This paper deals with the design of a robotic assistant for the transport of large and fragile objects. We propose a new collaborative robotic controller that fulfills the main requirements of co-transportation tasks of large and fragile objects: to execute any trajectory in a collaborative mode while minimizing the stress applied on the object by both partners in order to avoid damaging it. This controller prevents the robot from applying torques on the object while maintaining a desired orientation of the object along the transport trajectory in order to follow the operator. An original feature of our approach is to care about torques applied by both partners (not only by operator) during any co-manipulation trajectory execution. It leads to a novel outcome: the minimization of stress applied by both partners on a large and fragile object during its transport on any trajectory. We demonstrate the effectiveness of this approach in a collaborative transportation task.

Implementing an intuitive control law for an upper-limb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to assist carrying an unknown load without using force sensors or specific handle. The method is based on user's intentions estimated through a wireless EMG armband allowing movement direction and intensity estimation along 1 Degree of Freedom. This control law aimed to behave like a gravity compensation except that the mass of the load does not need to be known. The proposed approach was tested on 10 participants during a lifting task with a single Degree of Freedom upper-limb exoskeleton. Participants performed it in three different conditions : without assistance, with an exact gravity compensation and with the proposed method based on EMG armband. The evaluation of the efficiency of the assistance was based on EMG signals captured on seven muscles (objective indicator) and a questionnaire (subjective indicator). Results showed a statically significant reduction of mean activity of the biceps, erector spinae and deltoid by 20%±14%, 18%±12% and 25% ± 16% respectively while comparing the proposed method with no assistance. In addition, similar muscle activities were found both in the proposed method and the traditional gravity compensation. Subjective evaluation showed better precision, efficiency and responsiveness of the proposed method compared to the traditional one.

Establishing a symbiotic relationship between a human and a exoskeleton is the end goal in many applications in order to provide benefits to the user. However, the literature focusing on the human side of human-exoskeleton interaction has remained less exhaustive than the literature focusing on the design (hardware/software) of the exoskeleton device itself. It is, though, essential to understand how a human adapts his motor control when interacting with an exoskeleton. Motor adaptation is an implicit process carried out by the central nervous system when the body encounters a perturbation, a paradigm that has been extensively studied in the field of human motor control research. When wearing an exoskeleton, even “as-transparent-as-possible”, contact/interaction forces may impact well-known motor control laws in a way that may be detrimental to the user, and even compromise usability in real applications. The present paper investigates how interaction with a backdrivable upper-limb exoskeleton (ABLE) set in “transparent” mode of control affects the kinematics/dynamics of human movement in a simple task. We find that important motor control features are preserved when moving with ABLE but an overall movement slowness occurs, likely as a response to increased inertia according to optimal control simulations. Such a human motor control approach illustrates one possible way to assess the degree of symbiosis between human and exoskeleton, i.e. by grounding on well-known findings in motor control research.

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.

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.

In the field of comanipulation (i.e. a man and a robot sharing the same task), force amplification is an interesting function that can be achieved by using two force sensors. This technique is known in the literature but little attention has been paid so far to stability/passivity properties. We will explain how to deal with passivity based stability criteria, and point out performance limitations of such a control in case of noncollocated and bandwidth limited force sensors. Theoretical, as well as experimental results will be presented.

We focus on comanipulation, i.e. manipulation of an object simultaneously held by a robot and a human operator. In this domain, a major difficulty is raised by significant variations of human dynamics, which depend not only on the arm posture, but also on the muscular activity (muscular co-contraction) and more generally on the type of task being performed: fine positioning, gross and rapid movements, repeated movements, etc. An ideal comanipulation system should be able of adapting its behavior to the operator's functional intention, resulting in an intuitive assisting device. Toward this goal, we present in this paper first results of our research aimed at developing an instrumented handle mounted on a robot end-effector and held by an operator, that can be used for estimating the grasping force and for adapting the robot controller accordingly. We show first experimental evidences that changes in the grasping force drastically affect the robot controller performances. We thus propose a handle design and a gain scheduling strategy that result in a robot behavior adequate for any kind of grasps. This solution is successfully experimented with a 1 degree of freedom robot under largely variable comanipulation conditions, exhibiting a stable and efficiently adaptive behavior.

This paper proposes a novel planning algorithm for image based visual servoing (IBVS). IBVS is conceptually simple but prone to fail in case of large displacements. Hence, the objective is to provide paths for reference points in the image plane, such that the camera is steered from initial position to desired position. The novel scheme guarantees that the camera is always oriented such that the object of interest is in the field of view and that any reference point set corresponds indeed to a feasible camera pose. No camera calibration is necessary. As a novelty, this work is planning a camera motion, hence yielding compact paths in robot work space. The proposed planner is compared to others by experiments.

Using an industrial manipulator as a slave arm in a force-feedback teleoperation system requires to us deal with its poor backdrivability. Improving transparency of such a system, using a wrist force/torque sensor, is known as an effective and robust method. Another solution could be to use a base force/torque sensor. The paper focuses on the feasibility of such an approach in a force-feedback teleoperation context. It is then compared to a wrist force/torque sensor approach.