The primary issue in bilateral teleportation setups is the existence of communication delays, which can destabilize the system. We are addressing this challenge in the case of a bilateral leader–follower surgical setup, where the surgeon uses a haptic device as the leader robot to manipulate the surgical instrument held by a general-purpose manipulator, the follower robot. The follower robot is equipped with an elongated tool that through a small incision passes inside the patient's body, where sensitive structures may exist. These structures may include organs, arteries, or veins that require protection during surgery. To address this challenge, we propose a bilateral control framework that is proven to maintain passivity, ensure bounded tracking errors between the leader and follower robots, and impose remote center of motion and spatial constraints related with the sensitive structures, all in the presence of constant and variable communication delays. Experimental results in a virtual intraoperative environment, using a point cloud of a kidney and its surrounding vessels, demonstrate the effectiveness of our control scheme under various communication delay scenarios.

Robotic grasping of a target object in cluttered environments poses considerable challenges, often due to limited collision-free grasp affordances caused by the close proximity of other objects. To overcome this limitation, non-prehensile actions like pushing can be strategically employed to manipulate the environment and improve the chances of successful grasps. In this paper, we introduce a novel pre-grasp manipulation policy designed to efficiently retrieve a target object from dense clutter by leveraging pushing actions and considering the gripper’s kinematic capabilities to strategically position the target object within the gripper’s closing region for a secure grasp. Unlike conventional approaches, our policy incorporates sequential pushing, allowing the robot to make decisions while within the camera’s field of view without retracting to a home position, leading to significantly reduced execution time per action. Our policy, trained in simulation, seamlessly transfers to real-world scenarios. Extensive experimental evaluation demonstrates superior performance, faster completion times, and robust generalization to unseen objects compared to existing baselines.

In this work, a methodology to find the best view of a grape stem and approach angle in order to crop it is proposed. The control scheme is based only on a classified point cloud obtained by the in-hand camera attached to the robot’s end effector without continuous stem tracking. It is shown that the proposed controller finds and reaches the optimal view point and subsequently the stem fast and efficiently, accelerating the overall harvesting procedure. The proposed control scheme is evaluated through experiments in the lab with a UR5e robot with an in-hand RealSense camera on a mock-up vine.

In robotic assisted surgeries, surgical tools are inserted into the human body via an incision point in the abdominal wall, which is imposed as a remote center of motion (RCM). The selection of the incision's point location in the human body is critical for the success of the surgical procedure. In this paper, we propose a simulation tool for finding the optimal incision point location, which can be utilized by the surgeon during the preoperative stage. The surgeon can plan the path/region of intervention as well as sensitive regions which should be protected from unintentional damage by the surgical tool on the preoperative images of internal organs. A target admittance model that enforces a candidate incision as a RCM is utilized in the simulation enhanced by a term for following the planned path. We propose a cost evaluation function taking into account metrics involving the distance of the tool from sensitive areas, the tool links maximum pressure on tumors and the robot's dexterity measure. The example of a tumor resection task is used with the simulation tool to demonstrate its use in finding the incision points that ensures minimal intraoperative risks and accurate task execution.

In this work a control scheme is proposed to enforce dynamic obstacle avoidance constraints to the full body of actively compliant robots. We argue that both compliance and accuracy are necessary to build safe collaborative robotic systems; obstacle avoidance is usually not enough, due to the reliance on perception systems which exhibit delays and errors. Our scheme is able to successfully avoid obstacles, while remaining compliant in the entirety of the executed task. Therefore, in case of unexpected collisions due to perception system errors, the robot remains safe for humans and its environment. Our approach is validated through experiments with simulated and real obstacles utilizing a 7-dof KUKA LBR iiwa robotic manipulator.

In this work, a robot control method is proposed for dragging an object by applying top contact forces under unknown friction and object dynamics. This is a non-prehensile manipulation of an object that can enhance the grasping capabilities of a robotic manipulator in a plethora of grasping scenarios. In the proposed method, an initializing controller generates reference contact force trajectories until a desired contact motion status is achieved that enables dragging the object without slippage of the robot tip. Based on these forces, a sufficient Virtual Friction Cone (VFC) is calculated which allows proper position control of the object with no further slippage of the robotic tip. The proposed method is validated via simulations, where contact forces are simulated with the elasto-plastic friction model, and experiments with a KUKA robot dragging a variety of objects with different dynamics and surface friction.

We consider the problem of controlling a bilateral leader-follower robotic surgical set-up to allow kinesthetic haptic feedback to the user when the instrument approaches a forbidden area like sensitive organs arteries or veins that should be protected from injuries during surgery. The leader is a haptic device while the follower is a general purpose manipulator holding an elongated tool with an articulated instrument that should be manipulated through an entry port. We propose a control framework that is proved passive, incorporating a target admittance model for the follower that is designed in a way to impose a remote center of motion (RCM) while being subject to repulsive forces generated by properly designed artificial potentials associated with forbidden areas. Simulation and experimental results utilizing a virtual intraoperative environment provided as a point cloud of a kidney and its surrounding vessels characterized as forbidden areas, validate and demonstrate the performance of the proposed control scheme.

Kinesthetic teaching allows the direct skill transfer from the human to the robot and has been widely used to teach single arm tasks intuitively. In the bi-manual case, simultaneously moving both end-effectors is challenging due to the high physical and cognitive load imposed to the user. Thus, previous works on bi-manual task teaching resort to less intuitive methods by teaching each arm separately. This in turn requires motion synthesis and synchronization before execution. In this work, we leverage knowledge from the relative task space to facilitate a kinesthetic demonstration by guiding both end-effectors which is more human-like and intuitive way for performing bi-manual tasks. Our method utilizes the notion of virtual fixtures and inertia minimization in the null space of the task. The controller is experimentally validated in a bi-manual task which involves the drawing of a preset line on a workpiece utilizing two KUKA IIWA7 R800 robots. Results from ten participants were compared with a gravity compensation scheme demonstrating improved performance.

In this work, the utilization of Dirichlet (periodic sinc) base functions in DMPs for encoding periodic motions is proposed. By utilizing such kernels, we are able to analytically compute the minimum required number of kernels based only on the predefined accuracy, which is a hyperparameter that can be intuitively selected. The computation of the minimum required number of kernels is based on the frequency content of the demonstrated motion. The learning procedure essentially consists of the sampling of the demonstrated trajectory. The approach is validated through simulations and experiments with the KUKA LWR4+ robot, which show that utilizing the automatically calculated number of basis functions, the pre-defined accuracy is achieved by the proposed DMP model.

In this work, a novel Dynamic Movement Primitive (DMP) formulation is proposed which supports reversibility, i.e. backwards reproduction of a learned trajectory. Apart from sharing all favourable properties of the original DMP, decoupling the teaching of position and velocity profiles and bidirectional drivability along the encoded path are also supported. Original DMP have been extensively used for encoding and reproducing a desired motion pattern in several robotic applications. However, they lack reversibility, which is a useful and expedient property that can be leveraged in many scenarios. The proposed formulation is analyzed theoretically and its practical usefulness is showcased in an assembly by insertion experimental scenario.

In this work, the problem of human-robot collaborative object transfer to unknown target poses is addressed. The desired pattern of the end-effector pose trajectory to a known target pose is encoded using DMPs (Dynamic Movement Primitives). During transportation of the object to new unknown targets, a DMP-based reference model and an EKF (Extended Kalman Filter) for estimating the target pose and time duration of the human's intended motion is proposed. A stability analysis of the overall scheme is provided. Experiments using a Kuka LWR4+ robot equipped with an ATI sensor at its end-effector validate its efficacy with respect to the required human effort and compare it with an admittance control scheme.

Trajectory tracking in the orientation space utilizing unit quaternions yields non linear error dynamics as opposed to Cartesian position. In this work, we study trajectory tracking in the orientation space utilizing the most popular quaternion error representations and angular velocity errors. By selecting error functions carefully we show exponential convergence in a region of attraction containing large initial errors. We further show that under certain conditions frequently en-countered in practice, the formulation respecting the geometric characteristics of the quaternion manifold and its tangent space yields linear tracking dynamics allowing us to guarantee a desired tracking performance by gain selection without tuning. Simulation and experimental results are provided.

Kinesthetic teaching allows the direct skill transfer from the human to the robot through physical human-robot interaction. However, it is heavily affected by the robot’s dynamics and the control scheme utilized for the physical interaction. In this work, we aim at assisting the human-teacher by reducing her/his physical and cognitive load. To this aim, we propose a controller with virtual fixtures and inertia optimization for assisting kinesthetic teaching, exploiting knowledge of the task geometry and the robot redundancy. Experimental results utilizing a KUKA LWR4+ robot for the teaching of a brush painting motion on a curved surface validate the method and demonstrate its performance in comparison with a gravity compensation scheme and the utilization of virtual fixtures alone. The system is proved to be passive under the exertion of a human force.

In this work, the problem of cooperative human-robot manipulation of an object with large inertia is addressed, considering the availability of a kinematically controlled industrial robot. In particular, a variable admittance control scheme is proposed, where the damping is adjusted based on the power transmitted from the human to the robot, with the aim of minimizing the energy injected by the human while also allowing her/him to have control over the task. The proposed approach is evaluated via a human-in-the-loop setup and compared to a generic variable damping state-of-the-art method. The proposed approach is shown to achieve significant reduction of the human’s effort and minimization of unintended overshoots and oscillations, which may deteriorate the user’s feeling of control over the task.

In this work, we propose an augmentation to the Dynamic Movement Primitives (DMP) framework which allows the system to generalize to moving goals without the use of any known or approximation model for estimating the goal's motion. We aim to maintain the demonstrated velocity levels during the execution to the moving goal, generating motion profiles appropriate for human robot collaboration. The proposed method employs a modified version of a DMP, learned by a demonstration to a static goal, with adaptive temporal scaling in order to achieve reaching of the moving goal with the learned kinematic pattern. Only the current position and velocity of the goal are required. The goal's reaching error and its derivative is proved to converge to zero via contraction analysis. The theoretical results are verified by simulations and experiments on a KUKA LWR4+ robot.

Extracting a known target object from a pile of other objects in a cluttered environment is a challenging robotic manipulation task encountered in many robotic applications. In such conditions, the target object touches or is covered by adjacent obstacle objects, thus rendering traditional grasping techniques ineffective. In this paper, we propose a pushing policy aiming at singulating the target object from its surrounding clutter, by means of lateral pushing movements of both the neighboring objects and the target object until sufficient ’grasping room’ has been achieved. To achieve the above goal we employ reinforcement learning and particularly Deep Qlearning (DQN) to learn optimal push policies by trial and error. A novel Split DQN is proposed to improve the learning rate and increase the modularity of the algorithm. Experiments show that although learning is performed in a simulated environment the transfer of learned policies to a real environment is effective thanks to robust feature selection. Finally, we demonstrate that the modularity of the algorithm allows the addition of extra primitives without retraining the model from scratch.

This paper presents a teaching by demonstration method for contact tasks with periodic movement on planar surfaces of unknown pose. To learn the motion on the plane, we utilize frequency oscillators with periodic movement primitives and we propose modified adaptation rules along with an extraction method of the task's fundamental frequency by automatically discarding near-zero frequency components. Additionally, we utilize an online estimate of the normal vector to the plane, so that the robot is able to quickly adapt to rotated hinged surfaces such as a window or a door. Using the framework of progressive automation for compliance adaptation, the robot transitions seamlessly and bi-directionally between hand guidance and autonomous operation within few repetitions of the task. While the level of automation increases, a hybrid force/position controller is progressively engaged for the autonomous operation of the robot. Our methodology is verified experimentally in surfaces of different orientation, with the robot being able to adapt to surface orientation perturbations.

Over the last decades, Learning from Demonstration (LfD) has become a widely accepted solution for the problem of robot programming. According to LfD, the kinematic behavior is "taught" to the robot, based on a set of motion demonstrations performed by the human-teacher. The demonstrations can be either captured via kinesthetic teaching or external sensors, e.g., a camera. In this work, a controller for providing haptic cues of the robot's kinematic behavior to the human-teacher is proposed. Guidance is provided in procedures of kinesthetic coaching during inspection and partial modification of encoded motions. The proposed controller is based on an artificial potential field, designed to adjust the intensity of the haptic communication automatically according to the human intentions. The control scheme is proved to be passive with respect to robot's velocity and its effectiveness is experimentally evaluated in a KUKA LWR4+ robotic manipulator.

In this work we study the DMP spatial scaling in the Cartesian space. The DMP framework is claimed to have the ability to generalize learnt trajectories to new initial and goal positions, maintaining the desired kinematic pattern. However we show that the existing formulations present problems in trajectory spatial scaling when used in the Cartesian space for a wide variety of tasks and examine their cause. We then propose a novel formulation alleviating these problems. Trajectory generalization analysis, is performed by deriving the trajectory tracking dynamics. The proposed formulation is compared with the existing ones through simulations and experiments on a KUKA LWR 4+ robot.

In many industrial applications robot's motion has to be subjected to spatial constraints imposed by the geometry of the task, e.g. motion of the end-effector on a surface. Current learning by demonstration methods encode the motion either in the Cartesian space of the end-effector, or in the configuration space of the robot. In those cases, the spatial generalization of the motion does not guarantee that the motion will in any case respect the spatial constraints of the task, as no knowledge of those constraints is exploited. In this work, a novel approach for encoding a kinematic behavior is proposed, which takes advantage of such a knowledge and guarantees that the motion will, in any case, satisfy the spatial constraints and the motion pattern will not be distorted. The proposed approach is compared with respect to its ability for spatial generalization, to two different dynamical system based approaches implemented on the Cartesian space via experiments.

Snap-fit assemblies are widely used in the manufacturing of several product types, allowing part joining, while the parts remain unprocessed. The locking mechanism of a snap-fit is usually done within the object structure, not allowing visual identification of the successful process completion. Humans consider the forces developed between the two parts or the snapping sound, as an indication of success. This is difficult to realize in robotic assembly, and the process success is usually identified at a product quality control stage. The aim of this article is to migrate the human ability to identify a successful snap assembly to autonomous robotic assembly, via a machine learning framework, enabled by human-robot collaboration for rich data collection and labeling. The proposed framework allows learning while minimizing complexity, cost, and time. A generic feature set is proposed, which can produce good identification results in different snap assembly types. A feature transformation is also introduced that is fundamental for the real-time operation of the proposed framework and the identification of successful snap-assemblies. Three different objects are used to experimentally validate the approach using a KUKA LWR4+ robotic arm, resulting in high classification and real-time identification accuracy. Finally, a comparison with a model-based method is conducted.

In this article, a passive physical human-robot interaction (pHRI) controller is proposed to enhance pHRI performance in terms of precision, cognitive load, and user effort, in cases partial knowledge of the task is available. Partial knowledge refers to a subspace of SE(3) determined by the desired task, generally mixing both position and orientation variables, and is mathematically approximated by parametric expressions. The proposed scheme, which utilizes the notion of virtual constraints and the prescribed performance control methodology, is proved to be passive with respect to the interaction force, while guaranteeing constraint satisfaction in all cases. The control scheme is experimentally validated and compared with a dissipative control scheme utilizing a KUKA LWR4+ robot in a master-slave task; experiments also include an application to a robotic assembly case.

In this work, a passive physical human-robot interaction (pHRI) controller is proposed to intraoperatively ensure that sensitive tissues will not be damaged by the robot's tool. The proposed scheme uses the point cloud of the restricted region's surface as constraint definition and Artificial Potential fields for constraint enforcement. The controller is proven to be passive with respect to the interaction force and to guarantee constraint satisfaction in all cases. The proposed methodology is experimentally validated by the kinesthetic guidance of a KUKA LWR4+ robot's end-effector driving a virtual slave KUKA in the vicinity of a 3D point-cloud of a kidney and its adjacent vessels.

In this paper we propose a grasp strategy which exploits environmental contact for grasping domestic flat objects placed or hinged on support surfaces. The proposed grasp strategy considers the non-convex geometry of the object-surface combination, as this appears in objects like plates on tables or handles on cupboards. Following the fact that state-of-the-art grasp planners fail to produce candidate grasps for flat objects due to the environmental constraint of the support surface, this work utilizes compliant interaction of the hand with the support surface, inspired by human grasp strategies.

Classification methods are widely used in several types of applications and a lot of research works report highly accurate results on their ability to predict in unseen data. However, results are usually based on strong assumptions related to data preprocessing that might not hold in real world applications. The training set in practice can significantly differ to that of testing, especially when the classification process is carried out in real-time and the required preprocessing is not applicable without prior knowledge on the testing signals such as its length and amplitude. Sampling methods like sliding or additive window are usually employed, but not always resolve the problem that in many cases results in false positives. This work proposes an algorithm for real-time classification of signals with unknown length, based on a feature transformation that enables the classifier only when the signal's amplitude is within the expected event range. The proposed transformation can be used to generalize a classifier in similar data by only requiring knowledge of the expected event amplitude. The real-time performance of the proposed algorithm is evaluated in two industrial processes and its generalization ability in two novel (a synthetic and an industrial) data sets.

This work proposes the utilization of sinc functions as kernels of Dynamic Movement Primitives (DMP) models for encoding point-to-point kinematic behaviors. The proposed method presents a number of advantages with respect to the state of the art, as it (i) involves a simple learning technique, (ii) provides a method to determine the minimum required number of basis functions, based on the frequency content of the demonstrated motion and (iii) provides the ability to pre-define the reproduction accuracy of the learned behavior. The ability of the proposed model to accurately reproduce the behavior is demonstrated through simulations and experiments. Comparisons with the Gaussian-based DMP model show the proposed method's superiority in terms of computational complexity of learning and accuracy for a specific number of kernels.

This video features RAMCIP, a new service robot developed to provide proactive and discreet assistance to elderly with Mild Cognitive Impairments (MCI), supporting their daily activities at home. Starting with a thorough analysis of needs and requirements of the target population, the RAMCIP robot was developed as an integrated ensemble of advanced H/W and S/W components, realizing the robot skills of perception, cognition, safe navigation, grasping, manipulation, and human-robot communication, ample to operate in real, rather challenging domestic environments. The RAMCIP use-cases include proactive assistance provision to user's cooking, eating and medication activities, through discreet user monitoring and robot interventions by reminders and robotic manipulations., RAMCIP can bring the medicine, recognize fallen objects and electric appliance that has been forgotten turned on. It also recognizes the user walking in low-light conditions and turns on the light, as well as detects cases of emergency such as a fall. The robot provides also the user with cognitive training games and stimulates the user to contact with relatives through video-calls. Pilot trials of the RAMCIP robot have been performed in real homes of more than ten different users, in Barcelona, Spain; the video at hand exhibits the robot performing the target use cases.

Bimanual assembly of two parts require that a relative target pose is reached prior to the joining operation. Rather than utilizing one arm as a fixture for holding one of the parts while the other performs the assembly, motion generation in the relative end-effector frame is proposed that involves both arms. The proposed approach considers bimanual motion in a dynamic and uncertain environment addressing avoidance of collision with obstacles as well as the robot itself and the environment. Moreover, configurations that optimize the motion and force capabilities for the sucessful and efficient completion of the task are taken into account. A task priority strategy is adopted achieving online performance. Experimental results on the YuMi bimanual robot using the Stack-Of- Tasks hierarchical solver validate the performance of the proposed approach in a folding assembly task.

This work proposes a kinematic control method for human-robot handshake motions achieving fast motion synchronization given a preset internal robot handshake motion and a compliance level that reflects the robot's level of passiveness. The proposed method combines a non-linear dynamic system having an attractive limit cycle with an admittance controller and an adaptation mechanism so that interaction forces are minimized and a consensus oscillation is achieved between the engaging participants. The proposed method is validated by experimenting with a KUKA LWR4+ 7dof arm under various scenarios.

The rolling motion of a soft robotic fingertip is in this paper explicitly included in the control objectives together with the force/position regulation targets. A model based control law is proposed to linearize and decouple the system with respect to the force/position and sliding dynamics based on an appropriately defined task Jacobian. The controller is validated by simulations including rolling on a stationary surface and graspless manipulation of a flat object.

A human-inspired hand-over control strategy is proposed for the haptic interaction of two dual-fingered hands for the planar case. It is based on a grasp controller for an unknown object which achieves, via fingertip rolling, a stable grasp and a real object mass estimation. Object load transfer is receiver initiated, follows human evidence and involves awareness of the other hand's state based solely on local proprioceptive measurements. Simulation results illustrate the proposed approach.

In this work a modification of a Cartesian impedance controller is presented. The proposed controller guarantees compliance of the operated manipulator strictly within preselected spatial limits. Specifically, by introducing a nonlinear stiffness term into the control design, the manipulator is prevented from colliding with an explicitly defined hard boundary, like a human being or a fragile object. When operating in spatial regions sufficiently away from the boundary the manipulator behaves as if it were operated via a linear stiffness impedance controller, thus preserving in these regions its compliance and convergence characteristics. A comparative simulation with a linear stiffness impedance controller is performed to verify and clarify the proposed approach.

This paper proposes a controller for the stable grasp of an arbitrary-shaped object on the horizontal plane by two robotic fingers with rigid hemispherical fingertips. The controller stabilizes the grasp with optimal force angles and desired finger shaping determined through the choice of a control constant without requiring the utilization of any contact information regarding contact locations and contact angles or any estimates of them. Simulation results demonstrate the performance of the proposed controller and show its clear advantages with respect to other known control schemes.

Dexterity in robot hand object manipulation is irrevocably connected with achieving and maintaining rolling motion and contact at the fingertips. The problem of controlling a robotic fingertip for contact rolling is in this work addressed via the generation of a rolling motion trajectory and the application of a prescribed performance controller that can guarantee contact maintenance and a predefined fast convergence of the rolling tracking error to zero under any contact conditions. A simulation of a five degrees of freedom robot show excellent contact rolling performance even at cases of low friction while alternative controllers lead to contact sliding.

In this paper a novel type of tracking controller for flexible joint robots is proposed. Joint elasticity is considered unknown and may be time varying. Robot and motor dynamics are also considered unknown. The controller guarantees link position performance specifications that have been a-priori set utilizing full state feedback. Simulation on a two link flexible joint robot validate the efficiency of the proposed control approach.

In this paper a simple tracking controller for a variable stiffness joint is proposed. System dynamics is considered unknown. The controller guarantees link and stiffness motor position performance specifications that have been apriori set, utilizing full state feedback. Simulation results on the previously published CompAct-VSA joint validate the efficiency of the proposed control approach.

This paper proposes a PID type regulator that achieves not only the global asymptotic convergence of the robot joint velocities and position errors to zero but it also guarantees a prescribed performance for the position error transient that is independent of system constants and control parameters. The proportional term of the control input uses a transformed error (TP) which incorporates the desired performance function; given sufficiently high proportional and damping gains, the proposed TPID controller ensures the position error's prescribed performance irrespective of constant disturbances and choice of control gains. Control parameter selection is merely confined in achieving admissible input torques. Simulation results for a three dof spatial robot confirm the theoretical analysis and illustrate the robustness of the prescribed performance regulator in case of time-variant bounded disturbances.

A prescribed performance regulator for the generalized position of the robot arm endpoint in the task space is proposed. The control input which incorporates a transformed error guarantees a prescribed performance regarding the response of the endpoint generalized position error. The use of two different forms of this transformed error will be presented and compared. Mathematical proof of the controller's success in fulfilling the desired goals is given. A simulation of a three degrees of freedom robot is used to confirm the theoretical findings for both cases of the transformed error.

In this paper, we address unresolved issues in robot force/position tracking including the concurrent satisfaction of contact maintenance, lack of overshoot, desired speed of response, as well as accuracy level. The control objective is satisfied under uncertainties in the force deformation model and disturbances acting at the joints. The unknown nonlinearities that arise owing to the uncertainties in the force deformation model are approximated by a neural network linear in the weights and it is proven that the neural network approximation holds for all time irrespective of the magnitude of the modeling error, the disturbances, and the controller gains. Thus, the controller gains are easily selected, and potentially large neural network approximation errors as well as disturbances can be tolerated. Simulation results on a 6-DOF robot confirm the theoretical findings.

A control law is proposed that achieves predefined performance indices regarding the speed of response, the steady state and the allowed overshoot of the robot force/position tracking errors, ensuring no loss of contact of the robot end effector. The controller incorporates a transformed error, which includes the performance indices. The control objective is satisfied under parametric uncertainties in the robot dynamics and the elasticity model constant. Simulation results confirm the theoretical findings and compare the proposed controller with a conventional one.

This work deals with the problem of force/position trajectory tracking under uncertainties arising from surface position and orientation. A robotic finger with a soft hemispherical tip of uncertain compliance parameter is considered in contact with a rigid flat surface. A novel adaptive controller is designed using online estimates of the unknown parameters and is proved to achieve force and position tracking by ensuring the convergence of the estimated normal to the surface direction to its actual value. The performance of the proposed controller is demonstrated by a simulation example.

This work refers to the control of the contact of a simple one degree-of-freedom (dof) robot with a compliant surface using ideas from hybrid stability theory. The robot is modeled as a switched system. A position controller is used for the free motion and a force controller for the contact task. The goal is to stabilize the robot in contact with the environment and exert a desired force. The surface is modeled by an unknown, nonlinear elasticity function. By considering typical candidate Lyapunov functions for each of the two discrete system states, conditions on feedback gains are derived that guarantee Lyapunov asymptotic stability of the hybrid task. A sufficient condition that ensures that the robot will remain in contact with the surface is derived involving the system's velocity at the time of contact

This work proposes an adaptive control law for the force position regulation problem under surface kinematic uncertainties. A compliant contact with friction is considered. The control law achieves exact regulation of force and position along the surface tangent by identifying the surface slope. The asymptotic stability of the closed loop system equilibrium point is proved in a local sense and is demonstrated by a simulation example

This paper refers to the problem of force regulation for a robot finger with soft tip in contact with a rigid surface with unknown geometrical characteristics. A simple adaptive controller is employed in order to cope with surface kinematic uncertainties and the asymptotic stability of the force error is shown for the spatial case. Simulation results demonstrate the controller performance.

This paper discusses the problem of modeling a flexible object manipulated by a dual arm system with rolling contacts. The dynamic model of the flexible object is simplified by considering as flexible coordinates the maximum deformation at each contact and assuming that the mass transfer due to deformation can be modeled by two aggregated masses that are deformation dependent and move with the maximum deformation velocity. The overall system kinematics and dynamics are derived. Simple feedback control solutions for stable grasp and regulation of the object's position and orientation proposed for rigid object control are used and shown to achieve the desired state. Simulation results are presented.

This work refers to a planar system of two spherical elastic fingertips grasping a rigid polygonal object under rolling contact constraints at equilibrium. A contact motion model is proposed for elastic-rolling fingertips based on previously reported experimental findings on the rolling distance for a variety of soft materials. The equilibrium conditions are derived from the system dynamics and depend on the deformation and on the kind of elastic material with regard to the fingertip's rolling distance characteristics. Equilibrium conditions of the three contacted bodies (elastic fingertips-object) show that in general no-collinear interaction forces act on the object's surface and they correspond to the equilibrium of an "internal object" hold by two point contacts with friction.

The control of object stable grasping and pose regulation by two robotic fingers with soft rolling contacts is considered. The motion of the dual fingers is confined to the horizontal plane and is not affected by the gravity force. A simple feedback control law is proposed with the task to achieve a desired normal contact force and appropriate tangential forces to ensure a dynamically stable grasp. The nonlinear asymptotic stability of the closed loop system is proved. Simulation results for two 3-DOF fingers manipulating a rectangular object from an initial state to a final stable grasp configuration demonstrate the effectiveness of the controller.

In this paper dealing with the problem of stable grasping of a rigid and rectangular object with two robotic fingers with soft tips, we consider the case of rolling fingertips under a soft contact motion model which leads to non-holonomic constraints even for the planar case while taking into consideration the gravity effect. The analytical hand-object system kinematics and dynamics are derived and a feedback controller is proposed. The controller compensates finger gravity and superimposes control signals that realize a stable grasp configuration and compensate for the object gravity forces while driving the object at the upright position. The controller is shown to achieve asymptotic convergence to the desired upright object position at a stable grasp configuration. Simulation results are presented confirming the theoretical findings.

We consider the position and force regulation problem for a soft tip robot finger in contact with a rigid surface under kinematic uncertainties concerning the contact point location and the direction of free movement. The reproducing force is related to the displacement through a non-linear function whose characteristics are unknown but both the actual displacement and force can be directly measured. An adaptive controller is proposed and the asymptotic stability of the force error and estimated position error under dynamic and kinematic uncertainties is shown for the planar case. Simulation results for a 3-degrees-of-freedom planar robotic finger are presented.

This paper deals with the concept, design, construction and experimental results of a gripper dedicated to the grasping of nonrigid material pieces out of a bundle. Based on a number of functional requirements a gripper is proposed with two passively rotating fingers and its performance is assessed experimentally in the case of grasping furs.

We consider the problem of force regulation for the physical interaction between the soft tip of a robot finger and a rigid object under kinematic uncertainties. It is assumed that the nonlinear characteristics of the reproducing force and the finger dynamic parameters are unknown and that the kinematic uncertainties arise from both uncertain robot finger kinematics and uncertain rigid object geometry. An adaptive controller is proposed and the asymptotic stability for the force regulation problem under dynamic and kinematic uncertainties is shown for the planar case. Simulation results for a 3-degrees-of-freedom planar robotic finger are presented.

The stability of the hybrid position/force control for manipulators is examined using Lyapunov's direct method for decentralized controllers which are in the form of linear feedback of projected joint errors. This paper aims in clarifying the kinematic instabilities that have been reported to exist for the original hybrid control scheme using the Jacobian inverse for mapping cartesian errors to joint errors, and which have been later remedied in a control scheme using the Jacobian pseudoinverse. Stability conditions demonstrate the importance of the joint error projection matrix and the contact state. Experimental results for a 2-degrees-of-freedom planar manipulator using a PUMA 560 are given both in free space and in contact with a stiff wall.

This paper presents a robot system incorporating vision and force/torque sensing for handling of flat textile materials. Experimentation is used to draw conclusions concerning the performance of standard arms and sensing techniques. Also requirements of such systems are discussed.