Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater

Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a Supervised Variational Autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learned a series of latent representations of the soft mechanics transferrable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.Comment: 17 pages, 5 figures, 1 table, submitted to Advanced Intelligent Systems for revie

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

Innovative robot hand designs of reduced complexity for dexterous manipulation

This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects. Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension. Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty. In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness. Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm. In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Exploitation of environmental constraints in human and robotic grasping

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present support for this view found through the analysis of human grasp behavior and by showing robust robotic grasping based on constraint-exploiting grasp strategies. Furthermore, we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints

Exploitation of environmental constraints in human and robotic grasping

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present support for this view found through the analysis of human grasp behavior and by showing robust robotic grasping based on constraint-exploiting grasp strategies. Furthermore, we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints

Investigation and development of a flexible gripper with adaptable finger geometry

Das zuverlässige und schonende Greifen ist ein Hauptanliegen bei der Entwicklung von neuartigen Greifvorrichtungen. Je größer die Kontaktfläche zwischen dem Greifer und dem Greifobjekt ist, desto schonender und zuverlässiger ist der Greifvorgang. Um dieses Ziel zu erreichen wurden in den letzten Jahrzehnten zahlreiche Untersuchungen zu adaptiven passiven Greifern durchgeführt. Ein neuer Forschungszweig im Bereich selbstadaptiver Greifer sind Greifer mit nachgiebigen blattfederartigen Greifelementen (Greiferfinger) Die Funktionsweise basiert auf dem elastischen Ausknicken der Greifelemente infolge einer translatorische Antriebsbewegung Die vorliegende Arbeit konzentriert sich auf die Verbesserung des Greifvorgangs, indem die Kontaktlänge zwischen den blattfederartigen Greiferfingern und dem zu greifenden Objekt deutlich erhöht wird. Um diese Aufgabenstellung zu lösen, muss eine geeignete Greifergeometrie für ein gegebenes Greifobjekt berechnet werden. Die gezielte Berechnung der erfoderlichen Greifergeometrie für ein bekanntes Greifobjekt ist nicht möglich. Daher wurde als Lösungsansatz die umkehrte Richtung gewählt. Für eine definierte Greifgeometrie wird die Gestalt des dazu passenden “idealen” Greifobjektes ermittelt und anschließend mit der Gestalt zu greifenden Objektes verglichen. Bei Gestaltabweichungen wird die Greifergeometrie iterative verändert, bis seine geeignete Greifergeometrie gefunden wurde. Im Rahmen der vorliegenden Arbeit wird zunächst die Ermittlung des “idealen” Greifobjektes behandelt. Es wurde ein Algorithmus entwickelt, der für eine vorgegebene Greifergeometrie die Gestalt eines runden bzw. elliptischen Objektes ermittelt. Der Algorithmus verwendet als Eingabedaten die Biegelinien der elastisch ausgeknickten Greiffinger unter Berücksichtigung unterschiedlicher Randbedingungen. Als Ausgabedaten liefert der Algorithmus die Gestalt des passenden Greifobjektes zurück. Für quadratische bzw. rechteckige sowie für dreieckige Objekte wurden unterschiedliche Greifgeometrien untersucht. Außerdem wird für quadratische und rechteckige Objekte das Lösungskonzept für die Entwicklung eines weiteren Algorithmus beschrieben. In Kapitel 1 wird eine Klassifizierung von Greifern basierend auf der Anpassungsfähigkeit vorgestellt. In Kapitel 2 werden Lösungskonzepte, Modelle und Theorien vorgestellt. In Kapitel 3 werden Ablaufdiagramme der Algorithmen dargestellt. In Kapitel 4 wird die Entwicklung des Algorithmus für elliptische Objekte und deren Betriebsmodi beschrieben. In Kapitel 5 werden Greifgeometrien für quadratische bzw. Rechteckige sowie für dreieckige Objekte analysiert und die Ideen eines Algorithmus für quadratisch bzw. rechteckige Objekte beschrieben. In Kapitel 6 wird ein kurzer Überblick über die zukünftige Arbeiten.Reliable and gentle gripping is a major concern in the development of new gripping devices. The larger contact surface between the gripper and the gripping object, the gentler and more reliable the gripping process. In order to achieve this goal, further investigations on adaptive passive grippers have been carried out in the recent decades. A new branch of research in the field of self-adaptive grippers are compliant leaf-spring-like gripping elements (gripper fingers). Its mode of operation is based on the elastic buckling of the gripping elements as a result of a translatory drive movement. The present work focuses on improving the gripping process by increasing significantly the contact length between the compliant leaf-spring-like gripper fingers and the object to be gripped. In order to solve this task, a suitable gripper geometry for a given gripping object should be calculated The specific calculation of the required gripper geometry for a known gripping object is not possible; therefore, this work aims in the opposite direction. For a defined gripping geometry, the shape of the matching “ideal” gripping object is determined and then compared with the desired object to be gripped. In case of a deviation in the size, the gripper geometry is iteratively changed until its suitable gripper geometry has been found. In the present work, the determination of the “ideal” gripping object is the first task to deal with. An algorithm has been developed to determine the shape of a round-elliptical object for a given gripper geometry. The algorithm uses as data input the bend lines of the compliant twogripper finger under different boundary conditions. As data output, the algorithm returns the shape of the matching gripping object. For square-rectangular and triangular objects, different gripping geometries have been investigated. Furthermore, for square-rectangular objects, solution concepts for the development of an algorithm is described. In chapter 1, a classification based on adaptability is presented. In chapter 2, solution concepts, models and theories involved are introduced. In chapter 3, process flow diagrams of the algorithms are presented. In chapter 4, the development of the algorithm for elliptical objects and its operation modes are described. In chapter 5, gripping geometries for square-rectangular and triangular objects are analysed and the ideas of an algorithm for square-rectangular objects are described. In chapter 6, a brief overview of the futur work is commented.Tesi

Safe Grasping with a Force Controlled Soft Robotic Hand

Safe yet stable grasping requires a robotic hand to apply sufficient force on the object to immobilize it while keeping it from getting damaged. Soft robotic hands have been proposed for safe grasping due to their passive compliance, but even such a hand can crush objects if the applied force is too high. Thus for safe grasping, regulating the grasping force is of uttermost importance even with soft hands. In this work, we present a force controlled soft hand and use it to achieve safe grasping. To this end, resistive force and bend sensors are integrated in a soft hand, and a data-driven calibration method is proposed to estimate contact interaction forces. Given the force readings, the pneumatic pressures are regulated using a proportional-integral controller to achieve desired force. The controller is experimentally evaluated and benchmarked by grasping easily deformable objects such as plastic and paper cups without neither dropping nor deforming them. Together, the results demonstrate that our force controlled soft hand can grasp deformable objects in a safe yet stable manner.Comment: Accepted to 2020 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2020

A 3D-Printed Omni-Purpose Soft Gripper

Numerous soft grippers have been developed based on smart materials, pneumatic soft actuators, and underactuated compliant structures. In this article, we present a three-dimensional (3-D) printed omni-purpose soft gripper (OPSOG) that can grasp a wide variety of objects with different weights, sizes, shapes, textures, and stiffnesses. The soft gripper has a unique design that incorporates soft fingers and a suction cup that operate either separately or simultaneously to grasp specific objects. A bundle of 3-D-printable linear soft vacuum actuators (LSOVA) that generate a linear stroke upon activation is employed to drive the tendon-driven soft fingers. The support, fingers, suction cup, and actuation unit of the gripper were printed using a low-cost and open-source fused deposition modeling 3-D printer. A single LSOVA has a blocked force of 30.35 N, a rise time of 94 ms, a bandwidth of 2.81 Hz, and a lifetime of 26 120 cycles. The blocked force and stroke of the actuators are accurately predicted using finite element and analytical models. The OPSOG can grasp at least 20 different objects. The gripper has a maximum payload-to-weight ratio of 7.06, a grip force of 31.31 N, and a tip blocked force of 3.72 N