A Conformable Force/Tactile Skin for Physical Human–Robot Interaction

Cirillo, Andrea; Ficuciello, Fanny; Natale, Ciro; Pirozzi, Salvatore; Villani, Luigi

doi:10.1109/lra.2015.2505061

Cited by 135 publications

(67 citation statements)

References 19 publications

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“…In the experimental setup, the tactile skin developed within the SAPHARI Project [40] has been fixed on the end effector of the KUKA robotic arm, as shown in Figs. 5a, 5b.…”

Section: ) Tactile Sensing Setupmentioning

confidence: 99%

A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration

Falco

Natale

et al. 2019

IEEE Trans. Robot.

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In this work, we introduce the problem of crossmodal visuo-tactile object recognition with robotic active exploration. With this term, we mean that the robot observes a set of objects with visual perception and, later on, it is able to recognize such objects only with tactile exploration, without having touched any object before. Using a machine learning terminology, in our application we have a visual training set and a tactile test set, or vice versa. To tackle this problem, we propose an approach constituted by four steps: finding a visuotactile common representation, defining a suitable set of features, transferring the features across the domains, and classifying the objects. We show the results of our approach using a set of 15 objects, collecting 40 visual examples and five tactile examples for each object. The proposed approach achieves an accuracy of 94.7%, which is comparable with the accuracy of the monomodal case, i.e., when using visual data both as training set and test set. Moreover, it performs well compared to the human ability, which we have roughly estimated carrying out an experiment with ten participants.

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“…In the experimental setup, the tactile skin developed within the SAPHARI Project [40] has been fixed on the end effector of the KUKA robotic arm, as shown in Figs. 5a, 5b.…”

Section: ) Tactile Sensing Setupmentioning

confidence: 99%

A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration

Falco

Natale

et al. 2019

IEEE Trans. Robot.

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“…55 In order to execute a wider range of activities involving Human-Robot Collaboration (HRC), physical contacts should not be limited in advance to a designated tool at the end-effector level, but rather whole-arm manipulation conditions should be considered. This raises the additional issue of reconstructing the exchanged forces at generic contact points along the robot structure, either by 60 measuring them (e.g., using tactile sensitive skin in different locations [17]) or by estimating them in an indirect way, possibly combining model-based methods with other less invasive external sensors.…”

Section: Introductionmentioning

confidence: 99%

A model-based residual approach for human-robot collaboration during manual polishing operations

2018

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A fully robotized polishing of metallic surfaces may be insufficient in case of parts with complex geometric shapes, where a manual intervention is still preferable.Within the EU SYMPLEXITY project, we are considering tasks where manual polishing operations are performed in strict physical Human-Robot Collaboration (HRC) between a robot holding the part and a human operator equipped with an abrasive tool. During the polishing task, the robot should firmly keep the workpiece in a prescribed sequence of poses, by monitoring and resisting to the external forces applied by the operator. However, the user may also wish to change the orientation of the part mounted on the robot, simply by pushing or pulling the robot body and changing thus its configuration. We propose a control algorithm that is able to distinguish the external torques acting at the robot joints in two components, one due to the polishing forces being applied at the end-effector level, the other due to the intentional physical interaction engaged by the human. The latter component is used to reconfigure the manipulator arm and, accordingly, its end-effector orientation. The workpiece position is kept instead fixed, by exploiting the intrinsic redundancy of this subtask. The controller uses a F/T sensor mounted at the robot wrist, together with our re-$ cently developed model-based technique (the residual method) that is able to estimate online the joint torques due to contact forces/torques applied at any place along the robot structure. In order to obtain a reliable residual, which is necessary to implement the control algorithm, an accurate robot dynamic model (including also friction effects at the joints and drive gains) needs to be identified first. The complete dynamic identification and the proposed control method for the human-robot collaborative polishing task are illustrated on a 6R UR10 lightweight manipulator mounting an ATI 6D sensor. polishing 45 placed on the final robot flange provides then the exchanged forces/torques. When the contact point is known in advance, the problem of sensor-less contact force estimation without the use of a F/T sensor has been addressed in [13], based on motor torque/current measurements. A dithering feedforward torque

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“…However other technologies have been widely used, e.g. [15], [16], [17]. In the literature, we can find several examples of using artificial skin to successfully control a robot.…”

Section: Introductionmentioning

confidence: 99%

“…These sensors cover the arms, legs and chest of the robot and provide tactile feedback to detect potential contacts with the environment. Also, Cirillo et al [17] introduce a flexible skin capable of measuring three components of the applied force and the location of the contact point. This skin is used to control a KUKA LWR, and can recognize simultaneously intentional and unintentional contacts.…”

Section: Introductionmentioning

confidence: 99%

From multi-modal tactile signals to a compliant control

Dean-León

Bergner

Ramirez-Amaro

et al. 2016

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

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Abstract-In this paper, we present an end-to-end approach transforming multi-modal tactile signals into a compliant control to generate different dynamic robot behaviors. This is obtained by fusing multi-modal sensor signals from our artificial skin and joint sensors with different control approaches. One advantage of these compliant behaviors is to produce safer robots, especially for physical Human-Robot Interaction. A key component of our framework is a robot parametric modeling based on the artificial skin multi-modal sensors (proximity, force and acceleration). These generated models are used to control a robot improving and even changing its dynamic behavior. We validate our framework in a real wheeled humanoid robot, where our presented framework enables a stiff robotic system to be compliant and react to multi-modal tactile events (pre-contacts and contacts).

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A Conformable Force/Tactile Skin for Physical Human–Robot Interaction

Cited by 135 publications

References 19 publications

A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration

A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration

A model-based residual approach for human-robot collaboration during manual polishing operations

From multi-modal tactile signals to a compliant control

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