21 research outputs found
Shear-invariant Sliding Contact Perception with a Soft Tactile Sensor
Manipulation tasks often require robots to be continuously in contact with an
object. Therefore tactile perception systems need to handle continuous contact
data. Shear deformation causes the tactile sensor to output path-dependent
readings in contrast to discrete contact readings. As such, in some
continuous-contact tasks, sliding can be regarded as a disturbance over the
sensor signal. Here we present a shear-invariant perception method based on
principal component analysis (PCA) which outputs the required information about
the environment despite sliding motion. A compliant tactile sensor (the TacTip)
is used to investigate continuous tactile contact. First, we evaluate the
method offline using test data collected whilst the sensor slides over an edge.
Then, the method is used within a contour-following task applied to 6 objects
with varying curvatures; all contours are successfully traced. The method
demonstrates generalisation capabilities and could underlie a more
sophisticated controller for challenging manipulation or exploration tasks in
unstructured environments. A video showing the work described in the paper can
be found at https://youtu.be/wrTM61-pieUComment: Accepted in ICRA 201
GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger
This work describes the development of a high-resolution tactile-sensing
finger for robot grasping. This finger, inspired by previous GelSight sensing
techniques, features an integration that is slimmer, more robust, and with more
homogeneous output than previous vision-based tactile sensors. To achieve a
compact integration, we redesign the optical path from illumination source to
camera by combining light guides and an arrangement of mirror reflections. We
parameterize the optical path with geometric design variables and describe the
tradeoffs between the finger thickness, the depth of field of the camera, and
the size of the tactile sensing area. The sensor sustains the wear from
continuous use -- and abuse -- in grasping tasks by combining tougher materials
for the compliant soft gel, a textured fabric skin, a structurally rigid body,
and a calibration process that maintains homogeneous illumination and contrast
of the tactile images during use. Finally, we evaluate the sensor's durability
along four metrics that track the signal quality during more than 3000 grasping
experiments.Comment: RA-L Pre-print. 8 page
Calibration of tactile/force sensors for grasping with the PRISMA Hand II
The PRISMA Hand II is a mechanically robust anthropomorphic hand developed at PRISMA Lab, University of Naples Federico II. The hand is highly underactuated, three motors drive 19 joints via elastic tendons. Thanks to its particular mechanical design, the hand can perform not only adaptive grasps but also in-hand manipulation. Each fingertip integrates a tactile/force sensor, based on optoelectronic technology, to provide tactile/force feedback during grasping and manipulation, particularly useful with deformable objects. The paper briefly describes the mechanical design and sensor technology of the hand and proposes a calibration procedure for tactile/force sensors. A comparison between different models of Neural Networks architectures, suitable for sensors calibration, is shown. Experimental tests are provided to choose the optimal tactile sensing suite. Finally, experiments for the regulation of the forces are made to show the effectiveness of calibrated sensors
Dexterous manipulation of unknown objects using virtual contact points
The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.Peer ReviewedPostprint (author's final draft
Manipulación diestra de objetos desconocidos usando puntos de contacto virtuales
En este trabajo se presenta una estrategia de manipulación que permite rotar objetos desconocidos usando una mano robótica equipada con sensores táctiles.
Las configuraciones de la mano que permiten cambiar la posición del objeto se calculan usando la información táctil y cinemática que se obtiene mientras se manipula el objeto, y razonando en base a las posiciones deseadas y reales de las yemas de los dedos durante la manipulación, teniendo en cuenta que las primeras no son físicamente alcanzables al estar situadas en el interior del objeto y son por lo tanto posiciones virtuales que tienen asociados puntos de contacto virtuales.
El enfoque propuesto fue probado exitosamente usando tres dedos de una mano robótica antropomorfa (Allegro Hand), cuyas puntas de los dedos han sido modificadas para incluir los sensores táctiles (WTS-FT).
En la validación experimental se manipularon exitosamente varios objetos de uso cotidiano de diferentes formas, rotándolos satisfactoriamente sin necesidad de conocer su forma.Postprint (author's final draft
From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor
Deep learning has the potential to have the impact on robot touch that it has
had on robot vision. Optical tactile sensors act as a bridge between the
subjects by allowing techniques from vision to be applied to touch. In this
paper, we apply deep learning to an optical biomimetic tactile sensor, the
TacTip, which images an array of papillae (pins) inside its sensing surface
analogous to structures within human skin. Our main result is that the
application of a deep CNN can give reliable edge perception and thus a robust
policy for planning contact points to move around object contours. Robustness
is demonstrated over several irregular and compliant objects with both tapping
and continuous sliding, using a model trained only by tapping onto a disk.
These results relied on using techniques to encourage generalization to tasks
beyond which the model was trained. We expect this is a generic problem in
practical applications of tactile sensing that deep learning will solve. A
video demonstrating the approach can be found at
https://www.youtube.com/watch?v=QHrGsG9AHtsComment: Accepted in RAL and ICRA 2019. N. Lepora and J. Lloyd contributed
equally to this wor
Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing
This work deals with a practical everyday problem: stable object placement on
flat surfaces starting from unknown initial poses. Common object-placing
approaches require either complete scene specifications or extrinsic sensor
measurements, e.g., cameras, that occasionally suffer from occlusions. We
propose a novel approach for stable object placing that combines tactile
feedback and proprioceptive sensing. We devise a neural architecture that
estimates a rotation matrix, resulting in a corrective gripper movement that
aligns the object with the placing surface for the subsequent object
manipulation. We compare models with different sensing modalities, such as
force-torque and an external motion capture system, in real-world object
placing tasks with different objects. The experimental evaluation of our
placing policies with a set of unseen everyday objects reveals significant
generalization of our proposed pipeline, suggesting that tactile sensing plays
a vital role in the intrinsic understanding of robotic dexterous object
manipulation. Code, models, and supplementary videos are available at
https://sites.google.com/view/placing-by-touching
Soft Fingertips with Tactile Sensing and Active Deformation for Robust Grasping of Delicate Objects
Soft fingertips have shown significant adaptability for grasping a wide range of object shapes thanks to elasticity. This ability can be enhanced to grasp soft, delicate objects by adding touch sensing. However, in these cases, the complete restraint and robustness of the grasps have proved to be challenging, as the exertion of additional forces on the fragile object can result in damage. This paper presents a novel soft fingertip design for delicate objects based on the concept of embedded air cavities, which allow the dual ability of adaptive sensing and active shape changing. The pressurized air cavities act as soft tactile sensors to control gripper position from internal pressure variation; and active fingertip deformation is achieved by applying positive pressure to these cavities, which then enable a delicate object to be kept securely in position, despite externally applied forces, by form closure. We demonstrate this improved grasping capability by comparing the displacement of grasped delicate objects exposed to high-speed motions. Results show that passive soft fingertips fail to restrain fragile objects at accelerations as low as 0.1m/s2 , in contrast, with the proposed fingertips, delicate objects are completely secure even at accelerations of more than 5m/s2