Attribution of Autonomy and its Role in Robotic Language Acquisition

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The false attribution of autonomy and related concepts to artificial agents that lack the attributed levels of the respective characteristic is problematic in many ways. In this article we contrast this view with a positive viewpoint that emphasizes the potential role of such false attributions in the context of robotic language acquisition. By adding emotional displays and congruent body behaviors to a child-like humanoid robot’s behavioral repertoire we were able to bring naïve human tutors to engage in so called intent interpretations. In developmental psychology, intent interpretations can be hypothesized to play a central role in the acquisition of emotion, volition, and similar autonomy-related words. The aforementioned experiments originally targeted the acquisition of linguistic negation. However, participants produced other affect- and motivation-related words with high frequencies too and, as a consequence, these entered the robot’s active vocabulary. We will analyze participants’ non-negative emotional and volitional speech and contrast it with participants’ speech in a non-affective baseline scenario. Implications of these findings for robotic language acquisition in particular and artificial intelligence and robotics more generally will also be discussed.Peer reviewedFinal Published versio

Integrating Elastic Bands to Enhance Performance for Textile Robotics

The elastic bands integrated using the ruffles technique proved to be effective in enhancing the performance of the soft robotic structures. In the actuator application, the elastic bands greatly increased the bending capability and force capability of the structure, while in the eversion robot cap application, the elastic bands improved the performance slightly by maintaining the sensory payload at the tip without restricting the eversion process. These findings demonstrate the potential of using elastic bands and textile techniques in soft robotics to create more efficient and adaptable structures

Novel Tactile-SIFT Descriptor for Object Shape Recognition

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Fingertip Fiber Optical Tactile Array with Two-Level Spring Structure

Tactile perception is a feature benefiting reliable grasping and manipulation. This paper presents the design of an integrated fingertip force sensor employing an optical fiber based approach where applied forces modulate light intensity. The proposed sensor system is developed to support grasping of a broad range of objects, including those that are hard as well those that are soft. The sensor system is comprised of four sensing elements forming a tactile array integrated with the tip of a finger. We investigate the design configuration of a separate force sensing element with the aim to improve its measurement range. The force measurement of a single tactile element is based on a two-level displacement that is achieved thanks to a hybrid sensing structure made up of a stiff linear and flexible ortho-planar spring. An important outcome of this paper is a miniature tactile fingertip sensor that is capable of perceiving light contact, typically occurring during the initial stages of a grasp, as well as measuring higher forces, commonly present during tight grasps

A Non-linear Model for Predicting Tip Position of a Pliable Robot Arm Segment Using Bending Sensor Data

Using pliable materials for the construction of robot bodies presents new and interesting challenges for the robotics community. Within the EU project entitled STIFFness controllable Flexible & Learnable manipulator for surgical Operations (STIFF-FLOP), a bendable, segmented robot arm has been developed. The exterior of the arm is composed of a soft material (silicone), encasing an internal structure that contains air-chamber actuators and a variety of sensors for monitoring applied force, position and shape of the arm as it bends. Due to the physical characteristics of the arm, a proper model of robot kinematics and dynamics is difficult to infer from the sensor data. Here we propose a non-linear approach to predicting the robot arm posture, by training a feed-forward neural network with a structured series of pressures values applied to the arm's actuators. The model is developed across a set of seven different experiments. Because the STIFF-FLOP arm is intended for use in surgical procedures, traditional methods for position estimation (based on visual information or electromagnetic tracking) will not be possible to implement. Thus the ability to estimate pose based on data from a custom fiber-optic bending sensor and accompanying model is a valuable contribution. Results are presented which demonstrate the utility of our non-linear modelling approach across a range of data collection procedures

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Robotic surface exploration with vision and tactile sensing for cracks detection and characterisation

This paper presents a novel algorithm for crack localisation and detection based on visual and tactile analysis via fibre-optics. A finger-shaped sensor based on fibre-optics is employed for the data acquisition to collect data for the analysis and the experiments. To detect the possible locations of cracks a camera is used to scan an environment while running an object detection algorithm. Once the crack is detected, a fully-connected graph is created from a skeletonised version of the crack. A minimum spanning tree is then employed for calculating the shortest path to explore the crack which is then used to develop the motion planner for the robotic manipulator. The motion planner divides the crack into multiple nodes which are then explored individually. Then, the manipulator starts the exploration and performs the tactile data classification to confirm if there is indeed a crack in that location or just a false positive from the vision algorithm. If a crack is detected, also the length, width, orientation and number of branches are calculated. This is repeated until all the nodes of the crack are explored. In order to validate the complete algorithm, various experiments are performed: comparison of exploration of cracks through full scan and motion planning algorithm, implementation of frequency-based features for crack classification and geometry analysis using a combination of vision and tactile data. From the results of the experiments, it is shown that the proposed algorithm is able to detect cracks and improve the results obtained from vision to correctly classify cracks and their geometry with minimal cost thanks to the motion planning algorithm.Comment: 12 page

An Optimal State Dependent Haptic Guidance Controller via a Hard Rein

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