Dexterous Robotic Hands: Kinematics and Control

This report presents issues relating to the kinematics and control of dexterous robotic hands using the Utah-MIT hand as an illustrative example. The emphasis throughout is on the actual implementation and testing of the theoretical concepts presented. The kinematics of such hands is interesting and complicated owing to the large number of degrees of freedom involved. The implementation of position and force control algorithms on such tendon driven hands has previously suffered from inefficient formulations and a lack of sophisticated computer hardware. Both these problems are addressed in this report. A multiprocessor architecture has been built with high performance microcomputers on which real-time algorithms can be efficiently implemented. A large software library has also been built to facilitate flexible software development on this architecture. The position and force control algorithms described herein have been implemented and tested on this hardware.

Contact Sensors for Dexterous Robotic Hands

This thesis examines a tactile sensor and a thermal sensor for use with the Utah-MIT dexterous four fingered hand. Sensory feedback is critical or full utilization of its advanced manipulatory capabilities. The hand itself provides tendon tensions and joint angles information. However, planned control algorithms require more information than these sources can provide. The tactile sensor utilizes capacitive transduction with a novel design based entirely on silicone elastomers. It provides an 8 x 8 array of force cells with 1.9 mm center-to-center spacing. A pressure resolution of 8 significant bits is available over a 0 to 200 grams per square mm range. The thermal sensor measures a material's heat conductivity by radiating heat into an object and measuring the resulting temperature variations. This sensor has a 4 x 4 array of temperature cells with 3.5 mm center-to-center spacing. Experiments show that the thermal sensor can discriminate among material by detecting differences in their thermal conduction properties. Both sensors meet the stringent mounting requirements posed by the Utah-MIT hand. Combining them together to form a sensor with both tactile and thermal capabilities will ultimately be possible. The computational requirements for controlling a sensor equipped dexterous hand are severe. Conventional single processor computers do not provide adequate performance. To overcome these difficulties, a computational architecture based on interconnecting high performance microcomputers and a set of software primitives tailored for sensor driven control has been proposed. The system has been implemented and tested on the Utah-MIT hand. The hand, equipped with tactile and thermal sensors and controlled by its computational architecture, is one of the most advanced robotic manipulatory devices available worldwide. Other ongoing projects will exploit these tools and allow the hand to perform tasks that exceed the capabilities of current generation robots.

Reasoning Modeled as a Society of Communicating Experts

This report describes a domain independent reasoning system. The system uses a frame-based knowledge representation language and various reasoning techniques including constraint propagation, progressive refinement, natural deduction and explicit control of reasoning. A computational architecture based on active objects which operate by exchanging messages is developed and it is shown how this architecture supports reasoning activity. The user interacts with the system by specifying frames and by giving descriptions defining the problem situation. The system uses its reasoning capacity to build up a model of the problem situation from which a solution can interactively be extracted. Examples are discussed from a variety of domains, including electronic circuits, mechanical devices and music. The main thesis is that a reasoning system is best viewed as a parallel system whose control and data are distributed over a large network of processors that interact by exchanging messages. Such a system will be metaphorically described as a society of communicating experts.

Message-Driven Processor Architecture: Verson 11

The Message-Driven Processor is a node of a large-scale multiprocessor being developed by the Concurrent VLSI Architecture Group. It is intended to support fine-grained, message passing, parallel computation. It contains several novel architectural features, such as a low-latency network interface, extensive type-checking hardware, and on-chip memory that can be used as an associative lookup table. This document is a programmer's guide to the MDP. It describes the processor's register architecture, instruction set, and the data types supported by the processor. It also details the MDP's message sending and exception handling facilities.

Computational Vision: A Critical Review

We review the progress made in computational vision, as represented by Marr's approach, in the last fifteen years. First, we briefly outline computational theories developed for low, middle and high-level vision. We then discuss in more detail solutions proposed to three representative problems in vision, each dealing with a different level of visual processing. Finally, we discuss modifications to the currently established computational paradigm that appear to be dictated by the recent developments in vision.

Computational Consequences of Agreement and Ambiguity in Natural Language

The computer science technique of computational complexity analysis can provide powerful insights into the algorithm-neutral analysis of information processing tasks. Here we show that a simple, theory-neutral linguistic model of syntactic agreement and ambiguity demonstrates that natural language parsing may be computationally intractable. Significantly, we show that it may be syntactic features rather than rules that can cause this difficulty. Informally, human languages and the computationally intractable Satisfiability (SAT) problem share two costly computional mechanisms: both enforce agreement among symbols across unbounded distances (Subject-Verb agreement) and both allow ambiguity (is a word a Noun or a Verb?).

Intelligence Without Reason

Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation.

Principles, Opportunism and Seeing in Design: A Computational Approach

This thesis introduces elements of a theory of design activity and a computational framework for developing design systems. The theory stresses the opportunistic nature of designing and the complementary roles of focus and distraction, the interdependence of evaluation and generation, the multiplicity of ways of seeing over the history of a design session versus the exclusivity of a given way of seeing over an arbitrarily short period, and the incommensurability of criteria used to evaluate a design. The thesis argues for a principle based rather than rule based approach to designing documents. The Discursive Generator is presented as a computational framework for implementing specific design systems, and a simple system for arranging blocks according to a set of formal principles is developed by way of illustration. Both shape grammars and constraint based systems are used to contrast current trends in design automation with the discursive approach advocated in the thesis. The Discursive Generator is shown to have some important properties lacking in other types of systems, such as dynamism, robustness and the ability to deal with partial designs. When studied in terms of a search metaphor, the Discursive Generator is shown to exhibit behavior which is radically different from some traditional search techniques, and to avoid some of the well-known difficulties associated with them.

Towards an Example-Based Image Compression Architecture for Video-Conferencing

This paper consists of two major parts. First, we present the outline of a simple approach to very-low bandwidth video-conferencing system relying on an example-based hierarchical image compression scheme. In particular, we discuss the use of example images as a model, the number of required examples, faces as a class of semi-rigid objects, a hierarchical model based on decomposition into different time-scales, and the decomposition of face images into patches of interest. In the second part, we present several algorithms for image processing and animation as well as experimental evaluations. Among the original contributions of this paper is an automatic algorithm for pose estimation and normalization. We also review and compare different algorithms for finding the nearest neighbors in a database for a new input as well as a generalized algorithm for blending patches of interest in order to synthesize new images. Finally, we outline the possible integration of several algorithms to illustrate a simple model-based video-conference system.

A Computational Model for the Acquisition and Use of Phonological Knowledge

Does knowledge of language consist of symbolic rules? How do children learn and use their linguistic knowledge? To elucidate these questions, we present a computational model that acquires phonological knowledge from a corpus of common English nouns and verbs. In our model the phonological knowledge is encapsulated as boolean constraints operating on classical linguistic representations of speech sounds in term of distinctive features. The learning algorithm compiles a corpus of words into increasingly sophisticated constraints. The algorithm is incremental, greedy, and fast. It yields one-shot learning of phonological constraints from a few examples. Our system exhibits behavior similar to that of young children learning phonological knowledge. As a bonus the constraints can be interpreted as classical linguistic rules. The computational model can be implemented by a surprisingly simple hardware mechanism. Our mechanism also sheds light on a fundamental AI question: How are signals related to symbols?

AMAR: A Computational Model of Autosegmental Phonology

This report describes a computational system with which phonologists may describe a natural language in terms of autosegmental phonology, currently the most advanced theory pertaining to the sound systems of human languages. This system allows linguists to easily test autosegmental hypotheses against a large corpus of data. The system was designed primarily with tonal systems in mind, but also provides support for tree or feature matrix representation of phonemes (as in The Sound Pattern of English), as well as syllable structures and other aspects of phonological theory. Underspecification is allowed, and trees may be specified before, during, and after rule application. The association convention is automatically applied, and other principles such as the conjunctivity condition are supported. The method of representation was designed such that rules are designated in as close a fashion as possible to the existing conventions of autosegmental theory while adhering to a textual constraint for maximum portability.

On Directional Selectivity in Vertebrate Retina: An Experimental and Computational Study

This thesis describes an investigation of retinal directional selectivity. We show intracellular (whole-cell patch) recordings in turtle retina which indicate that this computation occurs prior to the ganglion cell, and we describe a pre-ganglionic circuit model to account for this and other findings which places the non-linear spatio-temporal filter at individual, oriented amacrine cell dendrites. The key non-linearity is provided by interactions between excitatory and inhibitory synaptic inputs onto the dendrites, and their distal tips provide directionally selective excitatory outputs onto ganglion cells. Detailed simulations of putative cells support this model, given reasonable parameter constraints. The performance of the model also suggests that this computational substructure may be relevant within the dendritic trees of CNS neurons in general.

Computational Structure of GPSG Models: Revised Generalized Phrase Structure Grammar

The primary goal of this report is to demonstrate how considerations from computational complexity theory can inform grammatical theorizing. To this end, generalized phrase structure grammar (GPSG) linguistic theory is revised so that its power more closely matches the limited ability of an ideal speaker--hearer: GPSG Recognition is EXP-POLY time hard, while Revised GPSG Recognition is NP-complete. A second goal is to provide a theoretical framework within which to better understand the wide range of existing GPSG models, embodied in formal definitions as well as in implemented computer programs. A grammar for English and an informal explanation of the GPSG/RGPSG syntactic features are included in appendices.

Towards a Computational Theory of Definite Anaphora Comprehension in English Discourse

This report investigates the process of focussing as a description and explanation of the comprehension of certain anaphoric expressions in English discourse. The investigation centers on the interpretation of definite anaphora, that is, on the personal pronouns, and noun phrases used with a definite article the, this or that. Focussing is formalized as a process in which a speaker centers attention on a particular aspect of the discourse. An algorithmic description specifies what the speaker can focus on and how the speaker may change the focus of the discourse as the discourse unfolds. The algorithm allows for a simple focussing mechanism to be constructed: and element in focus, an ordered collection of alternate foci, and a stack of old foci. The data structure for the element in focus is a representation which encodes a limted set of associations between it and other elements from teh discourse as well as from general knowledge.

A Computational Model of Skill Acquisition

This thesis confronts the nature of the process of learning an intellectual skill, the ability to solve problems efficiently in a particular domain of discourse. The investigation is synthetic; a computational performance model, HACKER, is displayed. Hacker is a computer problem-solving system whose performance improves with practice. HACKER maintains performance knowledge as a library of procedures indexed by descriptions of the problem types for which the procedures are appropriate. When applied to a problem, HACKER tries to use a procedure from this "Answer Library". If no procedure is found to be applicable, HACKER writes one using more general knowledge of the problem domain and of programming techniques. This new program may be generalized and added to the Answer Library.