E-TRoll: Tactile sensing and classification via a simple robotic gripper for extended rolling manipulations

Robotic tactile sensing provides a method of recognizing objects and their properties where vision fails. Prior work on tactile perception in robotic manipulation has frequently focused on exploratory procedures (EPs). However, the also-human-inspired technique of in-hand-manipulation can glean rich data in a fraction of the time of EPs. We propose a simple 3-DOF robotic hand design, optimized for object rolling tasks via a variable-width palm and associated control system. This system dynamically adjusts the distance between the finger bases in response to object behavior. Compared to fixed finger bases, this technique significantly increases the area of the object that is exposed to finger-mounted tactile arrays during a single rolling motion (an increase of over 60% was observed for a cylinder with a 30-millimeter diameter). In addition, this paper presents a feature extraction algorithm for the collected spatiotemporal dataset, which focuses on object corner identification, analysis, and compact representation. This technique drastically reduces the dimensionality of each data sample from 10×1500 time series data to 80 features, which was further reduced by Principal Component Analysis (PCA) to 22 components. An ensemble subspace k-nearest neighbors (KNN) classification model was trained with 90 observations on rolling three different geometric objects, resulting in a three-fold cross-validation accuracy of 95.6% for object shape recognition

Single-Motor Robotic Gripper With Three Functional Modes for Grasping in Confined Spaces

This study proposes a novel robotic gripper driven by a single motor. The main task is to pick up objects in confined spaces. For this purpose, the developed gripper has three operating modes: grasping, finger-bending, and pull-in modes. Using these three modes, the developed gripper can rotate and translate a grasped object, i.e., can perform in-hand manipulation. This in-hand manipulation is effective for grasping in extremely confined spaces, such as the inside of a box in a shelf, to avoid interference between the grasped object and obstacles. To achieve the three modes using a single motor, the developed gripper is equipped with two novel self-motion switching mechanisms. These mechanisms switch their motions automatically when the motion being generated is prevented. An analysis of the mechanism and control methodology used to achieve the desired behavior are presented. Furthermore, the validity of the analysis and methodology are experimentally demonstrated. The gripper performance is also evaluated through the grasping tests

Variable-friction finger surfaces to enable within-hand manipulation via gripping and sliding

The human hand is able to achieve an unparalleled diversity of manipulation actions. One contributor to this capability is the structure of the human finger pad, where soft internal tissue is surrounded by a layer of more rigid skin. This permits conforming of the finger pad around object contours for firm grasping, while also permitting low-friction sliding over object surfaces with a light touch. These varying modes of manipulation contribute to the common ability for in-hand-manipulation, where an object (such as a car key) may repositioned relative to the palm. In this letter, we present a simple mechanical analogy to the human finger pad, via a robotic finger with both high- and low-friction surfaces. The low-friction surface is suspended on elastic elements and recesses into a cavity when a sufficient normal force is applied (~1.2 to 2.5 N depending on contact location), exposing the high-friction surface. We implement one “variable friction” finger and one “constant friction” finger on a 2-DOF gripper with a simple torque controller. With this setup, we demonstrate how within-hand rolling and sliding of an object may be achieved without the need for tactile sensing, high-dexterity, dynamic finger/object modeling, or complex control methods. The addition of an actuator to the finger design allows controlled switching between variable-friction and constant-friction modes, enabling precise object translation and reorientation within a grasp, via simple motion sequences. The rolling and sliding behaviors are characterized with experimentally verified geometric models

Whole-Hand Robotic Manipulation with Rolling, Sliding, and Caging

Traditional manipulation planning and modeling relies on strong assumptions about contact. Specifically, it is common to assume that contacts are fixed and do not slide. This assumption ensures that objects are stably grasped during every step of the manipulation, to avoid ejection. However, this assumption limits achievable manipulation to the feasible motion of the closed-loop kinematic chains formed by the object and fingers. To improve manipulation capability, it has been shown that relaxing contact constraints and allowing sliding can enhance dexterity. But in order to safely manipulate with shifting contacts, other safeguards must be used to protect against ejection. “Caging manipulation,” in which the object is geometrically trapped by the fingers, can be employed to guarantee that an object never leaves the hand, regardless of constantly changing contact conditions. Mechanical compliance and underactuated joint coupling, or carefully chosen design parameters, can be used to passively create a caging grasp – protecting against accidental ejection – while simultaneously manipulating with all parts of the hand. And with passive ejection avoidance, hand control schemes can be made very simple, while still accomplishing manipulation. In place of complex control, better design can be used to improve manipulation capability—by making smart choices about parameters such as phalanx length, joint stiffness, joint coupling schemes, finger frictional properties, and actuator mode of operation. I will present an approach for modeling fully actuated and underactuated whole-hand-manipulation with shifting contacts, show results demonstrating the relationship between design parameters and manipulation metrics, and show how this can produce highly dexterous manipulators

A Novel Method for the Quantitative Assessment of Fingered Robot Hand Designs for In-Hand Manipulation using Human Studies and Object-Centric Benchmarks

Fingered robot hands are complicated systems made of three essential system components: its morphology, its actuation, and its software control. These system components are tightly coupled to each other. Due to this, it is hard to benchmark robot hand performance in a way to understand the contributions of the individual system components. This defense introduces a new approach to characterizing a robot hand's system components using human subjects and novel object-centric benchmarks to study the contributions of the morphology and actuation system components at in-hand manipulation tasks. What is demonstrated are two studies using this method to study a hand's actuation and morphological system components. In the first, a hand's actuation component is studied at how well it can utilize tools (pen, spray bottle). In the second, a hand's morphological component is studied at how well it can perform fundamental in-hand manipulation tasks. Hand performance is compared between designs in order to build a quantitative understanding of how a robot hand's design affects its performance. Other roboticists can use this method on their own hands and on their own tasks to improve the systemic understanding of their own hands