The increasing integration of robots in close human environments necessitates robust safety measures that can adapt to evolving tasks and conditions. Current standards rely on task-specific safety evaluations that are often inflexible, requiring repeated assessments whenever task parameters change. This work proposes MonLog, a data-driven, probabilistic method to automatically derive safety curves (SCs) from recent injury protection data sets. By leveraging non-linear modeling techniques, our approach addresses the limitations of conventional linear SCs, which often result in overly conservative speed restrictions. We present a comprehensive test routine to validate our method, highlighting improvements in both compliance with safety constraints and operational efficiency. Our findings demonstrate that the proposed approach not only enhances safety but also optimizes robotic performance, making it suitable for a wide range of applications.

Efficient and safe integration of robots into human workspaces remains a significant challenge. The ISO 10218-2 standard defines permissible force thresholds that a robot is allowed to exert on humans, along with a simple model to estimate the impact force based on the impact velocity, the involved human body part, and the effective mass of the robot. In this work, we experimentally demonstrate that state-of-the-art approaches fail to compute the effective robot mass accurately, leading to unsafe or overly-restrictive robot behavior. We address this shortcoming by presenting a data-driven collision mass map that accurately predicts the effective mass perceived at the end effector for a given collision location for the entire workspace. These maps are trained using a limited set of impact data selected by our proposed measurement procedure and can serve as valuable references for safety-critical applications. We validate our method on two robots, demonstrating accurate force predictions in compliance with ISO 10218-2. In our experiments, we show that our approach greatly reduces the required force measurements compared to state-of-the-art data-driven methods for risk assessment. Furthermore, our approach allows one to easily integrate different payloads, making it highly adaptable to various collaborative tasks. The proposed collision mass map can be standardized and deployed for any collaborative robot, enabling simple integration of robots for safe and more efficient human-robot interaction.

Contact estimation and force sensing are fundamental requirements for sensitive manipulation and safe physical human-robot interaction. The robot controllers that enable these functions rely on accurate and precise sensing. The performance of external force estimation is influenced by the design of the robot’s sensory system. And similar to how humans prefer specific arm configurations for performing precise and delicate tasks, e.g., drawing a thin, straight line, robots also have "sweet spots" that allow for the most accurate performance of tasks based on their sensing capabilities. To fully exploit a robot’s proprioceptive force sensing, it is essential to provide robot integrators, designers, and simulations with knowledge about these optimal settings including factors such as joint configurations, temperatures, and many more. This paper first investigates which of these factors are most relevant and how they can be best measured and based on that introduces force sensing error maps as a tool for structured research on robot force sensing performance and future developments of tactile robot applications. We first investigate the factors influencing the force sensing performance of 7-degree-of-freedom robots on the example of a Kinova Gen3 and then derive 2-dimensional Cartesian force sensing error maps for this robot, an LWR iiwa 14, and a Franka Emika robot. These maps enable comparison of robot sensing capabilities, revealing patterns and weak spots to guide application design toward more tactile areas.

Robotic system development must adopt a holistic approach for tactile and dynamic tasks, shifting from the decoupled design of end-effectors and robot manipulators for traditional sequential tasks. Although established metrics exist for traditional tasks, such as pick-and-place, they lack the nuanced evaluation required for dynamic and tactile operations. Accordingly, this paper introduces an integrated framework that defines and unifies decoupled and coupled gripper metrics into a single perspective. We categorise gripper metrics based on their interaction with the robot manipulator, which can be entirely decoupled, coupled by time-sequence, or coupled. Using this classification, we propose 16 metrics to evaluate force control, force reaction, and efficiency. We introduce three new experimental setups and describe the corresponding procedures to quantify these metrics. Results from three commercial finger grippers demonstrate the efficacy of the proposed metrics, revealing each gripper’s strengths and limitations when integrated into different manipulator systems. Incorporating these metrics into performance reviews provides a comprehensive evaluation of robotic system fitness, considering dynamic, real-time challenges. This supports informed design choices and enhances tactile manipulation tasks.

Recent advances in control and planning allow for seamless physical human–robot interaction (pHRI). At the same time, novel challenges appear in orchestrating intelligent decision-making and ensuring safe control of robots. Particularly in scenarios involving unforeseen or unintended collisions, robots face the imperative of reacting judiciously to avert potential risks to humans, other robots, obstacles, or themselves. At the same time, they need to maintain focus on their primary task or be able to safely resume it. Collision detection and identification algorithms are now well established in industry, yet complex collision reflexes have not transitioned into industrial applications beyond basic stopping reactions. Despite the introduction of numerous advanced high-performance reflex controllers over the past decades, their real-world adoption has remained a challenge. This work establishes a systematic framework to address that gap. For this, thereflex control problemis defined,reflex behaviorsare systematically classified and categorized, and relevantsafety datais acquired followingexisting international standards. We argue that this foundational step is crucial for improving the safety and capabilities of robots in both complex industrial and domestic environments. We validate our approach within the system class of articulated manipulators through a state-of-the-art cooperative pick-and-place task, providing a blueprint for future implementations for other robot classes.

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.

The use of pointed or edged tools or objects is one of the most challenging aspects of today’s application of physical human-robot interaction (pHRI). One reason for this is that the severity of harm caused by such edged or pointed impactors is less well studied than for blunt impactors. Consequently, the standards specify well-reasoned force and pressure thresholds for blunt impactors and advise avoiding any edges and corners in contacts. Nevertheless, pointed or edged impactor geometries cannot be completely ruled out in real pHRI applications. For example, to allow edged or pointed tools such as screwdrivers near human operators, the knowledge of injury severity needs to be extended so that robot integrators can perform well-reasoned, time-efficient risk assessments. In this paper, we provide the initial datasets on injury prevention for the human hand based on drop tests with surrogates for the human hand, namely pig claws and chicken drumsticks. We then demonstrate the ease and efficiency of robot use using the dataset for contact on two examples. Finally, our experiments provide a set of injuries that may also be expected for human subjects under certain robot mass-velocity constellations in collisions. To extend this work, testing on human samples and a collaborative effort from research institutes worldwide is needed to create a comprehensive human injury avoidance database for any pHRI scenario and thus for safe pHRI applications including edged and pointed geometries.

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned models.In this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

Safety for physical human-robot interaction (pHRI) is a major concern for all application domains. While current standardization for industrial robot applications provide safety constraints that address the onset of pain in blunt impacts, these impact thresholds are difficult to use on edged or pointed impactors. The most severe injuries occur in constrained contact scenarios, where crushing is possible. Nevertheless, situations potentially resulting in constrained contact only occur in certain areas of a workspace and design or organisational approaches can be used to avoid them. What remains are risks to the human physical integrity caused by unconstrained accidental contacts, which are difficult to avoid while maintaining robot motion efficiency. Nevertheless, the probability and severity of injuries occurring with edged or pointed impacting objects in unconstrained collisions is hardly researched. In this paper, we propose an experimental setup and procedure using two pendulums modeling human hands and arms and robots to understand the injury potential of unconstrained collisions of human hands with edged objects. Pig feet are used as ex vivo surrogate samples - as these closely resemble the physiological characteristics of human hands - to create an initial injury database on the severity of injuries caused by unconstrained edged or pointed impacts. For the effective mass range of typical lightweight robots, the data obtained show low probabilities of injuries such as skin cuts or bone/tendon injuries in unconstrained collisions when the velocity is reduced to < 0.5 m/s. Additionally, distinct differences between injury probability of the finger substitutes and the back of the hand substitutes are observed. The proposed experimental setups and procedures should be complemented by sufficient human modeling, e.g. the effective masses of human body parts, and will eventually lead to a complete understanding of the biomechanical injury potential in pHRI.

Compliance with global guidelines for sustainable and responsible production in modern industry requires a comparative analysis of consumer devices' energy consumption (EC). This also holds true for the newly established generation of lightweight industrial robots (LIRs). To identify potential strategies for energy optimization, standardized benchmarking procedures are required. However, to the best of the authors' knowledge, there is currently no standardized method for benchmarking the EC of manipulators. In response to this need, we have developed a comprehensive benchmarking framework to evaluate the EC of various LIR designs, delving into the theoretical power consumption under both static and dynamic conditions. Our analysis has led to the proposal of seven proposed metrics—three static and four dynamic. The static metrics—controller consumption, joint electronics consumption, and mechanical brakes' consumption—evaluate the maintenance EC of the robot. Meanwhile, we suggest three dynamic metrics that gauge the system's energy efficiency during motion, with or without payload. We extend this metrics selection by introducing the cost of transportation map for manipulators. For each of the metrics, we suggest a standardized measurement procedure based on state-of-the-art norms and literature. The metric set and experimental procedures are demonstrated using five manipulators (UR3e, UR5e, FR3, M0609, Gen3). Among the results, we can see interesting trends for future optimization of the electronic components and their architecture, e.g., reducing the robot's EC by decentralizing computation via low-consumption onboard controllers for basic tasks and external servers for complex ones.

Strategies for safe human-robot interaction (HRI), such as the well-established Safe Motion Unit, provide a velocity scaling for biomechanically safe robot motion. In addition, psychologically-based safety approaches are required for trustworthy HRI. Such schemes can be very conservative and robot motion complying with such safety approaches should be time efficient within the robot motion planning. In this study, we improve the efficiency of a previously introduced approach for psychologically-based safety in HRI via a Model Predictive Control robot motion planner that simultaneously adjusts Cartesian path and speed to minimise the distance to the target pose as fast as possible. A subordinate real-time motion generator ensures human physical safety by integrating the Safe Motion Unit. Our motion planner is validated by two experiments. The simultaneous adjustment of path and velocity accomplishes highly time efficient robot motion, while considering the human physical and psychological safety. Compared to direct path velocity scaling approaches our planner enables 28 % faster motion execution.

In physical human-robot interaction (pHRI), safety is a key requirement. As collisions between humans and robots can generally not be avoided, it must be ensured that the human is not harmed. The robot reflected mass, the contact geometry, and the relative velocity between human and robot are the parameters that have the most significant influence on human injury severity during a collision. The reflected mass depends on the robot configuration and can be optimized especially in kinematically redundant robots. In this paper, we propose the Mean Reflected Mass (MRM) metric. The MRM is independent of the direction of contact/motion and enables assessing and optimizing the robot posture w.r.t. safety. In contrast to existing metrics, it is physically interpretable, meaning that it can be related to biomechanical injury data for realistic and model-independent safety analysis. For the Franka Emika Panda, we demonstrate in simulation that an optimization of the robot's MRM reduces the mean collision force. Finally, the relevance of the MRM for real pHRI applications is confirmed through a collision experiment.

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.

Kinesthetic teaching of collaborative robots is applied for intuitive and flexible robot programming by demonstration. This enables non-experts to program such robots on the task-level. Multiple strategies exist to teach velocity- or torque-controlled robots and, thus, the maneuverability among commercial robots differs significantly. However, currently there exists no metric that quantifies how “well” the robot can be guided, e.g., how much effort is required to initiate a motion. In this paper, we propose standardized procedures to quantitatively assess robot manual maneuverability. First, we identify different motion phases during kinesthetic teaching. For each phase, we then propose metrics and experimental setups to evaluate them. The experimental protocols are applied to the proprietary teaching schemes of five commercial robots, namely the KUKA LWR iiwa 14, Yuanda Yu+, Franka Emika robot, and Universal Robot's UR5e and UR10e. The experimental comparison highlights distinct differences between the robots and shows that the proposed methods are a meaningful contribution to the performance and ergonomics assessment of collaborative robots.

In physical human-robot interaction (pHRI), robots need to detect and react to intended and unintended contacts in a safe manner. Proprioceptive sensing capabilities and collision detection and identification techniques differ among commercially available robots, which means that also their sensitivity to detect dynamic collisions with the environment or the human co-worker differ. Up to now, there exists no standardized procedure for assessing the contact sensitivity of a robotic system. In this paper, we propose the concept of contact sensitivity maps (CSM), a relationship between the robot's dynamic impact properties and the reliability of its collision handling. The CSM allows the robot user to determine for which robot workspace areas and dynamic collision parameters (mass, velocity) reliable contact detection and reaction can be expected. We propose a standardized benchmarking procedure and test setup for deriving CSMs. Finally, we analyze and compare the experimental results of the Universal Robots UR10e, UR5e, and Franka Emika Panda, where we observe significant differences in contact sensitivity.

Improving robot systems via newly-developed sensing devices, control algorithms, or state estimators in order to obtain safe and efficient human-robot interaction as well as tactile manipulation skills requires standardized performance measurement protocols for objective comparison. Common protocols to evaluate robot motion performance are currently defined in EN ISO 9283:1998. For tactile and safety performance, however, no common metrics were agreed on nor standardized yet. In this paper, we propose a set of quantifiable performance criteria for robot performance analysis, objectifying robot force sensing, force control, and collision detection/reaction performance. We introduce the corresponding measurement setups and protocols, demonstrate and experimentally validate each with a Universal Robot UR10e and UR5e as well as a Franka Emika Panda robot arm. The proposed performance criteria, metrics, and experimental setups constitute the basis of a fully tactile performance and safety benchmarking framework that allows to objectively evaluate tactile robot performance via reproducible reference tests.