This paper presents an adaptive and compliant approach-to-grasp strategy for multi-finger robotic hands, to improve the performance of autonomous grasping when encountering object position uncertainties. With the proposed approach-to-grasp strategy, the first robot finger to experience unexpected impact would pause its movement in a compliant manner, and remains in contact with the object to minimize the unplanned motion of the target object. At the same time, the remainder of the fingers continuously, adaptively move toward re-adjusted grasping positions with respect to the first finger in contact with the object, without the need for on-line re-planning or re-grasping. An adaptive grasp control strategy based on spatial virtual spring framework is proposed to achieve local (e.g. not resorting to the robotic arm) in-hand adjustments of the fingers not yet in contact. As such, these fingers can be adaptively driven to the adjusted desired position to accomplish the grasp. Experimental results demonstrate that significantly larger position errors with respect to the hand workspace can be accommodated with the proposed adaptive compliant grasp control strategy. As much as 391% increase in position error area coverage has been achieved. Finally, beyond the quantitative analysis, additional observations during the extensive experiment trials are discussed qualitatively, to help examine several open issues, and further understand the approach-to-grasp phases of the robot hand tasks.

In this work, we deal with the problem of planning a manipulation task for a robotic system composed of at least one dexterous arm and a dexterous multi-fingered hand. The goal of the local planner is to include both, the arm and the hand, in the execution of the task in a coordinated way. This is achieved by using the workspace of the hand which is computed offline. During the online planning, the current in-hand manipulation capability is evaluated taking advantage of the dimensions of the hand workspace and considering the task itself. Dynamic weights enable the computation of the instantaneous contributions of the two subsystems on the motion of the manipulated object. The method is evaluated in simulation as well as in several experiments on the real robot.

Traditional approaches in grasping consider separately the planning of contact points and forces. This often leads to optimally simulated grasps that fail in a real execution when the forces applied at the chosen contact points cannot resist expected perturbations like the own object weight. This paper presents a method that combines a grasp planner with the computation of grasp forces, which provides contact points and contact forces that take into account joint and torque limits of the robotic hand for fingertip grasps. The planner starts with a given hand pose relative to the object, and uses the concept of reachable independent contact regions to obtain a set of force closure grasps. Within this set, the grasp that best counteracts a given external wrench is chosen. The evaluation examples illustrate how the capabilities of the robotic hand can be used more effectively by this joint planning approach.

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.

Whole arm manipulation (WAM) allows robotic manipulators to grasp or even manipulate bulky and heavy objects. The idea is that a robot basically wraps around a bulky object to grasp it. This furthermore allows to grasp relative heavy objects, since the center of gravity of the object is located more closely to the first joints of the robot. Whole arm manipulation significantly increases the manipulation skills of a robot and makes it more useful in human environment and was already applied to carry bulky and heavy objects [9], [10], or a human dummy [11]. In the past only few controllers dedicated for WAM were presented. In this paper we propose a new impedance controller on object level that considers the grasp of a bulky object with contacts on the (passive) robot chest and on each forearm of a two-armed robot system. This included to locate the object frame along with the passive contact frame, so that only object rotations need to be commanded. The controller was successfully implemented on DLR Justin. A gymnastic ball with a diameter of 0.45 m was securely grasped and the object was rotated in three dimensions.

Replicating human hand capabilities on robotic hands is a great challenge in robotics. The high complexity of mechanical and actuation systems of available robotic device can be, however, considerably mitigated if a human inspired control is considered. In this paper the application of an object-based mapping to the control of robot hands is presented. The basic idea is to use a virtual object, e.g. a virtual sphere, to capture human hand motion generating suitable reference signals for a low level controller of the robotic hand. The low level controller considered, which shares the idea of virtual object to reduce the complexity of the control, is the static Intrinsically Passive Controller (s-IPC). This controller is inspired by the dynamic IPC, but provides a simpler and more efficient implementation. The proposed approach allows to map motion of a human hand model, controlled on the reduced subspace of postural synergies, onto robotic hands guaranteeing the stability of the robotic grasp. This concept, which has been experimentally validated in the paper, can be exploit for complex planning methods or used in telemanipulation application.

Compliance in multifingered hand improves grasp stability and effectiveness of the manipulation tasks. Compliance of robotic hands depends mainly on the joint control parameters, on the mechanical design of the hand, as joint passive springs, and on the contact properties. In object grasping the primary task of the robotic hand is the control of internal forces which allows to satisfy the contact constraints and consequently to guarantee a stable grasp of the object. When compliance is an essential element of the multifingered hand, and the control of the internal forces is not designed to be decoupled from the object motion, it happens that a change in the internal forces causes the object trajectory to deviate from the planned path with consequent performance degradation. This paper studies the structural conditions to design an internal force controller decoupled from object motions. The analysis is constructive and a controller of internal forces is proposed. We will refer to this controller as object motion-decoupled control of internal forces. The force controller has been successfully tested on a realistic model of the DLR Hand II. This controller provides a trajectory interface allowing to vary the internal forces (and to specify object motions) of an underactuated hand, which can be used by higher-level modules, e.g. planning tools.

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.

Despite the progress since the first attempts of mankind to explore space, it appears that sending man in space remains challenging. While robotic systems are not yet ready to replace human presence, they provide an excellent support for astronauts during maintenance and hazardous tasks. This paper presents the development of a space qualified multi-fingered robotic hand and highlights the most interesting challenges. The design concept, the mechanical structure, the electronics architecture and the control system are presented throughout this overview paper.

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.

An anthropomorphic hand arm system using variable stiffness actuation has been developed at DLR. It is aimed to reach its human archetype regarding size, weight and performance. The main focus of our development is put on robustness, dynamic performance and dexterity. Therefore, a paradigm change from impedance controlled, but mechanically stiff joints to robots using intrinsic variable compliance joints is carried out. Collisions of the rigid joint robot at high speeds with stiff objects induce the energy too fast for an active controller to prevent damages. In contrast, passively compliant robots are able to temporarily store energy. In this case the resulting internal forces applied to the robot structure and the drive trains are reduced. Furthermore, the energy storage allows to outperform the dynamics of stiff robots. The hand drives and the electronics are completely integrated within the forearm. Extremely miniaturized electronics have been developed to drive the 52 motors of the system and interface their sensors. Several variable stiffness actuation principles used in the arm joints and the hand are presented. The paper highlights the different requirements that they have to fulfill. A first test of the systems robustness and dynamics has been performed by driving nails with a grasped hammer and is demonstrated in the attached video.

A large workspace and proper force capabilities of a robotic thumb can be obtained using a tensegrity structure for the actuation, similar to the human thumb base muscles. Using nonlinear stiffness elements and an antagonistic architecture, the joint stiffness can be adjusted by variation of the tendon pre-tension. However, the highly nonlinear actuation creates new control challenges and in particular the nonlinear tendon kinematics must be accounted for. Despite the challenges, the nonlinear structure is required to achieve the desired torques. In this paper, the dynamic equations of a tendon driven thumb are established. An efficient formulation is proposed to generate the pretension forces in order to preserve the torques and approximate the stiffness matrix. A cascaded structure is used for the controller. The equations for the inner tendon force control loop and the outer impedance control loop are presented. Because of the absence of link side position sensors, an iterative estimation algorithm is proposed and implemented in real-time. It is shown that, using the mechanical joint flexibility, the controller impedance gain can be adjusted to improve the steady-state effective impedance. The search algorithm robustness is evaluated through a set of simulations. Finally, experimental results and equivalent simulations demonstrate the effectiveness of our controller.

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.

In many robotic manipulation tasks a robotic hand is used to just grasp and fix the object of interest while the object motion is performed by the arm. Motivated by the analysis on human grasping using data reduction techniques, we applied this concept to the DLR Hand II. Therefore, we analyzed the grasp database that was grown over the past years to find suitable robotic ¿synergy coordinates¿. 74% of these grasps can be represented by two coordinates that were originally defined by 12 joint variables. As a second step, a synergy impedance controller was derived and implemented extending the work on passivity based hand control at DLR. This controller for torque-controlled robot hands allows to imitate the behavior of a synergistic, respectively underactuated, hand. Such a controller provides furthermore a simplified interface for higher level grasping strategies and allows furthermore to manually teach new grasps easily. The controller was evaluated on the DLR Hand II by commanding steps that demonstrate the desired transient behavior. Finally, two objects were successfully grasped validating our approach.

Tendon driven mechanisms have been considered in robotic design for several decades. They provide lightweight end effectors with high dynamics. Using remote actuators it is possible to free more space for mechanics or electronics. Nevertheless, lightweight mechanism are fragile and unfortunately their control software can not protect them during the very first instant of an impact. Compliant mechanisms address this issue, providing a mechanical low pass filter, increasing the time available before the controller reacts. Using adjustable stiffness elements and an antagonistic architecture, the joint stiffness can be adjusted by variation of the tendon pre-tension. In this paper, the fundamental equations of m antagonistic tendon driven mechanisms are reviewed. Due to limited tendon forces the maximum torque and the maximum acheivable stiffness are dependent. This implies, that not only the torque workspace, or the stiffness workspace must be considered but also their interactions. Since the results are of high dimensionality, quality measures are necessary to provide a synthetic view. Two quality measures, similar to those used in grasp planning, are presented. They both provide the designer with a more precise insight into the mechanism.

A robotic ball-catching system built from a multi-purpose 7-DOF lightweight arm (DLR-LWR-III) and a 12 DOF four-fingered hand (DLR-Hand-II) is presented. Other than in previous work a mechatronically complex dexterous hand is used for grasping the ball and the decision of where, when and how to catch the ball, while obeying joint, speed and work cell limits, is formulated as an unified nonlinear optimization problem with nonlinear constraints. Three different objective functions are implemented, leading to significantly different robot movements. The high computational demands of an online realtime optimization are met by parallel computation on distributed computing resources (a cluster with 32 CPU cores). The system achieves a catch rate of > 80% and is regularly shown as a live demo at our institute.

The impressive manipulation capabilities of the human hand are undoubtedly related to the thumb opposition. Such a versatility is highly desirable in the context of humanoid robots, in particular when performing object manipulation. Biomechanical data, surgery procedures and rehabilitation surveys represent an excellent base from which a robotic design can be inferred. This knowledge must be understood to identify the properties required for manipulation skills, and especially, to obtain a holistic view of the thumb functionality. Several designs have been realized, that concentrated on biomimetism or on classical mechanism designs. Therefore, it is currently difficult for designers to obtain a clear overview of the properties required for a functional robot thumb. In the present case, a robotic hand with size, forces, velocity and shape comparable to the human ones, is envisioned. Unlike most of robotic designs-where the fingers are modular and the thumb is simply a finger placed in opposition-the thumb benefits from an intensive functional analysis. This paper gathers anatomy, surgery and rehabilitation data and identifies the properties required for human like manipulation. Based on this synergy, guidelines are presented that are fused and applied to the hand design of the Integrated Hand arm project of DLR.

This paper presents experimental results on the five-finger dexterous robot hand DLR-HIT II, with Cartesian impedance control based on joint torque and nonlinearity compensation for elastic dexterous robot joints. To improve the performance of the impedance controller, system parameter estimations with extended kalman filter and gravity compensation have been investigated on the robot hand. Experimental results show that, for the harmonic drive robot hand with joint toruqe feedback, accurate position tracking and stable torque/force response can be achieved with cartesian and joint impedance controller. In addition, a FPGA-based control architecture with flexible communication is proposed to perform the designed impedance controller.

Research on humanoid robots for use in servicing tasks, e.g. fetching and delivery, attracts steadily more interest. With “Rollin' Justin” a mobile robotic system and research platform is presented that allows sophisticated control algorithms and dexterous manipulation. This video gives an overview of the mobile humanoid robotic system “Rollin' Justin” with special emphasis on mechanical design features, control issues and high-level system capabilities such as human robot interaction.

Research on humanoid robots for use in servicing tasks, e.g. fetching and delivery, attracts steadily more interest. With Rollin' Justin a mobile robotic system and research platform is presented that allows the implementation and demonstration of sophisticated control algorithms and dexterous manipulation. Important problems of service robotics such as mobile manipulation and strategies for using the increased workspace and redundancy in manipulation task can be studied in detail. This paper gives an overview of the design considerations for a mobile platform and their realizations to transform the formerly table-mounted humanoid upper body system Justin into Rollin' Justin, a fully self-sustaining mobile research platform.

This video presents a telepresence system which enables a human operator to explore a remote environment by means of a multimodal man machine interface and rollin' JUSTIN as teleoperator. The man machine interface allows for bimanual, dexterous manipulation and, through two different operating modi of the man machine interface, wide area movement as well. A bimanual assembly task, consisting of grasping a connector, opening and closing it again, is shown in this video.

The capabilities of DLR's multi-DOF humanoid robot Justin are extended with the help of a dynamic torque control component for base reaction minimization. Since the mobile base of the robot comprises springs, reactions induced by arm/torso motions lead to vibrations and deteriorate the performance. The control component is derived from the equation of motion of the robot, represented as an underactuated system, and partitioned into a ¿driven¿ subsystem (one of the arms), and a ¿compensating¿ subsystem (the other arm, with or w/o torso contribution). The control component is then embedded into the existing sophisticated controller structure of Justin, as a feedforward component, with additional control signals from an augmented PD feedback controller. It was possible to obtain satisfactory performance with a very ¿soft¿ compensatory subsystem. The experimental results confirmed the potential of this model-based approach for use in a complex multi-DOF system. As far as we know, this is the first time that a dynamic-coupling compensating controller is applied to a real system of such complexity, utilizing thereby a torque control interface.

The object level control of a dexterous robot hand provides an intuitive high-level interface to solve fine manipulation tasks. In the past, many algorithms were proposed based on a weighted pseudoinverse of the grasp map. In a different approach Stramigioli introduces a virtual object - called "intrinsically passive controller (IPC)". This controller distributes the generalized object forces using coupling springs whose weighting have an intuitive physical meaning. Even though this controller has been known for several years we will present the first experimental results for a four-fingered hand. Furthermore, the term virtual grasp map is introduced and a method to parameterize the stiffness parameters in order to obtain an effective object level stiffness and a damping design is proposed. An implementation of the IPC is tested on the DLR Hand II and its performance is analyzed by manipulating soft and stiff objects.

Walking is a fascinating way of locomotion that is very robust, especially in unstructured terrain. Many researchers devote their time to understanding its underlying principles and to build robots based on their findings. Using the fingers of DLR-Hand II a six-legged actively compliant walking robot is developed. It is intended to be used as testbed for the evaluation of different force- and position-based leg and gait control algorithms for hexapod walking in rough terrain. Following a brief overview of the finger hardware, the use of fingers as legs is analyzed and discussed. The body geometry as well as the systems constituting the robot are described. The compliance control algorithm used is explained and finally some experimental results are presented.

The current discussion on physical human robot interaction and the related safety aspects, but also the interest of neuro-scientists to validate their hypotheses on human motor skills with bio-mimetic robots, led to a recent revival of tendon-driven robots. In this paper, the modeling of tendon-driven elastic systems with nonlinear couplings is recapitulated. A control law is developed that takes the desired joint position and stiffness as input. Therefore, desired motor positions are determined that are commanded to an impedance controller. We give a physical interpretation of the controller. More importantly, a static decoupling of the joint motion and the stiffness variation is given. The combination of active (controller) and passive (mechanical) stiffness is investigated. The controller stiffness is designed according to the desired overall stiffness. A damping design of the impedance controller is included in these considerations. The controller performance is evaluated in simulation.

This video presents a humanoid two-arm system developed as a research platform for studying dexterous two-handed manipulation. The system is based on the modular DLR-Lightweight-Robot-III and the DLR-Hand-II. Two arms and hands are combined with a three degrees-of-freedom movable torso and a visual system to form a complete humanoid upper body. The diversity of the system is demonstrated by showing the mechanical design, several control concepts, the application of rapid prototyping and hardware-in-the-loop (HIL) development as well as two-handed manipulation experiments and the integration of path planning capabilities.

In this paper, the dynamic model of a robot with antagonistic actuated joints is presented, and the problem of full linearization via static state feedback is analyzed. The use of transmission elements with nonlinear relation between the displacement and the actuated force allows to control both the position and the stiffness of each joint. The main advantage of this actuation modality is that the achieved stiffness becomes a mechanical characteristic of the system and it is not the result of an immediate control action as in the classical impedance control scheme (Davison, 2003). Different examples of implementation of this kind of devices are known in literature, even if limited to one single joint (Kjita et al., 2003; Laumond and Kineocam, 2006; Mansard and Chaumette, 2004 and 2006) and the application of antagonistic actuated kinematic chains in the field of robotic hand design is under investigation (Stasse et al., 2006). After a brief review of the dependence of the properties of antagonistic actuation on the transmission elements characteristics, a scheme for simultaneous stiffness-position control of the linearized system is presented. Finally, simulation results of a two-link antagonistic actuated arm are reported and discussed.

The control of humanoid manipulators is very challenging due to the large number of degrees of freedom and the resulting redundancy. Using joint-level control complex planning algorithms are needed to accomplish tasks. For intuitive operation and hence short development times of applications high-level control interfaces are needed. Furthermore, for many tasks it is desirable to define an impedance behavior in task space. In this paper a flexible control law is proposed which offers object-level impedances for two-handed manipulation. The controller structure is based on the well-known compliance control law. The main contributions of this work are the way how to combine several potential functions for two-handed manipulation and the experimental validation of hand-arm coordination. The controller is implemented on DLR's humanoid manipulator Justin and its performance is demonstrated experimentally by unscrewing a can and motion of a grasped box.

Holding an object and manipulating it in 6D is a key application for multifingered robot hands. In the past many algorithms were proposed based on a weighted pseudoinverse of the grasp map combined with an internal force control. The majority of these algorithms require robust contact detection/tracking and switching controllers. Employing the virtual object introduced by Stramigioli we present an object-level control law. We define a novel virtual object frame based on the robot hand configuration. Our control law takes a desired object frame and desired grasping forces as input, it is passive, has an intuitive physical meaning, and stability is even given in case a finger looses contact with the object. A damping design as a function of the desired object stiffness and the combined hand-object inertia is presented. The performance of the controller is proven in two experiments implemented on the DLR Hand II