Adam Spiers

The Enactive Torch: A New Tool for the Science of Perception

The cognitive sciences are increasingly coming to terms with the embodied, embedded, extended, and experiential aspects of the mind. Exemplifying this shift, the enactive approach points to an essential role of goal-directed bodily activity in the generation of meaningful perceptual experience, i.e., sense-making. Here, building on recent insights into the transformative effects of practical tool-use, we make use of the enactive approach in order to provide a definition of an enactive interface in terms of augmented sense-making. We introduce such a custom-built interface, the Enactive Torch, and present a study of its experiential effects. The results demonstrate that the user experience is not adequately captured by any standardly assumed perceptual modality; rather, it is a new feeling that is mediated by the design of the device and shaped by the overall situation of the task. Taken together these findings show that there is much to be gained by synergies between engineering and the cognitive sciences in the creation of new experience-centered technology. We suggest that the guiding principle should be the design of interfaces that serve as a transparent medium for augmenting our natural skills of interaction with the world, instead of requiring conscious attention to the interface as an opaque object in the world.

Safe Adaptive Compliance Control of a Humanoid Robotic Arm with Anti-Windup Compensation and Posture Control

Safety is very important for physical human-robot interaction. Compliance control can solve an important aspect of the safety problem by dealing with impact and other forces arising during close contact between humans and robots.An adaptive compliance model reference controller was implemented in real-time on a 4 degrees of freedom (DOF) humanoid robotic arm in Cartesian space. The robot manipulator has been controlled in such a way as to follow the compliant passive behaviour of a reference mass-spring-damper system model subject to an externally sensed force. The redundant DOF were used to control the robot motion in a human-like pattern via minimization of effort, a function of gravity. Associated actuator saturation issues were addressed by incorporating a novel anti-windup (AW) compensator originally developed for a neural network scheme. The controller was simulated for a robotic arm representing the Bristol-Elumotion-Robotic-Torso II (BERT II) and then tested on the real BERT II arm. BERT II has been developed in collaboration by Bristol Robotics Laboratory and Elumotion Ltd.

Unplanned, model-free, single grasp object classification with underactuated hands and force sensors

In this paper we present a methodology for discriminating between different objects using only a single force closure grasp with an underactuated robot hand equipped with force sensors. The technique leverages the benefits of simple, adaptive robot grippers (which can grasp successfully without prior knowledge of the hand or the object model), with an advanced machine learning technique (Random Forests). Unlike prior work in literature, the proposed methodology does not require object exploration, release or re-grasping and works for arbitrary object positions and orientations within the reach of a grasp. A two-fingered compliant, underactuated robot hand is controlled in an open-loop fashion to grasp objects with various shapes, sizes and stiffness. The Random Forests classification technique is used in order to discriminate between different object classes. The feature space used consists only of the actuator positions and the force sensor measurements at two specific time instances of the grasping process. A feature variables importance calculation procedure facilitates the identification of the most crucial features, concluding to the minimum number of sensors required. The efficiency of the proposed method is validated with two experimental paradigms involving two sets of fabricated model objects with different shapes, sizes and stiffness and a set of everyday life objects.

Robotic Implementation of Realistic Reaching Motion Using a Sliding Mode/Operational Space Controller

It has been shown that a task-level controller with minimal-effort posture control produces human-like motion in simulation. This control approach is based on the dynamic model of a human skeletal system superimposed with realistic muscle like actuators whose effort is minimised. In practical application, there is often a degree of error between the dynamic model of a system used for controller derivation and the actual dynamics of the system. We present a practical application of the task-level control framework with simplified posture control in order to produce life-like and compliant reaching motions for a redundant task. The addition of a sliding mode controller improves performance of the physical robot by compensating for unknown parametric and dynamic disturbances without compromising the human-like posture.

Single-Grasp Object Classification and Feature Extraction with Simple Robot Hands and Tactile Sensors

Classical robotic approaches to tactile object identification often involve rigid mechanical grippers, dense sensor arrays, and exploratory procedures (EPs). Though EPs are a natural method for humans to acquire object information, evidence also exists for meaningful tactile property inference from brief, non-exploratory motions (a ‘haptic glance’). In this work, we implement tactile object identification and feature extraction techniques on data acquired during a single, unplanned grasp with a simple, underactuated robot hand equipped with inexpensive barometric pressure sensors. Our methodology utilizes two cooperating schemes based on an advanced machine learning technique (random forests) and parametric methods that estimate object properties. The available data is limited to actuator positions (one per two link finger) and force sensors values (eight per finger). The schemes are able to work both independently and collaboratively, depending on the task scenario. When collaborating, the results of each method contribute to the other, improving the overall result in a synergistic fashion. Unlike prior work, the proposed approach does not require object exploration, re-grasping, grasp-release, or force modulation and works for arbitrary object start positions and orientations. Due to these factors, the technique may be integrated into practical robotic grasping scenarios without adding time or manipulation overheads.

M2 Gripper: Extending the Dexterity of a Simple, Underactuated Gripper

In the development of robotic hands, researchers have sought to increase inherent functionality without incurring greater complexity and cost. In this paper, we extend the manipulation capabilities of a simple gripper through a novel, underactuated design that produces several distinctive modes of operation. The proposed asymmetric hand design, the Multi-Modal (M2) Gripper, consists of a modular thumb with varying degrees of passive compliance and a dexterous, tendon-driven forefinger that can produce either underactuated or fully-actuated behaviors. With only two actuators and basic open-loop control, the hand is able to adaptively grasp objects of varying geometries, pinch-grasp smaller items, and perform some degree of in-hand manipulation via rolling and controlled sliding. We also detail the properties of this hand morphology that make it well-suited for future work in medical applications, haptic exploration, and studies on controlled stick-slip manipulation tasks.

First validation of the Haptic Sandwich: A shape changing handheld haptic navigation aid

This paper presents the Haptic Sandwich, a handheld robotic device that designed to provide navigation instructions to pedestrians through a novel shape changing modality. The device resembles a cube with an articulated upper half that is able to rotate and translate (extend) relative to the bottom half, which is grounded in the users hand. The poses assumed by the device simultaneously correspond to heading and proximity to a navigational target. The Haptic Sandwich provides an alternative to screen and/or audio based navigation technologies for both visually impaired and sighted pedestrians. Unlike many robotic or haptic navigational solutions, the haptic sandwich is discrete and unobtrusive in terms of form and sensory stimulus. Due to the novel nature of the interface, two user studies were undertaken to validate the concept and device. In the first experiment, stationary participants attempted to identify poses assumed by the device, which was hidden from view. 80% of poses were correctly identified and 17.5% had the minimal possible error. Multi-DOF errors accounted for only 1.1% of all responses. Perception accuracy of the rotation and extension DOF was significantly different. In the second study, participants attempted to locate a sequence of invisible navigational targets while walking with the device. Good navigational ability was demonstrated after minimal training. All participants were able to locate all targets, utilizing both DOF. Walking path efficiency was between 32%-56%. In summary, the paper presents the design of a novel shape changing haptic user interface intended to be intuitive and unobtrusive. The interface is then validated by stationary perceptual experiments and an embodied (walking) target finding pilot study.

Design and Evaluation of Shape-Changing Haptic Interfaces for Pedestrian Navigation Assistance

Shape-changing interfaces are a category of device capable of altering their form in order to facilitate communication of information. In this work, we present a shape-changing device that has been designed for navigation assistance. ‘The Animotus’ (previously, ‘The Haptic Sandwich’ ), resembles a cube with an articulated upper half that is able to rotate and extend (translate) relative to the bottom half, which is fixed in the users grasp. This rotation and extension, generally felt via the users fingers, is used to represent heading and proximity to navigational targets. The device is intended to provide an alternative to screen or audio based interfaces for visually impaired, hearing impaired, deafblind, and sighted pedestrians. The motivation and design of the haptic device is presented, followed by the results of a navigation experiment that aimed to determine the role of each device DOF, in terms of facilitating guidance. An additional device, ‘The Haptic Taco’, which modulated its volume in response to target proximity (negating directional feedback), was also compared. Results indicate that while the heading (rotational) DOF benefited motion efficiency, the proximity (translational) DOF benefited velocity. Combination of the two DOF improved overall performance. The volumetric Taco performed comparably to the Animotus’ extension DOF.

State of the art in prosthetic wrists: Commercial and research devices

The human wrist contributes greatly to hand mobility and manipulation capabilities in healthy individuals, but both the commercial and research domains have often overlooked prosthetic wrists in favor of terminal device development. In this paper, we review the current state of the art of in a wide variety of passive, body powered and active wrists from both the prosthetics industry and research community. We primarily focus on the mechanical design and kinematic arrangement of these systems, giving details of articulation methods and specifications where possible. Among other take-aways, the review shows that very few powered wrists are available commercially, all of which are single-DOF, that multi-DOF wrist designs are most often serial chain systems, and that there seems to be opportunities for the development of body-powered wrist devices or wrists with a parallel kinematic architecture. Additionally, of the three DOF of the human wrist, radial/ulnar deviation is least commonly implemented in hardware.

Biologically Inspired Control of Humanoid Robot Arms: Robust and Adaptive Approaches

This book investigates a biologically inspired method of robot arm control, developed with the objective of synthesising human-like motion dynamically, using nonlinear, robust and adaptive control techniques in practical robot systems. The control method caters to a rising interest in humanoid robots and the need for appropriate control schemes to match these systems. Unlike the classic kinematic schemes used in industrial manipulators, the dynamic approachesproposed here promote human-like motion with better exploitation of the robots physical structure. This also benefits human-robot interaction. The control schemes proposed in this book are inspired by a wealth of human-motion literature that indicates the drivers of motion to be dynamic, model-based and optimal. Such considerations lend themselves nicely to achievement via nonlinear control techniques without the necessity for extensive and complex biological models. The operational-space method of robot control forms the basis of many of the techniques investigated in this book. The method includes attractive features such as the decoupling of motion into task and posture components. Various developments are made in each of these elements. Simple cost functions inspired by biomechanical effort and discomfort generate realistic posture motion. Sliding-mode techniques overcome robustness shortcomings for practical implementation. Arm compliance is achieved via a method of model-free adaptive control that also deals with actuator saturation via anti-windup compensation. A neural-network-centered learning-by-observation scheme generates new task motions, based on motion-capture data recorded from human volunteers. In other parts of the book, motion capture is used to test theories of human movement. All developed controllers are applied to the reaching motion of a humanoid robot arm and are demonstrated to be practically realisable. This book is designed to be of interest to those wishing to achieve dynamics-based human-like robot-arm motion in academic research, advanced study or certain industrial environments. The book provides motivations, extensive reviews, research results and detailed explanations. It is not only suited to practising control engineers, but also applicable for general roboticists who wish to develop control systems expertise in this area.