechanical transparency. Compared to the first version, the design features a 3-DoF actuated shoulder joint and a mechanism to regulate the pretension of Bowden cables. Force/torque sensors are installed to directly measure the interaction forces between the human arm and the exoskeleton at the connecting points. Three control strategies were evaluated to follow a desired trajectory; A PD controller, a PD controller with friction observer, and an adaptive controller based on Radial Basis Function (RBF). These strategies also form the basis for an admittance control, aimed at improving the exoskeleton’s mechanical transparency during interaction with the human arm. Simulations and experimental results demonstrate that the PD control, supported by friction estimation via a momentum observer, achieves superior tracking performance. Moreover, the system’s mechanical transparency is enhanced using the admittance RBF-based controller, showing marginally superior results.

Despite recent advancements in torque-controlled tactile robots, integrating them into manufacturing settings remains challenging, particularly in complex environments. Simplifying robotic skill programming for non-experts is crucial for increasing robot deployment in manufacturing. This work proposes an innovative approach, Vision-Augmented Unified Force-Impedance Control (VA-UFIC), aimed at intuitive visuo-tactile exploration of unknown 3D curvatures. VA-UFIC stands out by seamlessly integrating vision and tactile data, enabling the exploration of diverse contact shapes in three dimensions, including point contacts, flat contacts with concave and convex curvatures, and scenarios involving contact loss. A pivotal component of our method is a robust online contact alignment monitoring system that considers tactile error, local surface curvature, and orientation, facilitating adaptive adjustments of robot stiffness and force regulation during exploration. We introduce virtual energy tanks within the control framework to ensure safety and stability, effectively addressing inherent safety concerns in visuo-tactile exploration. Evaluation using a Franka Emika research robot demonstrates the efficacy of VA-UFIC in exploring unknown 3D curvatures while adhering to arbitrarily defined force-motion policies. By seamlessly integrating vision and tactile sensing, VA-UFIC offers a promising avenue for intuitive exploration of complex environments, with potential applications spanning manufacturing, inspection, and beyond.

As robots become more and more intelligent, the complexity of the algorithms behind them is increasing. Since these algorithms require high computation power from the onboard robot controller, the weight of the robot and energy consumption increases. A promising solution to tackle this issue is to relocate the expensive computation to the cloud. In this pioneering work, the possibility of relocating a state-of-the-art nonlinear control is investigated. To this end, the Unified Force-Impedance Controller (UFIC) is relocated to a remote location and high frequency feedback loop is established by including the remote controller in the loop. Passivity analysis is used to ensure the stability of the whole system, comprising the robot in interaction with the environment, the communication channel, as well as the remote controller. The instability associated with the communication channel is resolved by Time Domain Passivity Approach (TDPA). The performance of the proposed framework is experimentally evaluated on a robot arm in interaction with the environment. The results illustrate the stability of the system to a time-varying delay of up to 50 ± 10ms.

With the increasing demand for new healthcare solutions and technologies, such as those resulting from the COVID-19 crisis, and the growing elderly population, exoskeletons for teleoperation are a promising solution for many future medical applications. In this context, we propose two force- sensitive upper-limb exoskeletons for teleoperation, that are characterized by: i) torque-controlled robotic actuators, ii) rigid-body model compensations, and iii) a lightweight design achieved through the use of Bowden cable transmissions and remotely placed actuators. Specifically, we present a semi-active upper-limb exoskeleton for which we demonstrate human- device interaction control and bilateral teleoperation with force- feedback, evaluated via simulation, in the lab and over the Internet. We also introduce a design for a future fully-active upper-limb exoskeleton with two contact force/torque sensors, for a dual-arm device, which features a novel 3-degrees-of- freedom exoskeleton shoulder design and a contact wrench mitigation controller, as demonstrated through simulation. With this work, we propose the essential technical steps towards a novel teleoperation system for elderly care.

Identifying the inertial parameters of real robotic manipulators is a fundamental step towards realistic modeling and better controller performances, which is crucial for safe human-robot interaction. Our work introduces a novel framework for identifying a generalized set of base inertial parameters of a serial link manipulator. This framework is designed to be adaptable to accommodate any new mounting configuration of the robot. Our theoretical analysis highlights the influence of the robot's mounting configuration on the emergence of new parameters that cannot be identified through the conventional vertical base-axis mounting approach studied previously. To validate our proposed framework, we carried out two main experiments: the first involved simulation to establish the feasibility of our concept, and in the second, our framework was employed on a Franka Emika Robot in a real-world scenario to demonstrate and validate our approach. Our simulation results confirmed the feasibility of our proposed framework, while our real-world experiment successfully identified the generalized base inertial parameter set and validated its applicability to a new robot mounting configuration.

Teleoperated robots are believed to play an important role for future applications in industry, medicine and other domains. Examples for this are remote assembly and maintenance, surgery, diagnosis or deep-sea and space exploration. Such applications are made possible by state-of-the-art tactile manipulators, well-researched control schemes and novel communication technologies such as the fifth generation of mobile communication (5G). The achievable performance is highly dependent on the communication delay and thus on the distance between leader and follower station, as well as the potentially used wireless protocol. Specially in this regard, 5G is a promising technology compared to the other communication protocols for transferring tactile information. In this paper, we introduce our telepresence reference platform, which can be used for empirical evaluation of different algorithms and communications. Comparative analysis are conducted to capture the influence of wireless communication protocols on telepresence systems consisting of complex robotic arms. The experiment compares the influence of 5G, LTE and WiFi communication protocols with regard to the motion and force tracking performance of the system.

Tactile robots shall be deployed for dynamic task execution in production lines with small batch sizes. Therefore, these robots should have the ability to respond to changing conditions and be easy to (re-)program. Operating under uncertain environments requires unifying subsystems such as robot motion and force policy into one framework, referred to as tactile skills. In this paper, we propose the enhancement of these skills for passivity-based skill motion learning in stiffness-adaptive unified force-impedance control. To achieve the increased level of adaptability, we represent all tactile skills by three basic primitives: contact initiation, manipulation, and contact termination. To ensure passivity and stability, we develop an energy-based approach for unified force-impedance control that allows humans to teach the robot motion through physical interaction during the execution of a tactile task. We incorporate our proposed framework into a tactile robot to experimentally validate the motion adaptation by interaction performance and stability of the control. While the polishing task is presented as our use case through the paper, the experiments can also be carried out with various tactile skills. Finally, the results show the novel controller's stability and passivity to contact-loss and stiffness adaptation, leading to successful programming by interaction.

The concept of hybrid zero dynamics is a promising approach for designing exponentially stabilizing controllers for dynamic walking with some degrees of underactuation. By this approach a feedback controller is designed such that a stable periodic orbit, within an invariant submanifold for the hybrid closed-loop system is created. This is usually achieved through an exponentially fast dynamics transverse to the zero dynamics manifold and the stability properties of such periodic orbit is then transferred to the full-order dynamic system. In this paper a passivity-based controller for a planar biped with one degree of underactuation is designed. By this approach we aim to preserve the natural dynamics of the system in the transverse dynamics (i.e. the dynamics transverse to the zero dynamics manifold) in contrast to the common input-output linearization method which cancels these dynamics. A Lyapunov stability analysis of the full-order system based on the conditional stability theorem is presented. By this analysis, the asymptotic stability of the periodic orbit in lower dimensional state space is extended to the full dimensional space. The results of the analysis are verified by simulation on a seven-link biped robot walking with zero ankle torque in sagittal plane.

The problem of image based visual servoing for robots working in a cluttered dynamic environment is addressed in this paper. It is assumed that the environment is observed by depth sensors which allow to measure the distance between any moving obstacle and the robot. Also an eye-in-hand camera is used to extract image features. The main idea is to control suitable image moments and to relax a certain number of robot's degrees of freedom during the interaction phase. If an obstacle approaches the robot, the main visual servoing task is relaxed partially or completely, while the image features are kept in the camera field of view by controlling the image moments. Fuzzy rules are used to set the desired values of the image moments. Beside that, the relaxed redundancy of the robot is exploited to avoid collisions. After removing the risk of collision, the main visual servoing task is resumed. The effectiveness of the algorithm is shown by several case studies on a KUKA LWR 4 robot arm.

An estimator of external generalized forces (force plus moments) acting on aerial platforms, and based on the momentum of the mechanical system, is proposed for the control of VToL UAVs together with a hierarchical architecture separating the translational and rotational dynamics of the vehicle. The closed-loop system equations are shaped as mechanical impedances, programmable through the controller gains, and forced by the residuals given by the estimation error. This arrangement allows the VToL UAVs to perform hovering and tracking tasks without a precise knowledge of the vehicle dynamics and in presence of external disturbances and unmodeled aerodynamic effects. Experiments are presented to evaluate the performance of the proposed control design.

In this paper, the problem of controlling a robot manipulator in task space, while guaranteeing a compliant behavior for the redundant degrees of freedom, is considered. This issue may arise in the case where the robot experiences an interaction on its body, especially in the presence of humans. The proposed approach guarantees correct task execution and compliance of the robot's body during intentional or accidental interaction in the null space of the main task, simultaneously. The asymptotic stability of the task-space error is ensured by using suitable observers to estimate and compensate the generalized forces acting on the task variables, without using joint torque measurements. Two different controller-observer algorithms are designed, and they are based on the task-space error and on the generalized momentum of the robot, respectively. The performance of the proposed algorithms is verified in experiments on a 7R lightweight robot arm.

In this paper an adaptive multi-priority nonlinear control algorithm for a redundant manipulator system is developed based on the Lyapunov like approach. The method considers the parametric uncertainties in the system and defines a proper filtered error signal to achieve asymptotic stability and convergence in tracking error, both for the main task and sub-tasks according to the allocated priority. The performance of the proposed method is studied by some numerical simulations.

In this paper a new approach for the null-space impedance control of a kinematically redundant robot is proposed. The approach is useful for the case where the robot experience an external interaction on the body, especially in the presence of humans. The proposed algorithm guarantees safe and dependable physical interaction of the robot body with the environment, thanks to the null-space impedance control. At the same time, the correct execution of the task assigned to the end effector is ensured by a disturbance observer. The algorithm does not require joint torque measurements. The performance of the proposed controller is verified through simulations on 7R KUKA lightweight robot arm.

This paper presents a dynamic level control algorithm to meet simultaneously multiple desired tasks based on allocated priorities for redundant robotic systems. It is shown that this algorithm can be treated as a general framework to achieve control over the whole body of the robot and some of the previously developed results are formalized using this approach. Null-space impedance control is proposed as one of the main results of using this method and is evaluated by means of computer simulation.