Fine dexterous manipulation requires reactive control based on rich sensing of manipulator-object interactions. Tactile sensing arrays provide rich contact information across the manipulator’s surface. However their implementation faces two main challenges: accurate force estimation across complex surfaces like robotic hands, and integration of these estimates into reactive control loops. We present a data-efficient calibration method that enables rapid, full-array force estimation across varying geometries, providing online feedback that accounts for non-linearities and deformation effects. Our force estimation model serves as feedback in an online closed-loop control system for interaction force tracking. The accuracy of our estimates is independently validated against measurements from a calibrated force-torque sensor. Using the Allegro Hand equipped with Xela uSkin sensors, we demonstrate precise force application through an admittance control loop running at 100Hz, achieving up to 0.12±0.08 [N] error margin—results that show promising potential for dexterous manipulation.

In this paper, we propose an algorithm for computing a smoothed version of the distance between two objects. As opposed to the traditional Euclidean distance between two objects, which may not be differentiable, this smoothed distance is guaranteed to be differentiable. Differentiability is an important property in many applications, in particular in robotics, in which obstacle-avoidance schemes often rely on the derivative/Jacobian of the distance between two objects. We prove mathematical properties of this smoothed distance and of the algorithm for computing it, and show its applicability in robotics by applying it to a second order kinematic control framework, also proposed in this paper. The control framework using smooth distances was successfully implemented on a 7 DOF manipulator.

This article presents a new kinematic control paradigm for redundant robots based on optimization. The general approach takes into account convex objective functions with inequality constraints and a specific equality constraint resulting from a Lyapunov function, which ensures closed-loop stability by design. Furthermore, we tackle an important particular case by using a convex combination of quadratic and l1-norm objective functions, making possible for the designer to choose different degrees of sparseness and smoothness in the control inputs. We provide a pseudoanalytical solution to this optimization problem and validate the approach by controlling the center of mass of the humanoid robot HOAP3.

The paper describes a control framework using a capacitive skin for advanced and safe human-robot interactions. The skin consists of high density capacitive sensors which are able to detect objects at close distance. We propose a multitask kinematic control scheme which manages at once the control task of the end-effector and the obstacle avoidance task by using the capacitive skin measurements. To solve the multiobjective problem, a Weight-Prioritized solution based on a QP formalism is adopted. The robustness of the kinematic controller is enhanced by adding a local joint reconfiguration when the robot moves close to a singularity. Experimental results on manipulator arm equipped with the capacitive sensors are presented in realistic situations of human-robot interactions.

Knowledge of the mass and inertial parameters of a humanoid robot is crucial for the development of model-based controller and motion planning in dynamics situation. Parameters are usually provided from Computer Aided Design (CAD) data and thus inaccurate specially if the robot is modified over time. In this paper, a practical method consisting of hanging a humanoid robot to a fix force sensor to perform its dynamic identification is proposed. This allows, contrary to the literature, to generate very exciting and dynamic motions to identify most of the elements of the inertia tensors in a reduced amount of time. This procedure transforms an instable floating base legged humanoid robot to a safe fix base tree structure robot which makes easier to generate optimal exciting motions. Because of a better excitation the overall trajectory lasts for less than a minute. The method was experimentally validated with a HOAP3 humanoid robot and using a 6-axis force sensor. A reduction of 3 times in average of the RMS difference between measured external reaction forces and moments and their estimates from CAD data was obtained with a single minute of optimal exciting motions.

To make production lines more flexible, dual-arm robots are good candidates to be deployed in autonomous assembly units. In this paper, we propose a sparse kinematic control strategy, that minimizes the number of joints actuated for a coordinated task between two arms. The control strategy is based on a hierarchical sparse QP architecture. We present experimental results that highlight the capability of this architecture to produce sparser motions (for an assembly task) than those obtained with standard controllers.

Deforming a cable to a desired (reachable) shape is a trivial task for a human to do without even knowing the internal dynamics of the cable. This paper proposes a framework for cable shapes manipulation with multiple robot manipulators. The shape is parameterized by a Fourier series. A local deformation model of the cable is estimated on-line with the shape parameters. Using the deformation model, a velocity control law is applied on the robot to deform the cable into the desired shape. Experiments on a dual-arm manipulator are conducted to validate the framework.

In this paper, we present a control strategy that enables intuitive physical human-robot collaboration with mobile manipulators equipped with an omnidirectional base. When interacting with a human operator, intuitiveness of operation is a major concern. To this end, we propose a redundancy solution that allows the mobile base to be fixed when working locally and moves it only when the robot approaches a set of constraints. These constraints include distance to singular poses, minimum of manipulability and distance to objects and angular deviation. Experimental results with a Kuka LWR4 arm mounted on a Neobotix MPO700 mobile base validate the proposed approach.

In human-robot interaction, the robot must behave safely, especially when an operator is present in its workspace. Even higher safety levels must be attained when physical contact occurs between the two. To this end, standards such as the ISO10218 define the requirements for a robot to be considered safe for interaction with human operators in an industrial environment. In this paper, we propose an adaptive damping controller that fulfills the ISO10218 requirements by limiting the tool velocity, power and contact force online (and only when needed). The controller is experimentally validated on a hand-arm robotic system, in a mock-up collaborative application. For the hand, safe interaction is enhanced by using tactile sensing, both to regulate grasp forces and to provide an intuitive interface for the operator.

Non-holonomic omnidirectional mobile robots have higher load carrying capacity than their holonomic counterparts. Once the steer joint configuration is initialized, they can perform arbitrarily complex three-dimensional trajectories in the plane of motion and, as such, are more suitable for industrial contexts. However, their kinematic model presents representational and structural singularities, solutions to which must respect actuator performance limits. Recent research efforts have provided either simple restricting of the velocity space (among which few considered hardware limits) or complex non-restricting (no hardware limits considered) solutions. Most of these efforts are providing solutions at the kinematic control level. Instead, here we propose both a representational singularity free kinematic model, and a simple numeric treatment for the kinematic singularity. We further provide a method to tune the latter, to respect the actuator acceleration limits. Thanks to its steer rate damping behavior, the method can be further extended, to respect joint limits. Another benefit is the treatment of the singularity at the level of the kinematic model, which enhances real time capabilities. The developed method has been tested successfully on the Neobotix-MPO700 mobile robot and shown superior results as compared to the embedded controller.

Knowledge of the mass and inertial parameters of a humanoid robot or a human being is crucial for the development of model-based control, as well as for monitoring the rehabilitation process. These parameters are also important for obtaining realistic simulations in the field of motion planning and human motor control. For robots, they are often provided by computer-aided design data, while averaged anthropometric table values are often used for human subjects. The unit/subject-specific inertial parameters can be identified by using the external wrench caused by the ground reaction. However, the identification accuracy intrinsically depends on the excitation properties of the recorded motion. In this paper, a new method for obtaining optimal excitation motions is proposed. This method is based on the identification model of legged systems and on optimization processes to generate excitation motions while handling mechanical constraints. A pragmatic decomposition of this problem, the use of a new excitation criterion, and a quadratic program to identify inertial parameters are proposed. The method has been experimentally validated onto an HOAP-3 humanoid robot and with one human subject.

A human's center of mass (CoM) trajectory is useful to evaluate the dynamic stability during daily life activities such as walking and standing up. To estimate the subject-specific CoM position in the home environment, we make use of a statically equivalent serial chain (SESC) developed with a portable measurement system. In this paper we implement a constrained Kalman filter to achieve an online estimation of the SESC parameters while accounting for the human body's bilateral symmetry. This results in constraining SESC parameters to be consistent with the human skeletal model used. The proposed identification method can inform the subject or the therapist, in real-time, about the quality of the on-going CoM estimation. This information can be helpful to reduce the identification time and establish a personalized protocol. A Kinect is used as a markerless motion capture system for measuring limb orientations while the Wii board is used to measure the subject's center of pressure (CoP) during the identification phase. CoP measurements and Kinect data were recorded for four able-bodied subjects. The recorded data was then given to the proposed recursive algorithm to identify the parameters of the SESC online. A cross-validation test was performed to verify the identification performance. The results for these subjects are shown and discussed.

For intuitive human-robot collaboration, the robot must quickly adapt to the human behavior. To this end, we propose a multimodal sensor-based control framework, enabling a robot to recognize human intention, and consequently adapt its control strategy. Our approach is marker-less, relies on a Kinect and on an on-board camera, and is based on a unified task formalism. Moreover, we validate it in a mock-up industrial scenario, where human and robot must collaborate to insert screws in a flank.

In this study, we endeavor to better understand the human motor control system in order to help transposing some of its features onto humanoid robots. The postural coordination task investigated is related to an experimental paradigm that consists in visual target tracking task while balancing. We want to test whether the human biomechanical responses, namely phase / antiphase coordination mode transition, as exhibited during the actual experiments can be modeled by a linearized double inverted pendulum and parallel independent PD feedback control loops. Remarkably, these loops implement joint space control using cartesian task space variables. Furthermore, we want to see how the feedback control gains given by an optimization procedure scale w.r.t frequency or target motion magnitude. A closed-loop synthesis is developed that consists in minimizing a minimum torque criterion under both balance and task constraints. We show that the optimal feedback control gains obtained yield model responses consistent with the literature. In a second part, we implement a gain-scheduling approach where control gains values are predicted via interpolation. Finally, our approach implements a controller capable of achieving the task even when the frequency of the target motion varies over time.

In this paper, we propose a method that allows a human to perform complex manipulation tasks jointly with a robotic partner. To that end, the robot has a library of assistances that it can provide for helping the human partner during a priori unknown collaborative tasks. According to the haptic cues naturally transmitted by the human partner, the robot selects on-line the suitable assistance for the current intended collaborative motion. Based on the naive bayes classifier and the Matthew Correlation Coefficient, the parameters of the decision-making are automatically tuned. An experiment on a real arm manipulator is provided to validate the proposed approach.

Center of mass (CoM) estimation can be used to evaluate human stability during rehabilitation. A personalized estimation can be obtained using the serial equivalent static chain (SESC) method, calibrated using a series of static postures. The estimation accuracy is dependent on the number and quality of poses used during calibration. Currently, this limits the method's application to unimpaired individuals. We present a preliminary study of a SESC identified in a multi-contact scenario during a Sit-to-Stand task. Stanford's SAI (Simulation and Active Interface) platform was used to emulate motion and predict relevant reaction forces. The CoM estimation obtained is valid for motions similar to those used during identification. Using a three-dimensional model, the estimated mean error was less than 26 millimetres for a Sit-to-Stand task involving displacements along all axes. As such, personalized CoM estimation can be available for patients with a limited range of whole body motion.

We aim at addressing in this paper the issue of non-verbal communication involved in a human-robot interaction. We first propose a taxonomy of assistance that an operator may expect from a partner during a long object manipulation task. Then, this paper presents a method to detect which kind of robot assistance is needed, thanks to the decoding of haptic cues conveyed by the operator. An experimental study on thirty subjects moving a long object together with a robot that is providing an assistance is introduced. A statistical analysis highlights the relationships between haptic measures and intentions of motion. This analysis shows that wrench measurements constitute an incomplete information to detect the operator's intention of motion.

Center of mass (CoM) trajectory is important during standing and walking since it can be used as an index for stability and fall prediction. Unfortunately current methods for CoM estimation require the use of specialized equipment (such as motion capture and force platforms) in controlled environments. This paper aims at applying the statically equivalent serial chain (SESC) method to obtain CoM position using widely available and portable hardware; a Microsoft's Kinect and a Nintendo's Wii balance board. During identification, CoM is approximated by CoP measurements and the virtual chain is created for able-bodied subjects. The result demostrates that the SESC method can be applied outside the laboratory environment using a Kinect. Cross-validation of the identified model was performed to evaluate the accuracy of the method. Results obtained of five subjects are shown and discussed.

In this paper we propose a novel approach for interactive manipulation involving a human and a humanoid. The interaction is represented by means of the relative configuration between the human's and the robot's hands. Based on this principle and a set of mathematical tools also proposed in the paper, a large set of tasks can be represented intuitively. We also introduce the concept of simultaneous handling using mirrored movements, where the human controls the robot and simultaneously interacts with it by means of a common manipulated object. Illustrative experiments are performed to validate the proposed techniques.

In this paper we present a novel approach in human/robot collaboration, where the robot controls not only its arm but also the human's by means of functional electrical stimulation (FES). The task is described by using the cooperative dual task-space approach, providing a considerable degree of invariance with respect to the morphology of the agents involved. Experimental results in a “ball in the hoop” task using healthy blindfolded subjects show the validity of the approach and encourages further experiments with impaired subjects, for instance hemiplegic or quadriplegic patients.

This paper introduces effective numerical methods for the planning and fast replanning of safe motions to ensure the safety, balance, and integrity of humanoid robots over the whole motion duration. Our safe methods do not depend on, nor are connected to, any type of modeling or constraints. To plan safe motions, certain constraints have to be satisfied over a continuous interval of time. Classical methods revert to time-grid discretization, which can be risky for the robot. We introduce a hybrid method to plan safe motions, which combines a classical unsafe method with a verification step that checks constraint violation and computes excess by the usage of interval analysis. When the robot meets unexpected situations, it has to replan a new motion, which is often too time consuming. Hence, we introduce a new method to rapidly replan safe motions, i.e., in less than 2 s CPU time. It computes offline feasible subsets in the vicinity of safe motions and finds online a solution in these subsets without actually recomputing the nonlinear constraints. Our methods are validated by the use the HOAP-3 robot, where the motions are run with no balance controller.

We propose a set of control strategies for performing two arm manipulation with the goal of simplifying the task definition. In order to develop these strategies we propose a new representation, derived from the cooperative task-space, in the dual quaternion domain. The result is a compact and “singularity free” representation for two arm systems, named cooperative dual task-space. All the proposed control strategies share the same general scheme and are derived by using an analytical approach. Moreover, the mathematical treatment is given in a coherent and systematic fashion, and thus other strategies may be derived using the same argument. Experimental results show the effectiveness and usefulness of the cooperative dual task-space framework and the proposed control strategies.

We propose in this paper a new concept of unified position/orientation control of robot manipulator by describing the end-effector motion as a dual quaternion involving both translation and rotation. The development of the forward kinematic model and Jacobian matrix in dual quaternion space is detailed as well as the stability of the controller. At last, simulation and experimental results highlight the efficiency and performance of this controller.

Motion databasing is an important topic in robotics research. Humanoid robots have a large number of degrees of freedom and their motions have to satisfy a set of constraints (balance, maximal joint torque velocity and angle values). Thus motion planning cannot efficiently be done on-line. The computation of optimal motions is performed off-line to create databases that transform the problem of large computation time into a problem of large memory space. Motion planning can be seen as a Semi-Infinite Programming problem (SIP) since it involves a finite number of variables over an infinite set of constraints. Most methods solve the SIP problem by transforming it into a finite programming one using a discretization over a prescribed grid. We show that this approach is risky because it can lead to motions which may violate one or several constraints. Then we introduce our new method for planning safe motions. It uses Interval Analysis techniques in order to achieve a safe discretization of the constraints. We show how to implement this method and use it with state-of-the-art constrained optimization packages. Then, we illustrate its capabilities for planning safe motions dedicated to the HOAP-3 humanoid robot.

This paper models recent data in the field of postural coordination showing the existence of self-organized postural states, and transition between them, underlying suprapostural tracking movements. The proposed closed-loop controller captures the complex postural behaviors observed in humans and can be used to implement efficient and simple balance control principles in humanoids.

Optimal motions are usually used as joint reference trajectories for repetitive or complex motions. In the case of soccer robots, the kicking motion is usually a benchmark motion, computed off-line, without taking into account the current position of the robot or the direction of the goal. Moreover, robots must react quickly to any situation, even if not expected, and cannot spend time to generate a new optimal motion by the classical way. Therefore, we propose a new method for fast motion re-planning based on an off-line computation of a feasible sub-set of the motion parameters, using Interval Analysis.

The use of wireless network signal strength proposed to estimate the relative position between vehicles in a robot formation set. We propose a relative leader-follower model which allows us to use the reception signal strength as control variable for robot stabilization in the formation. Vehicle sensors consist of wheel speed and steering angle, the WiFi data consist of reception signal strength (RSS) and the angle of the maximal RSS with respect to the robot orientation. A nonholonomic nonlinear model vehicle is considered, due to this nonlinearities an extended Kalman filter EKF is used. Simulation and experimental results of the proposed estimation strategy are presented.

This paper addresses the problem of restoring standing in paralegia via functional electrical stimulation (FES) and investigates the relashionship between body posture and voluntary upper-body movements. A methodology is presented for upper-body posture estimation in the sagittal plane from force and torque measurements exerted on handles during human standing, in the hypothesis of quasi-static equilibrium. The method consists in setting up constraints related to the geometric equations and the hand-handle interaction. All measured quantities are subject to an uncertainty assumed unknown but bounded. The set membership estimation problem is solved via interval analysis. Guaranteed uncertainty bounds are computed for the estimated postures. The methodology is validated experimentally with spinal cord injured patients with lesions between T5 and T12. Possible applications of the developed methodology are lower limbs function rehabilitation within clinical centers, walk assistance and independent mobility for spinal cord injured patients.

This work proposes a control architecture for the consistent motions of a group of nonholonomic robotic vehicles. We present a decentralized control strategy that permits each vehicle to autonomously compute an optimal trajectory by using only locally generated information. In addition, to reach the desired positions in the robot formation, each robot follows a trajectory that is adapted thanks to reactive terms incorporated in the path planning process. This allows the robot to avoid obstacles and to maintain communication with the rest of the formation. We provide the proof of the convergence of this algorithm. Simulation results validate and highlight the efficiency and relevance of this method.

The main challenge that we face when applying functional electrical stimulation (FES) to paralyzed lower limbs is to avoid hyperstimulation and to defer the muscular fatigue as much as possible. FES is used to excite paralyzed muscles that are under lesions and consequently no more controlled by paraplegic patients. We aimed in this study to compute the needed patterns stimulation necessary to perform a desired given motion of the knee joint. We coupled the exact input output feedback linearization with a model predictive controller (MPC). This latter enables us to incorporate explicitly constraints on inputs, outputs and system states. Internal dynamics stability was mathematically proved and MPC performances were compared to a classical pole placement controller in terms of robustness, stability and finite time convergence.

International audience

Functional electrical stimulation (FES) is used to excite paralysed muscles that would otherwise be uncontrollable by paraplegic patients. Consequently, the patient could recover partially some of lower limb functions improving the cardiovascular system, increasing oxygen uptake and bettering the whole quality of life. In this paper, we apply a control design based on a higher order sliding mode to a complex physio-mathematical muscle model. This model is based on macroscopic Hill and microscopic Huxley concepts. The main goal concerns the prediction of the needed pattern stimulation (current and pulse width), which will extend the overall performances and defer the muscle fatigue as much as possible. The controller is mathematically computed and shown to provide satisfactory stability and tracking errors. Its efficiency is illustrated with the control of the knee joint angle under a co-contraction approach.

For the teleoperation of a robot over an IP network, we must deal with the variable network delays as well as with events that can occur during the teleoperation. In this paper both control and architectural aspects are exposed. We first present a stable remote position/force control scheme for manipulator robot, based on a delay regulator and Smith's predictor principle. Some experiments show the improvement obtained by this method. However to face very important network delay variations and possible connection rupture, we designed a supervised delay regulator based architecture. This architecture, embedded on a mobile robot, is presented and results of experiments are also given and explained. All these experiments, performed on long distance (Mexico-France: /spl sim/8000 km), show the improvement obtained on both themes, that conjointly enhances and secures the teleoperation through the Internet.

The main goal of this paper is to define, study and analyse a remote control architecture for a set of nonholonomic robotic vehicles. This project (ACCORD) gathers three laboratories and the French Army Research Office. Each of these laboratories deals with a part of this multidisciplinary project which includes coordinated control, the control architecture, control with time delay and monitoring of the wireless network. In this paper, we propose to present the whole goal of this project including the basis experimental setup developed to validate our control algorithm. Secondly, we focus on a new decentralized control strategy that uses the leader-follower principle. The originality of this paper stems from the use of the signal level of the wireless connection as a control vector. Indeed, each vehicle is fitted with two wireless devices. One of them is equipped with a sector antenna fitted on a DC-motor to track the direction of better receiving level. Thus, it allows us to know the relative angular position of the follower pointing out the leader. By using the wireless technology as sensor instead of vision for instance, allows a longer distance of coordinated control loop between each vehicle (approx. 100 m) even if the GPS information is not available.

The new generation of implanted neuroprostheses allows muscles to be controlled with fine accuracy, high selectivity and the repeatability of the muscle's response can be achieved. Thus, the closed loop control of such systems becomes possible. The SUAW project succeeded in the implantation of an advanced neuroprosthetic device on two patients, but the movement generation remains open loop and is tuned empirically. Nevertheless, the good results obtained give us the opportunity to envisage the system evolves towards closed loop control and automatic synthesis of the stimulation patterns generating the desired movement. To achieve this goal, some preliminary researches have to be carried out, beginning with a specific modelling that can be used in the context of functional electrical stimulation (FES). The main issues concern muscle modelling including the interaction with the skeleton, fatigue, FES parameters as inputs, and the identification of dynamic parameters, and afterwards, the motion synthesis and the closed loop control based on this model. Besides, the scientific approach is the same as in robotics so that the theoretical tools used in the control theory are the same and directly applicable. This paper describes the results obtained in the previous project SUAW and how we attempt, through the new project DEMAR, to enhance the global performances of the system.

Long distance teleoperation over asynchronous transmission links makes many classical teleoperation schemes unstable. The use of this kind of media involves varying transmission delays that may become prohibitive even at short distance. However, they are standardized, cheap and widespread over the planet. The paper presents our latest works on the improvement of teleoperation loops, by providing a low-level architecture which permits the use of classical teleoperation controls over asynchronous digital links.

In order to perform complex coordinated tasks with a two-arm robot, some kind of robust hybrid position/force control is necessary. As a first step towards such a control scheme, the authors present mainly the results they have obtained in simulation with a single-arm robot. They have developed a simulator which allows one to test various control algorithms. In particular, they have tested a variable structure system for the hybrid control of a Puma robot. Several experimental results are presented in this case. Controlled variables for a two-arm system are also proposed.>