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Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots' resilience to perturbations during tasks that involve static obstacle avoidance, we propose incorporating barrier certificates into an optimization problem to learn a stable and barrier-certified DS. Such optimization problem can be very complex or extremely conservative when the traditional linear parameter-varying formulation is used. Thus, different from previous approaches in the literature, we propose to use polynomial representations for DSs, which yields an optimization problem that can be tackled by sum-of-squares techniques. Finally, our approach can handle obstacle shapes that fall outside the scope of assumptions typically found in the literature concerning obstacle avoidance within the DS learning framework. Supplementary material can be found at the project webpage: https://martinschonger.github.io/abc-ds, Comment: 7 pages, 7 figures, accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)

In the field of Learning from Demonstration (LfD), Dynamical Systems (DSs) have gained significant attention due to their ability to generate real-time motions and reach predefined targets. However, the conventional convergence-centric behavior exhibited by DSs may fall short in safety-critical tasks, specifically, those requiring precise replication of demonstrated trajectories or strict adherence to constrained regions even in the presence of perturbations or human intervention. Moreover, existing DS research often assumes demonstrations solely in Euclidean space, overlooking the crucial aspect of orientation in various applications. To alleviate these shortcomings, we present an innovative approach geared toward ensuring the safe execution of learned orientation skills within constrained regions surrounding a reference trajectory. This involves learning a stable DS on SO(3), extracting time-varying conic constraints from the variability observed in expert demonstrations, and bounding the evolution of the DS with Conic Control Barrier Function (CCBF) to fulfill the constraints. We validated our approach through extensive evaluation in simulation and showcased its effectiveness for a cutting skill in the context of assisted teleoperation.

This paper investigates the tracking problem of a smooth coordinate-invariant trajectory using dual quaternion algebra. The proposed architecture consists of a cascade structure in which the outer-loop MPC performs real-time smoothing of the manipulator's end-effector twist while an inner-loop kinematic controller ensures tracking of the instantaneous desired end-effector pose. Experiments on a $7$-DoF Franka Emika Panda robotic manipulator validate the proposed method demonstrating its application to constraint the robot twists, accelerations and jerks within prescribed bounds.

This work focuses on the agile transportation of liquids with robotic manipulators. In contrast to existing methods that are either computationally heavy, system/container specific or dependant on a singularity-prone pendulum model, we present a real-time slosh-free tracking technique. This method solely requires the reference trajectory and the robot's kinematic constraints to output kinematically feasible joint space commands. The crucial element underlying this approach consists on mimicking the end-effector's motion through a virtual quadrotor, which is inherently slosh-free and differentially flat, thereby allowing us to calculate a slosh-free reference orientation. Through the utilization of a cascaded proportional-derivative (PD) controller, this slosh-free reference is transformed into task space acceleration commands, which, following the resolution of a Quadratic Program (QP) based on Resolved Acceleration Control (RAC), are translated into a feasible joint configuration. The validity of the proposed approach is demonstrated by simulated and real-world experiments on a 7 DoF Franka Emika Panda robot. Code: https://github.com/jonarriza96/gsft Video: https://youtu.be/4kitqYVS9n8, Comment: This paper has been accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, May 2024. Copyright @ IEEE

Controlling the shape of deformable linear objects using robots and constraints provided by environmental fixtures has diverse industrial applications. In order to establish robust contacts with these fixtures, accurate estimation of the contact state is essential for preventing and rectifying potential anomalies. However, this task is challenging due to the small sizes of fixtures, the requirement for real-time performances, and the infinite degrees of freedom of the deformable linear objects. In this paper, we propose a real-time approach for estimating both contact establishment and subsequent changes by leveraging the dependency between the applied and detected contact force on the deformable linear objects. We seamlessly integrate this method into the robot control loop and achieve an adaptive shape control framework which avoids, detects and corrects anomalies automatically. Real-world experiments validate the robustness and effectiveness of our contact estimation approach across various scenarios, significantly increasing the success rate of shape control processes.

While many advancements have been made in the development of template models for describing upright-trunk locomotion, the majority of the effort has been focused on the stance phase. In this paper, we develop a new compact dynamic model as a first step toward a fully unified locomotion template model (ULT-model) of an upright-trunk forward hopping system, which will also require a unified control law in the next step. We demonstrate that all locomotion subfunctions are enabled by adding just a point foot mass and a parallel leg actuator to the well-known trunk SLIP model and that a stable limit cycle can be achieved. This brings us closer toward the ultimate goal of enabling closed-loop dynamics for anchor matching and thus achieving simple, efficient, robust and stable upright-trunk gait control, as observed in biological systems., Comment: 7 pages, 10 figures

Template models are frequently used to simplify the control dynamics for robot hopping or running. Passive limit cycles can emerge for such systems and be exploited for energy-efficient control. A grand challenge in locomotion is trunk stabilization when the hip is offset from the center of mass (CoM). The swing phase plays a major role in this process due to the moment of inertia of the leg; however, many template models ignore the leg mass. In this work, the authors consider a robot hopper model (RHM) with a rigid trunk and leg plus a hip that is displaced from the CoM. It has been previously shown that no passive limit cycle exists for such a model given a linear hip spring. In this work, we show that passive limit cycles can be found when a nonlinear hip spring is used instead. To the authors' knowledge, this is the first time that a passive limit cycle has been found for this type of system., Comment: 7 pages, 6 figures

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., Comment: accepted fo presentation at IEEE ICRA 2024

As robotics becomes increasingly vital for environmental protection, there is a growing need for effective deployment methods that match the pace of robotics innovation. Current strategies often fall short, leaving a gap between the potential of robotics and their practical application in the field. Addressing this challenge, we introduce a mobile hub concept designed to provide the necessary infrastructure and support for deploying a diverse, multi-domain robot team effectively. This paper presents the development and insights into `SVAN' (Synchronous Team-Robot Van), a prototype of our mobile hub concept. We delve into the mechanical construction and software setup of SVAN, offering a comprehensive overview of its capabilities and design considerations. Further, we discuss the hardware specifications and share valuable lessons learned during the prototype's development and deployment. In addition to this paper, an accepted video complements our exploration by depicting SVAN in its envisioned role as an environmental guardian, highlighting its potential in ecological monitoring and preservation. Furthermore, our discussion is enriched by referencing a previously accepted paper detailing a novel methodology for continuous UAV mission cycling enabled by a mobile hub like SVAN. These accompanying works underscore our contribution towards addressing the existing gaps in robot deployment strategies, presenting a scalable and efficient framework to overcome operational challenges in environmental robotics., Comment: 7 pages, 10 figures, accepted paper at IEEE ICRA Workshop on Field Robotics 2024

The scientific hypothesis of this dissertation is that complex robotic systems with active compliance and force control can be systematically designed under a unified mechatronic design and synthesis framework. The framework is successfully validated by three distinct robotic devices that were designed, based on various actuation, sensing, and transmission concepts, implemented and experimentally tested., Die wissenschaftliche Hypothese dieser Arbeit besteht darin, zu zeigen, dass komplexe Robotersysteme mit aktiver Nachgiebigkeits- und Kraftregelung systematisch unter Verwendung eines verallgemeinerten mechatronischen Entwicklungs- und Syntheseframeworks entworfen werden können. Das Framework wird erfolgreich an drei Robotersystemen validiert, die, basierend auf unterschiedlichen Aktoren, Sensoren und Transmissionskonzepten, implementiert und experimentell getestet werden.

This thesis introduces self-protective reflexes for robots and prostheses inspired by analyzing human withdrawal reflexes on a single finger and coordinated arm level. Complementary to that, human arm coordination in everyday tasks served the design of a novel control framework denoted synergy complement control. It allows a prosthesis to complement residual limb movements autonomously. Thus, the hybrid system naturally renders intended movements for its carrier and adapts online to new tasks., In dieser Dissertation wird eine neue Schutzreflex-Methodik für Roboter und Prothesen eingeführt, die durch die Analyse menschlicher Finger- und Armrückzugsreflexe inspiriert ist. Darüber hinaus wird die menschliche Armkoordination bei Greifaufgaben genutzt, um eine neue Steuerungsmethode für Prothesen namens Synergie-Komplement-Kontrolle zu entwickeln. Sie ergänzt auf natürliche Weise die Stumpf-Bewegungen des Nutzers und passt sich online neuen Aufgaben an.

This thesis aims at making robots with rigid and elastic joints aware of human collision safety. A framework is proposed that captures human injury occurrence and robot inherent safety properties in a unified manner. It allows to quantitatively compare and optimize the safety characteristics of different robot designs and is applied to stationary and mobile manipulators. On the same basis, novel motion control schemes are developed and experimentally validated., Ziel dieser Arbeit ist es, die Kollisionsdynamik von Robotern mit starren und elastischen Gelenken systematisch in die Entwicklung sicherer Mensch-Roboter-Interaktion zu integrieren. Es wird ein methodischer Rahmen vorgeschlagen, mit dem die Sicherheitseigenschaften und -fähigkeiten verschiedener Robotersystemklassen quantitativ verglichen und optimiert werden können. Auf dieser Basis werden neuartige Steuerungs- und Regelungsverfahren vorgeschlagen und experimentell validiert.

To speed up and reduce power consumption per cycle in robotic manipulation, one option is to exploit intentional collisions with the surrounding environment and objects, an approach referred to as impact-aware manipulation. Within this context, this paper focuses on developing an online collision monitoring framework for distinguishing between expected and unexpected post-impact behaviors. The classification is based on a desired post-impact motion created via an idealized rigid robot-object-environment model. To generate a classification error bound, it employs a causal envelop filter that is needed due to the unavoidable joint and environment flexibility. In this way, it becomes possible to compare a desired idealized rigid response, which is straightforward to obtain with existing tools, with a measured impact response, which is affected by difficult-to-model post-impact oscillations. The classifier can be used for single-contact as well as multi-contact impact scenarios, such as those occurring in surface-to-surface impacts, and allows for tuning of the sensitivity between expected and unexpected post-impact behaviors. The monitoring framework fuses a (bandpass) momentum observer with impact-aware control to extend the classical collision event pipeline. As a proof of concept, we show the effectiveness of the approach through numerical simulations as well as with preliminary experimental results.

Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.

Automation in the life science research laboratory is a paradigm that has gained increasing relevance in recent years. Current robotic solutions often have a limited scope, which reduces their acceptance and prevents the realization of complex workflows. The transport and manipulation of laboratory supplies with a robot is a particular case where this limitation manifests. In this paper, we deduce a taxonomy of biolaboratory liquid containers that clarifies the need for a flexible grasping solution. Using the taxonomy as a guideline, we design fingers for a parallel robotic gripper which are developed with a monolithic dual-extrusion 3D print that integrates rigid and soft materials to optimize gripping properties. We design fine-tuned fingertips that provide stable grasps of the containers in question. A simple actuation system and a low weight are maintained by adopting a passive compliant mechanism. The ability to resist chemicals and high temperatures and the integration with a tool exchange system render the fingers usable for daily laboratory use and complex workflows. We present the task suitability of the fingers in experiments that show the wide range of vessels that can be handled as well as their tolerance against displacements and their grasp stability., Comment: Version of the article that was accepted for publication at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

The goal of this thesis is to accomplish a path tracking accuracy of below 20 µm with a robot manipulator via an external stabilization device. The first approach focuses on damping the pose dependent structural dynamics of a robot manipulator using active vibration damping methods applied via a proof-mass actuator. The second approach uses a highly precise positioning system to stabilize the process tool against any disturbances transferred over the robot's structure., Das Ziel dieser Arbeit ist es, eine Bahngenauigkeit von unter 20 µm mit einem Robotermanipulator durch eine externe Stabilisierungsvorrichtung zu erreichen. Der erste Ansatz konzentriert sich auf die Dämpfung der Strukturdynamik eines Robotermanipulators durch aktive Dämpfungsmethoden mit einen Proof-Mass-Aktor. Der zweite Ansatz verwendet ein hochpräzises Positionierungssystem, um das Prozesswerkzeug gegen Störungen zu stabilisieren die über die Roboterstruktur übertragen werden.

The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role. It is well-established that humans exhibit extraordinary dexterity when handling objects. Such dexterity seems to derive from a robust understanding of object properties (such as weight, size, and shape), as well as a remarkable capacity to interact with them. Hand postures commonly demonstrate the influence of specific regions on objects that need to be grasped, especially when objects are partially visible. In this work, we leverage human-like object understanding by reconstructing and completing their full geometry from partial observations, and manipulating them using a 7-DoF anthropomorphic robot hand. Our approach has significantly improved the grasping success rates of baselines with only partial reconstruction by nearly 30% and achieved over 150 successful grasps with three different object categories. This demonstrates our approach's consistent ability to predict and execute grasping postures based on the completed object shapes from various directions and positions in real-world scenarios. Our work opens up new possibilities for enhancing robotic applications that require precise grasping and manipulation skills of real-world reconstructed objects., Comment: Accepted to RA-L 2023

We propose the next evolution of the artificial sense of touch, including an in-depth examination of the latest advancements in tactile sensing technology and the challenges that remain. We delve into the forefront of DNA and nanomaterials that enable the design of functionalized nanostructures in combination with the advantages of auto-assembly mechanisms. We evaluate the impact those technologies have on the challenges still faced in tactile sensing technology, including self-healing mechanisms, self-adaption, multi-modal, stretchable sensor structures, neuromorphic signal transmission, and scalable manufacturing. To conclude, this evolving technology has the potential to redefine the artificial sense of touch, offering mechanisms that enable advanced artificial somatosensory systems that equal or surpass human capabilities., Comment: 19 pages, 2 figures, journal

In path planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sampling-based planners. A notable example is Batch Informed Trees (BIT*), where planners iteratively determine paths to batches of vertices within the exploration area. However, utilizing a consistent batch size is inefficient for initial pathfinding and optimal performance, it relies on effective task allocation. This paper introduces Flexible Informed Trees (FIT*), a sampling-based planner that integrates an adaptive batch-size method to enhance the initial path convergence rate. FIT* employs a flexible approach in adjusting batch sizes dynamically based on the inherent dimension of the configuration spaces and the hypervolume of the n-dimensional hyperellipsoid. By applying dense and sparse sampling strategy, FIT* improves convergence rate while finding successful solutions faster with lower initial solution cost. This method enhances the planner's ability to handle confined, narrow spaces in the initial finding phase and increases batch vertices sampling frequency in the optimization phase. FIT* outperforms existing single-query, sampling-based planners on the tested problems in R^2 to R^8, and was demonstrated on a real-world mobile manipulation task., Comment: 7 pages,7 figures

The convergence of embodied agents and large language models (LLMs) has brought significant advancements to embodied instruction following. Particularly, the strong reasoning capabilities of LLMs make it possible for robots to perform long-horizon tasks without expensive annotated demonstrations. However, public benchmarks for testing the long-horizon reasoning capabilities of language-conditioned robots in various scenarios are still missing. To fill this gap, this work focuses on the tabletop manipulation task and releases a simulation benchmark, \textit{LoHoRavens}, which covers various long-horizon reasoning aspects spanning color, size, space, arithmetics and reference. Furthermore, there is a key modality bridging problem for long-horizon manipulation tasks with LLMs: how to incorporate the observation feedback during robot execution for the LLM's closed-loop planning, which is however less studied by prior work. We investigate two methods of bridging the modality gap: caption generation and learnable interface for incorporating explicit and implicit observation feedback to the LLM, respectively. These methods serve as the two baselines for our proposed benchmark. Experiments show that both methods struggle to solve some tasks, indicating long-horizon manipulation tasks are still challenging for current popular models. We expect the proposed public benchmark and baselines can help the community develop better models for long-horizon tabletop manipulation tasks., Comment: 6 pages, 4 figures. The video and code of LoHoRavens are available at https://cisnlp.github.io/lohoravens-webpage

As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We further provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot.

Elastic actuation taps into elastic elements' energy storage for dynamic motions beyond rigid actuation. While Series Elastic Actuators (SEA) and Variable Stiffness Actuators (VSA) are highly sophisticated, they do not fully provide control over energy transfer timing. To overcome this problem on the basic system level, the Bi-Stiffness Actuation (BSA) concept was recently proposed. Theoretically, it allows for full link decoupling, while simultaneously being able to lock the spring in the drive train via a switch-and-hold mechanism. Thus, the user would be in full control of the potential energy storage and release timing. In this work, we introduce an initial proof-of-concept of Bi-Stiffness-Actuation in the form of a 1-DoF physical prototype, which is implemented using a modular testbed. We present a hybrid system model, as well as the mechatronic implementation of the actuator. We corroborate the feasibility of the concept by conducting a series of hardware experiments using an open-loop control signal obtained by trajectory optimization. Here, we compare the performance of the prototype with a comparable SEA implementation. We show that BSA outperforms SEA 1) in terms of maximum velocity at low final times and 2) in terms of the movement strategy itself: The clutch mechanism allows the BSA to generate consistent launch sequences while the SEA has to rely on lengthy and possibly dangerous oscillatory swing-up motions. Furthermore, we demonstrate that providing full control authority over the energy transfer timing and link decoupling allows the user to synchronously release both elastic joint and gravitational energy. This facilitates the optimal exploitation of elastic and gravitational potentials in a synergistic manner., Comment: 8 pages, 9 figures. Submitted to IEEE Robotics and Automation Letters

As of today, robots exhibit impressive agility but also pose potential hazards to humans using/collaborating with them. Consequently, safety is considered the most paramount factor in human-robot interaction (HRI). This paper presents a multi-layered safety architecture, integrating both physical and cognitive aspects for effective HRI. We outline critical requirements for physical safety layers as service modules that can be arbitrarily queried. Further, we showcase an HRI scheme that addresses human factors and perceived safety as high-level constraints on a validated impact safety paradigm. The aim is to enable safety certification of human-friendly robots across various HRI scenarios., Comment: Accepted for Human-Friendly Robotics 2023: 16th International Workshop

One of the most important aspects of autonomous systems is safety. This includes ensuring safe human-robot and safe robot-environment interaction when autonomously performing complex tasks or in collaborative scenarios. Although several methods have been introduced to tackle this, most are unsuitable for real-time applications and require carefully hand-crafted obstacle descriptions. In this work, we propose a method combining high-frequency and real-time self and environment collision avoidance of a robotic manipulator with low-frequency, multimodal, and high-resolution environmental perceptions accumulated in a digital twin system. Our method is based on geometric primitives, so-called primitive skeletons. These, in turn, are information-compressed and real-time compatible digital representations of the robot's body and environment, automatically generated from ultra-realistic virtual replicas of the real world provided by the digital twin. Our approach is a key enabler for closing the loop between environment perception and robot control by providing the millisecond real-time control stage with a current and accurate world description, empowering it to react to environmental changes. We evaluate our whole-body collision avoidance on a 9-DOFs robot system through five experiments, demonstrating the functionality and efficiency of our framework., Comment: Published at 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)

Studying the manipulation of deformable linear objects has significant practical applications in industry, including car manufacturing, textile production, and electronics automation. However, deformable linear object manipulation poses a significant challenge in developing planning and control algorithms, due to the precise and continuous control required to effectively manipulate the deformable nature of these objects. In this paper, we propose a new framework to control and maintain the shape of deformable linear objects with two robot manipulators utilizing environmental contacts. The framework is composed of a shape planning algorithm which automatically generates appropriate positions to place fixtures, and an object-centered skill engine which includes task and motion planning to control the motion and force of both robots based on the object status. The status of the deformable linear object is estimated online utilizing visual as well as force information. The framework manages to handle a cable routing task in real-world experiments with two Panda robots and especially achieves contact-aware and flexible clip fixing with challenging fixtures.

In the field of Geriatronics, enabling effective and transparent communication between humans and robots is crucial for enhancing the acceptance and performance of assistive robots. Our early-stage research project investigates the potential of language-based modulation as a means to improve human-robot interaction. We propose to explore real-time modulation during task execution, leveraging language cues, visual references, and multimodal inputs. By developing transparent and interpretable methods, we aim to enable robots to adapt and respond to language commands, enhancing their usability and flexibility. Through the exchange of insights and knowledge at the workshop, we seek to gather valuable feedback to advance our research and contribute to the development of interactive robotic systems for Geriatronics and beyond., Comment: GERIATRONICS SUMMIT 2023

Real-time computation of optimal control is a challenging problem and, to solve this difficulty, many frameworks proposed to use learning techniques to learn (possibly sub-optimal) controllers and enable their usage in an online fashion. Among these techniques, the optimal motion framework is a simple, yet powerful technique, that obtained success in many complex real-world applications. The main idea of this approach is to take advantage of dynamic motion primitives, a widely used tool in robotics to learn trajectories from demonstrations. While usually these demonstrations come from humans, the optimal motion framework is based on demonstrations coming from optimal solutions, such as the ones obtained by numeric solvers. As usual in many learning techniques, a drawback of this approach is that it is hard to estimate the suboptimality of learned solutions, since finding easily computable and non-trivial upper bounds to the error between an optimal solution and a learned solution is, in general, unfeasible. However, we show in this paper that it is possible to estimate this error for a broad class of problems. Furthermore, we apply this estimation technique to achieve a novel and more efficient sampling scheme to be used within the optimal motion framework, enabling the usage of this framework in some scenarios where the computational resources are limited., Comment: This work has been accepted to the 22nd IFAC World Congress

This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is real-time capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings., Comment: This work has been accepted to the 22nd IFAC World Congress

Herein, we report a new approach to synthesize a series of 1,2,4-[e]-benzotriazine and cinnoline derivatives from 3-substituted isoindolin-1-one. All the reported products are obtained through an economical two-step synthetic procedure resulting in fair-to-high yields. Cinnolines (a) and 1,2,4-[e]-benzotriazines (b) result from an intramolecular cyclization of the corresponding 3-substituted isoindolin-1-ones, which, in turn, are prepared by an addition reaction from 2-cyanobenzaldehyde and 2-(2-nitrophenyl) acetonitrile (a) or 2-nitroaniline derivatives (b). A proposed mechanism for this transformation is presented.

This thesis aims at making robots with rigid and elastic joints aware of human collision safety. A framework is proposed that captures human injury occurrence and robot inherent safety properties in a unified manner. It allows to quantitatively compare and optimize the safety characteristics of different robot designs and is applied to stationary and mobile manipulators. On the same basis, novel motion control schemes are developed and experimentally validated., Ziel dieser Arbeit ist es, die Kollisionsdynamik von Robotern mit starren und elastischen Gelenken systematisch in die Entwicklung sicherer Mensch-Roboter-Interaktion zu integrieren. Es wird ein methodischer Rahmen vorgeschlagen, mit dem die Sicherheitseigenschaften und -fähigkeiten verschiedener Robotersystemklassen quantitativ verglichen und optimiert werden können. Auf dieser Basis werden neuartige Steuerungs- und Regelungsverfahren vorgeschlagen und experimentell validiert.

Herein, we report a new approach to synthesize a series of 1,2,4-[e]-benzotriazine and cinnoline derivatives from 3-substituted isoindolin-1-one. All the reported products are obtained through an economical two-step synthetic procedure resulting in fair-to-high yields. Cinnolines (a) and 1,2,4-[e]-benzotriazines (b) result from an intramolecular cyclization of the corresponding 3-substituted isoindolin-1-ones, which, in turn, are prepared by an addition reaction from 2-cyanobenzaldehyde and 2-(2-nitrophenyl) acetonitrile (a) or 2-nitroaniline derivatives (b). A proposed mechanism for this transformation is presented.

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input., Comment: Accepted version of article that has been published in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches., Comment: Accepted to IFAC World Congress 2023

Recently, many reactive trajectory planning approaches were suggested in the literature because of their inherent immediate adaption in the ever more demanding cluttered and unpredictable environments of robotic systems. However, typically those approaches are only locally reactive without considering global path planning and no guarantees for simultaneous collision avoidance and goal convergence can be given. In this paper, we study a recently developed circular field (CF)-based motion planner that combines local reactive control with global trajectory generation by adapting an artificial magnetic field such that multiple trajectories around obstacles can be evaluated. In particular, we provide a mathematically rigorous analysis of this planner in a planar environment to ensure safe motion of the controlled robot. Contrary to existing results, the derived collision avoidance analysis covers the entire CF motion planning algorithm including attractive forces for goal convergence and is not limited to a specific choice of the rotation field, i.e., our guarantees are not limited to a specific potentially suboptimal trajectory. Our Lyapunov-type collision avoidance analysis is based on the definition of an (equivalent) two-dimensional auxiliary system, which enables us to provide tight, if and only if conditions for the case of a collision with point obstacles. Furthermore, we show how this analysis naturally extends to multiple obstacles and we specify sufficient conditions for goal convergence. Finally, we provide a challenging simulation scenario with multiple non-convex point cloud obstacles and demonstrate collision avoidance and goal convergence., Comment: Published in IEEE Transactions on Automatic Control (Early Access)

This paper is about fast slosh free fluid transportation. Existing approaches are either computationally heavy or only suitable for specific robots and container shapes. We model the end effector as a point mass suspended by a spherical pendulum and study the requirements for slosh free motion and the validity of the point mass model. In this approach, slosh free trajectories are generated by controlling the pendulum's pivot and simulating the motion of the point mass. We cast the trajectory optimization problem as a quadratic program; this strategy can be used to obtain valid control inputs. Through simulations and experiments on a 7 DoF Franka Emika Panda robot we validate the effectiveness of the proposed approach., Comment: IEEE/RSJ IROS 2022

The number of sequential tasks a single gripper can perform is significantly limited by its design. In many cases, changing the gripper fingers is required to successfully conduct multiple consecutive tasks. For this reason, several robotic tool change systems have been introduced that allow an automatic changing of the entire end-effector. However, many situations require only the modification or the change of the fingertip, making the exchange of the entire gripper uneconomic. In this paper, we introduce a paradigm for automatic task-specific fingertip production. The setup used in the proposed framework consists of a production and task execution unit, containing a robotic manipulator, and two 3D printers - autonomously producing the gripper fingers. It also consists of a second manipulator that uses a quick-exchange mechanism to pick up the printed fingertips and evaluates gripping performance. The setup is experimentally validated by conducting automatic production of three different fingertips and executing graspstability tests as well as multiple pick- and insertion tasks, with and without position offsets - using these fingertips. The proposed paradigm, indeed, goes beyond fingertip production and serves as a foundation for a fully automatic fingertip design, production and application pipeline - potentially improving manufacturing flexibility and representing a new production paradigm: tactile 3D manufacturing., Comment: 8 pages, 9 figures

Manipulability ellipsoids efficiently capture the human pose and reveal information about the task at hand. Their use in task-dependent robot teaching - particularly their transfer from a teacher to a learner - can advance emulation of human-like motion. Although in recent literature focus is shifted towards manipulability transfer between two robots, the adaptation to the capabilities of the other kinematic system is to date not addressed and research in transfer from human to robot is still in its infancy. This work presents a novel manipulability domain adaptation method for the transfer of manipulability information to the domain of another kinematic system. As manipulability matrices/ellipsoids are symmetric positive-definite (SPD) they can be viewed as points on the Riemannian manifold of SPD matrices. We are the first to address the problem of manipulability transfer from the perspective of point cloud registration. We propose a manifold-aware Iterative Closest Point algorithm (ICP) with parallel transport initialization. Furthermore, we introduce a correspondence matching heuristic for manipulability ellipsoids based on inherent geometric features. We confirm our method in simulation experiments with 2-DoF manipulators as well as 7-DoF models representing the human-arm kinematics., Comment: 8 pages, 6 figures, to be published in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Natural language is one of the most intuitive ways to express human intent. However, translating instructions and commands towards robotic motion generation and deployment in the real world is far from being an easy task. The challenge of combining a robot's inherent low-level geometric and kinodynamic constraints with a human's high-level semantic instructions traditionally is solved using task-specific solutions with little generalizability between hardware platforms, often with the use of static sets of target actions and commands. This work instead proposes a flexible language-based framework that allows a user to modify generic robotic trajectories. Our method leverages pre-trained language models (BERT and CLIP) to encode the user's intent and target objects directly from a free-form text input and scene images, fuses geometrical features generated by a transformer encoder network, and finally outputs trajectories using a transformer decoder, without the need of priors related to the task or robot information. We significantly extend our own previous work presented in Bucker et al. by expanding the trajectory parametrization space to 3D and velocity as opposed to just XY movements. In addition, we now train the model to use actual images of the objects in the scene for context (as opposed to textual descriptions), and we evaluate the system in a diverse set of scenarios beyond manipulation, such as aerial and legged robots. Our simulated and real-life experiments demonstrate that our transformer model can successfully follow human intent, modifying the shape and speed of trajectories within multiple environments. Codebase available at: https://github.com/arthurfenderbucker/LaTTe-Language-Trajectory-TransformEr.git

Natural language is the most intuitive medium for us to interact with other people when expressing commands and instructions. However, using language is seldom an easy task when humans need to express their intent towards robots, since most of the current language interfaces require rigid templates with a static set of action targets and commands. In this work, we provide a flexible language-based interface for human-robot collaboration, which allows a user to reshape existing trajectories for an autonomous agent. We take advantage of recent advancements in the field of large language models (BERT and CLIP) to encode the user command, and then combine these features with trajectory information using multi-modal attention transformers. We train the model using imitation learning over a dataset containing robot trajectories modified by language commands, and treat the trajectory generation process as a sequence prediction problem, analogously to how language generation architectures operate. We evaluate the system in multiple simulated trajectory scenarios, and show a significant performance increase of our model over baseline approaches. In addition, our real-world experiments with a robot arm show that users significantly prefer our natural language interface over traditional methods such as kinesthetic teaching or cost-function programming. Our study shows how the field of robotics can take advantage of large pre-trained language models towards creating more intuitive interfaces between robots and machines. Project webpage: https://arthurfenderbucker.github.io/NL_trajectory_reshaper

As collaborative robots move closer to human environments, motion generation and reactive planning strategies that allow for elaborate task execution with minimal easy-to-implement guidance whilst coping with changes in the environment is of paramount importance. In this paper, we present a novel approach for generating real-time motion plans for point-to-point tasks using a single successful human demonstration. Our approach is based on screw linear interpolation,which allows us to respect the underlying geometric constraints that characterize the task and are implicitly present in the demonstration. We also integrate an original reactive collision avoidance approach with our planner. We present extensive experimental results to demonstrate that with our approach,by using a single demonstration of moving one block, we can generate motion plans for complex tasks like stacking multiple blocks (in a dynamic environment). Analogous generalization abilities are also shown for tasks like pouring and loading shelves. For the pouring task, we also show that a demonstration given for one-armed pouring can be used for planning pouring with a dual-armed manipulator of different kinematic structure., Comment: 8 pages, 7 figures, to be published in IEEE International Conference on Robotics and Automation (ICRA) 2022, Philadelphia (PA) USA

The current technical specification ISO/TS15066:2016(E) for safe human-robot interaction contains logically conflicting definitions for the contact between human and robot. This may result in different interpretations for the contact classification and thus no unique outcome can be expected, which may even cause a risk to the human. In previous work, we showed a first set of implications. This paper addresses the possible interpretations of a collision scenario as a result of the varying interpretations for a risk assessment. With an experiment including four commercially available robot systems we demonstrate the procedure of the risk assessment following the different interpretations of the TS. The results indicate possible incorrect use of the technical specification, which we believe needs to be resolved in future revisions. For this, we suggest tools in form of a decision tree and constrained collision force maps, which enable a simple, unambiguous risk assessment for HRI.

In this work we present a platform to assess robot platform skills using an internet-of-things (IoT) task board device to aggregate performances across remote sites. We demonstrate a concept for a modular, scale-able device and web dashboard enabling remote competitions as an alternative to in-person robot competitions. We share data from nine robot platforms located across four continents in three manipulation task categories of object localization, object insertion, and component disassembly through an organized international robot competition - the Robothon Grand Challenge. This paper discusses the design of an electronic task board, the strategies implemented by the top-performing teams and compares their results with a benchmark solution to the presented task board. Through this platform, we demonstrate fully remote, online competitions can generate innovative robotic solutions and tested a tool for measuring remote performances. Using the open-sourced task board code and design files, the reader can reproduce the benchmark solution or configure the platform for their own use case and share their results transparently without transporting their robot platform., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

We present an application of invariant polynomials in machine learning. Using the methods developed in previous work, we obtain two types of generators of the Lorentz- and permutation-invariant polynomials in particle momenta; minimal algebra generators and Hironaka decompositions. We discuss and prove some approximation theorems to make use of these invariant generators in machine learning algorithms in general and in neural networks specifically. By implementing these generators in neural networks applied to regression tasks, we test the improvements in performance under a wide range of hyperparameter choices and find a reduction of the loss on training data and a significant reduction of the loss on validation data. For a different approach on quantifying the performance of these neural networks, we treat the problem from a Bayesian inference perspective and employ nested sampling techniques to perform model comparison. Beyond a certain network size, we find that networks utilising Hironaka decompositions perform the best., Comment: 27 pages, 5 figures, 3 tables

In this dissertation, we propose a novel framework for safe human-robot collaboration which focuses on implementing a reliable collision handling pipeline. The proposed framework fills the gap towards robust safe human-robot collaboration in uncertain environments. The work does not only provide a theoretic framework for reliable human-robot collaboration in uncertain environments but also shows great potential in practical applications., In dieser Dissertation wird ein neues Gesamtkonzept für sichere Mensch-Roboter-Kollaboration-Systeme vorgestellt, welches sich intensiv mit der zuverlässigen Kollisionsauswertung auseinandersetzt. Das vorgeschlagene Framework schließt die Lücke zu einem robusten, sicheren Mensch-Roboter-Kollaboration in unsicheren Umgebungen. Die Arbeit bietet somit nicht nur einen theoretischen Rahmen für eine zuverlässige Mensch-Roboter-Kollaboration in unsicheren Umgebungen, sondern zeigt auch ein großes Potenzial für praktische Anwendungen.

PURPOSE: The Global Burden of Diseases (GBD) Studies have estimated that low back pain is one of the costliest ailments worldwide. Subsequent to GBD publications, leadership of the four largest global spine societies agreed to form SPINE20. This article introduces the concept of SPINE20, the recommendations, and the future of this global advocacy group linked to G20 annual summits. METHODS: The founders of SPINE20 advocacy group coordinated with G20 Saudi Arabia to conduct the SPINE20 summit in 2020. The summit was intended to promote evidence-based recommendations to use the most reliable information from high-level research. Eight areas of importance to mitigate spine disorders were identified through a voting process of the participating societies. Twelve recommendations were discussed and vetted. RESULTS: The areas of immediate concern were "Aging spine," "Future of spine care," "Spinal cord injuries," "Children and adolescent spine," "Spine-related disability," "Spine Educational Standards," "Patient safety," and "Burden on economy." Twelve recommendations were created and endorsed by 31/33 spine societies and 2 journals globally during a vetted process through the SPINE20.org website and during the virtual inaugural meeting November 10-11, 2020 held from the G20 platform. CONCLUSIONS: This is the first time that international spine societies have joined to support actions to mitigate the burden of spine disorders across the globe. SPINE20 seeks to change awareness and treatment of spine pain by supporting local projects that implement value-based practices with healthcare policies that are culturally sensitive based on scientific evidence.

Safe and efficient trajectory planning for autonomous robots is becoming increasingly important in both industrial applications and everyday life. The demands on a robot which has to react quickly and precisely to changes in cluttered, unknown and dynamic environments are particularly high. Towards this end, based on the initial idea proposed in Haddadin et al. 2013 we propose the Circular Field Predictions approach, which unifies reactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planning reactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planning. The proposed approach is inspired by electromagnetic fields, free of local minima and extended with artificial multi-agents to efficiently explore the environment. The algorithm is extensively analysed in complex simulation environments where it is shown to be able to generate smooth trajectories around arbitrarily shaped obstacles. Moreover, we experimentally verified the approach with a 7 Degree-of-Freedom (DoF) Franka Emika robot.