There exist numerous ways to achieve multi-tasking control in kinematically redundant robots to accomplish several goals simultaneously. In all approaches, regardless of the specific type of controller, one has to make a choice about the closed-loop inertia and consequently the dynamic task couplings. Here, we introduce a new control strategy which combines two fundamentally different properties that have not been brought together yet. First, we fully dynamically decouple all individual subtasks, which cannot be achieved with classical passivity-based or hierarchical approaches. Second, we provide high robustness in practice which is structurally not possible with any inverse-dynamics approaches enforcing a decoupled but constant closed-loop inertia. Beside formal proofs of stability and passivity, we compare our approach with the other categories in various simulations and experiments. Since the proposed controller is grounded on the fundamental property of full natural task-space decoupling, this underlying strategy and its benefits can also be transferred to other design methods such as quadratic programming, MPC, or learning-based approaches.

In this work, we propose a singularity-robust whole-body control framework that ensures smooth task transitions while maintaining strict priorities. The weighted generalized inverse is adopted to derive a hierarchical control law compatible with singular and redundant tasks. Moreover, a smooth activation matrix is proposed to continuously shape both null-space projectors and task-level control actions. Validation has been conducted in MATLAB/Simulink and MuJoCo simulations with Rollin’ Justin.

Since modern robots are supposed to work closely together with humans, physical human-robot interaction is gaining importance. One crucial aspect for safe collaboration is a robust collision reaction strategy that is triggered after an unintentional physical contact. In this work, we propose a dynamically-consistent collision reaction controller, where the reactive motion is performed in one particular desired direction in Cartesian space, without disturbing the remaining ones. This results in more intuitive and more predictable behavior of the end-effector. In addition, the proposed reaction control law is independent of contact and internal observer dynamics used for collision detection. The theoretical claims are validated in simulation and experiments. The proposed reaction controller is experimentally compared with a conventional approach for collision reaction. All experiments have been conducted on a torque controlled KUKA LWR IV + lightweight robot.

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.

MOTIVATION: As complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of 'pseudo-bulk' data, generated by aggregating single-cell RNA-seq expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists. RESULTS: We developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modeling of cell-type-specific mRNA bias using experimentally derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. SimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods. AVAILABILITY AND IMPLEMENTATION: SimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Physical human-robot interaction is known to be a crucial aspect in modern lightweight robotics. Herein, the estimation of external interactions is essential for the effective and safe collaboration. In this work, an extended momentum-based disturbance observer is presented which includes the sensing redundancy related to additional force-torque measurements. The observer eliminates the need for acceleration measurements/estimates and it is able to accurately reconstruct multiple simultaneous contact locations. Moreover, it provides uncoupled, configuration-independent, and singularity-free estimates of the external forces. The performance of the approach is experimentally validated on the SARA robot, the new generation of DLR lightweight robots, involving high resolution force-torque sensors in a redundant arrangement.

Lightweight robots are known to be intrinsically elastic in their joints. The established classical approaches to control such systems are mostly based on motor-side coordinates since the joints are comparatively stiff. However, that inevitably introduces errors in the coordinates that actually matter: the ones on the link side. Here we present a new joint-torque controller that uses feedback of the link-side positions. Passivity during interaction with the environment is formally shown as well as asymptotic stability of the desired equilibrium in the regulation case. The performance of the control approach is experimentally validated on DLR's new generation of lightweight robots, namely the SARA robot, which enables this step from motor-side-based to link-sided-based control due to sensors with higher resolution and improved sampling rate.

Injuries, accidents, strokes, and other diseases can significantly degrade the capabilities to perform even the most simple activities in daily life. A large share of these cases involves neuromuscular diseases, which lead to severely reduced muscle function. However, even though affected people are no longer able to move their limbs, residual muscle function can still be existent. Previous work has shown that this residual muscular activity can suffice to apply an EMG-based user interface. In this paper, we introduce DLR's robotic wheelchair EDAN (EMG-controlled Daily Assistant), which is equipped with a torque-controlled, eight degree-of-freedom light-weight arm and a dexterous, five-fingered robotic hand. Using electromyography, muscular activity of the user is measured, processed and utilized to control both the wheelchair and the robotic manipulator. This EMG-based interface is enhanced with shared control functionality to allow for efficient and safe physical interaction with the environment.

The control of a robot in its task space is a standard approach nowadays. If the system is kinematically redundant with respect to this goal, one can even execute additional subtasks simultaneously. By utilizing null space projections, for example, the whole stack of tasks can be implemented within a strict task hierarchy following the order of priority. One of the most common methods to track multiple task-space trajectories at the same time is to feedback-linearize the system and dynamically decouple all involved subtasks, which finally yields the exponential stability of the desired equilibrium. In this article, we provide a hierarchical multi-objective controller for trajectory tracking that ensures both asymptotic stability of the equilibrium and a desired contact impedance at the same time. In contrast to the state of the art in prioritized multi-objective control, feedback of the external forces can be avoided and the natural inertia of the robot is preserved. The controller is evaluated in simulations and on a standard lightweight robot with torque interface. The approach is predestined for precise trajectory tracking where dedicated and robust physical-interaction compliance is crucial at the same time.

Humanoid robots typically display locomotion patterns that include walking with flat foot-ground contact, and knees slightly bent. However, analysis of human gait indicate that several physiological mechanisms like stretched knees, heel-strike and toe push-off increase the step length and energetic efficiency of locomotion. This paper presents an implementation of two of those mechanisms, namely stretched knees and push-off, on a quasi-static whole-body balancing controller. The influence of such mechanisms on the kinematic capabilities of the DLR humanoid robot TORO is analyzed in different experiments, and their benefits are thoroughly discussed. As a result, the energetic savings of balancing with stretched knees are shown to be of reduced magnitude with respect to the overall power consumption of the robot, and the ability of TORO for negotiating stairs is greatly enhanced.

Light-weight robotic manipulators in combination with power wheelchairs can help to restore the mobility of people with disabilities. While such systems are available on the market, they typically are limited to fully manual control modes. In research, shared control methods are employed, to increase the usability of these systems. Here, we present an additional extension, by introducing a whole-body control concept to the assistive robotic system EDAN. Combined with shared control, the whole-body controller allows the realization of complex tasks which necessitate the coordination of arm and platform, while ensuring compliant behavior resulting from the impedance control law. The implemented approach is analyzed and validated in an exemplary task of opening a door, passing through it and closing it afterwards. While this task would exceed the reachability of the arm in a classical approach, the combination of whole-body control with a shared control scheme allows for quick and efficient execution.

Future planetary rovers will gain the ability to manipulate their environment in addition to the maneuverability of current systems. For dedicated contact interaction, Cartesian impedance control is a well-established approach from numerous terrestrial applications. In this paper we will present a whole-body Cartesian impedance controller for a planetary rover equipped with a robotic arm. In contrast to classical terrestrial whole-body controllers, the issue of proper wheel force distribution will be addressed within the control framework. A global optimization solves this redundancy in the over-actuation of the mobile base while additionally handling the kinematic redundancy in the serial kinematic sub-chain of the robot. The approach is experimentally validated on the DLR Lightweight Rover Unit. It can be used for versatile manipulation in rough terrain such as encountered in planetary exploration or terrestrial search-and-rescue scenarios.

Introducing humanoid robots in areas where space is limited, for example in search-and-rescue scenarios or industrial manufacturing, represents a huge challenge, especially when the environment is cluttered and unknown. The robot should be capable of utilizing multiple contact points distributed across the entire body and not just its feet and hands. Extra contacts on the whole body, for instance including the knees and elbows, enable the robot to increase its agility and robustness by enhancing the support polygon. This paper applies our passivity-based approach for hierarchical whole-body control including balancing to scenarios involving contacts distributed all over the body of the robot as required in confined spaces. The approach is experimentally validated on the torque-controlled humanoid robot TORO to demonstrate the general applicability of the presented framework.

Kinematic redundancy is a characteristic and beneficial property in collaborative robots nowadays as it enhances the flexibility and dexterity of the system. While the robot is manipulating an object, it is often necessary to kinematically reconfigure the robot, for example, when it obstructs the human. For this, internal or so-called null space motions can be carried out which do not affect the main task. In general, it is desirable that the human coworker can anticipate how the robot will move at any time. However, for null space motions this is typically not the case as they are non-intuitive and not suitable for interaction. In this work, we develop intuitive null space interaction behaviors for redundant manipulators, where the human can easily guide the robot. We want to provide users with a tool, that is straightforward to implement and solves real-world problems effectively. Two practical applications for an eight- and ten-DOF robot demonstrate the performance of the proposed method.

Motion planning in robotics is a very large field of research. Many different approaches have been developed to create smooth trajectories for robot movement. For example there are optimization algorithms, which optimize kinematic or dynamic properties of a trajectory. Furthermore, nonlinear programming methods like e.g. optimal control, or polynomial based methods are widely used for trajectory generation. Most of these techniques are used to calculate a trajectory in advance, or they are limited to create point-to-point motions, where the robot needs to stop when switching to the next target point, especially, when interpolating in rotational space. In this paper, we combine a low-pass filter and spherical linear interpolation to realize a velocity-limited online trajectory generator for robot orientations in quaternion space. We use the developed motion generator for mirroring a human arm motion with a robot, recorded by a low frequency visual tracking. Using the proposed method, we can replicate the motion of the operator's arm with very little delay and thereby achieve an easy-to-use interface. Furthermore, as we can strictly limit the velocity of the generated motion, the approach can safely be used in human robot collaboration applications.

Many household chores and industrial manufacturing tasks require a certain compliant behavior to make deliberate physical contact with the environment. This compliant behavior can be implemented by modern robotic manipulators. However, in order to plan the task execution, a robot requires generic process models of these tasks which can be adapted to different domains and varying environmental conditions. In this work we propose a classification of compliant manipulation tasks meeting these requirements, to derive related actions for automated planning. We also present a classification for the sub-category of wiping tasks, which are most common and of great importance in service robotics.We categorize actions from an object-centric perspective to make them independent of any specific robot kinematics. The aim of the proposed taxonomy is to guide robotic programmers to develop generic actions for any kind of robotic systems in arbitrary domains.

The workspace and performance of a humanoid robot is decisively influenced by the design of its torso. The joints or spinal discs are usually the weak points due to the high stress they are exposed to, e. g. when lifting heavy objects. One way to circumvent the necessity of large motors is to use parallel mechanisms to optimize the distribution of loads. Here, we analyze the workspace of the humanoid robot Rollin' Justin of the German Aerospace Center (DLR) w. r. t. the constraints imposed by kinematic coupling of torso joints via tendons. The results of the analysis can be used for planning and reactive control to efficiently exploit the torso performance capabilities of the robotic system. As an application, we design a potential field based controller to avoid violating these constraints and implement it on the real robot.

A feedback control to generate jumping motions for compliantly actuated multilegged robots is proposed. The method allows to specify the direction of the jumping motion. This is achieved by a constraint that defines a one-dimensional submanifold and a bang-bang control which generates a limit cycle on this submanifold. The approach is based on classical impedance control with the difference that the stiffness on the submanifold and the force to preserve a predefined nominal body configuration result from the intrinsic mechanical springs in the joints. Furthermore, we propose two controller implementations: the first implementation does not require to detect the contact state, while the second implementation requires contact state detection, but accounts in addition for Coulomb friction constraints. The controller is validated in simulation with a compliantly actuated quadruped.

Many houseworks such as cleaning the floor or wiping the windows require to manipulate tools over wide areas. It is necessary to move along a path while manipulating a tool with the whole body and applying exactly the right amount of force to successfully accomplish the task. So mastering such a challenge demands detailed knowledge about the involved objects and the underlying process models. Reasoning about an appropriate parameterization of the task is thereby essential. In this paper we propose a combination of object-centered hybrid reasoning and compliant force control to solve complex whole-body mobile manipulation issues. Depending on the objects involved in the task, an appropriate controller is selected and automatically parameterized. The methods are validated in an elaborate experiment on the humanoid robot Rollin' Justin.

Dexterous manipulation of everyday objects requires a precise tactile sense. Slip detection is mandatory to overcome uncertainty and compensate for external disturbances. We compare three different approaches for detecting slip. The methods are model-based slip detection via friction cones, vibration-based detection via bandpass filtering, and a common learning algorithm. They are implemented and tested on a tendon-driven two-finger setup equipped with two tactile BioTac®sensors. Several experiments are conducted to evaluate each approach. The characteristics of the methods are discussed and compared.

Robots with a large number of actuated degrees of freedom are usually redundant w.r.t. a given task. That kinematic redundancy can be utilized to execute additional tasks simultaneously, e. g. via null space projection techniques. We introduce a new representation of hierarchical robot dynamics which are based on a set of particular null space velocities. Dynamic consistency is preserved, and strict compliance with the order of priority is ensured at all times due to a power-conserving cancellation of coupling terms by active control. No external force measurements have to be performed. We show asymptotic stability of the generic closed-loop system with an arbitrary number of hierarchy levels. Several simulations confirm our results.

Compliant actuators in robotic systems improve robustness against rigid impacts and increase the performance and efficiency of periodic motions such as hitting, jumping and running. However, in the case of rigid impacts, as they can occur during hitting or running, the system behavior is changed compared to free motions which turns the control into a challenging task. We introduce a controller that excites periodic motions along the direction of an intrinsic mechanical oscillation mode. The controller requires no model knowledge and adapts to a modal excitation by means of measurement of the states. We experimentally show that the controller is able to stabilize a hitting motion on the variable stiffness robot DLR Hand Arm System. Further, we demonstrate by simulation that the approach applies for legged robotic systems with compliantly actuated joints. The controlled system can approach different modes of motion such as jumping, hopping and running, and thereby, it is able to handle the repeated occurrence of robot-ground contacts.

The technological progress in the field of robotics results in more and more complex manipulators. However, having an increasing number of degrees of freedom raises the question of how to use them effectively. In turn, establishing manipulators in human environments, e.g., as service robots, calls for the fulfillment of various constraints and tasks at the same time. In the context of torque controlled robotic systems, we provide an approach to simultaneously deal with a multitude of tasks and constraints which are arranged in a hierarchy, utilizing the large number of actuated joints of the manipulator. To this end, we propose a continuous null space projection technique to consider unilateral constraints, singular Jacobian matrices and dynamic variations of the priority order within the hierarchical structure. We show that activating and deactivating tasks as well as crossing singularities does not lead to a discontinuous control law. Simulations and experiments on the humanoid Justin of the German Aerospace Center (DLR) validate our approach. The presented concept is supposed to contribute to whole-body control frameworks.

A little fisherman in his diving suit competes against a superior fishing feet. After a crazy chase underwater, he manages to catch the Big Fish? and thereby knocks his competitors out.

Reactively dealing with self-collisions is an important requirement on multidegree-of-freedom robots in unstructured and dynamic environments. Classical methods to integrate respective algorithms into task hierarchies cause substantial problems: Either these unilateral safety constraints are permanently active, unnecessarily locking DOF for other tasks, or they get activated online and result in a discontinuous control law. We propose a new, reactive self-collision avoidance algorithm for highly complex robotic systems with a large number of DOF. In particular, configuration-dependent damping is imposed to dissipate undesired kinetic energy in a well-directed manner. Moreover, we merge the algorithm with a novel method to incorporate these unilateral constraints into a dynamic task hierarchy. Our approach both allows us to specifically limit the force/torque derivative to comply with physical constraints of the real robot and to prevent discontinuities in the control law while activating/deactivating the constraints. No redundancy is wasted. No comparable algorithms have been developed and implemented on a torque-controlled robot with such a level of complexity so far. The implementation of our generic solution on the multi-DOF humanoid Justin clearly validates the performance and demonstrates the real-time applicability of our synthetic approach. The proposed method can be used to contribute to whole-body controllers.

The mobile humanoid Rollin'Justin is a versatile experimental platform for research in manipulation tasks. Previously, different state of the art control methods and first autonomous task execution scenarios have been demonstrated. In this video two new applications with challenging task requirements are presented. One is the catching of one or even two flying balls using all of Justin's degrees of freedom. The other is the autonomous preparation of coffee. Both applications need adequate sensors to support local referencing. The required precision in position and timing is realized in software, using the sensor information, taking the varying precision of Justin's kinematic sub-chains into account and handling all timings in sub-millisecond range.

One characteristic attribute of mobile platforms equipped with a set of independent steering wheels is their omnidirectionality and the ability to realize complex translational and rotational trajectories. An accurate coordination of steering angle and spinning rate of each wheel is necessary for a consistent motion. Since the orientations of the wheels must align to the Instantaneous Center of Rotation (ICR), the current location and velocity of this specific point is essential for describing the state of the platform. However, singular configurations of the controlled system exist depending on the ICR, leading to unfeasible control inputs, i.e., infinite steering rates. Within this work we address and analyze this problem in general. Furthermore, we propose a solution for mobile platforms with variable footprint. An existing controller based on dynamic feedback linearization is augmented by a new potential field-based algorithm for singularity avoidance which uses the tunable leg lengths as an additional control input to minimize deviations from the nominal motion trajectory. Simulations and experimental results on the mobile platform of DLR's humanoid manipulator Justin support our approach.

One of the fundamental demands on robotic systems is a safe interaction with their environment. For fulfilling that condition, both collisions with obstacles and the own structure have to be avoided. We address the problem of self collisions and propose an algorithm for its avoidance which is based on artificial repulsion potential fields and applicable to both torque and position controlled manipulators. To this end, we design a damping that incorporates the configuration dependance of the robot. For a maximum level of safety, an additional emergency brake strategy based on kinetic energy considerations is introduced for situations in which self collisions are not avoidable by the controller. Experiments are performed on DLR's humanoid Justin.

Service robotics is expected to be established in human households and environments within the next decades. Therefore, dexterous and flexible behavior of these systems as well as guaranteeing safe interaction are crucial for that progress. We address these issues in terms of control strategies for the whole body of DLR's humanoid Justin. Via impedance control laws, we enable the robot to realize main tasks compliantly while, at the same time, taking care of aspects like physical limitations and collision avoidance with its own structure and the environment autonomously. The controller provides a natural redundancy resolution between the arms, the torso and the wheeled platform. A low-dimensional task space interface is proposed that can be used by planning tools. Thereby, planning time can be saved significantly. Experimental results on DLR's Justin are presented to validate our approach.