Specialization of Perceptual Processes

In this report, I discuss the use of vision to support concrete, everyday activity. I will argue that a variety of interesting tasks can be solved using simple and inexpensive vision systems. I will provide a number of working examples in the form of a state-of-the-art mobile robot, Polly, which uses vision to give primitive tours of the seventh floor of the MIT AI Laboratory. By current standards, the robot has a broad behavioral repertoire and is both simple and inexpensive (the complete robot was built for less than $20,000 using commercial board-level components). The approach I will use will be to treat the structure of the agent's activity---its task and environment---as positive resources for the vision system designer. By performing a careful analysis of task and environment, the designer can determine a broad space of mechanisms which can perform the desired activity. My principal thesis is that for a broad range of activities, the space of applicable mechanisms will be broad enough to include a number mechanisms which are simple and economical. The simplest mechanisms that solve a given problem will typically be quite specialized to that problem. One thus worries that building simple vision systems will be require a great deal of {it ad-hoc} engineering that cannot be transferred to other problems. My second thesis is that specialized systems can be analyzed and understood in a principled manner, one that allows general lessons to be extracted from specialized systems. I will present a general approach to analyzing specialization through the use of transformations that provably improve performance. By demonstrating a sequence of transformations that derive a specialized system from a more general one, we can summarize the specialization of the former in a compact form that makes explicit the additional assumptions that it makes about its environment. The summary can be used to predict the performance of the system in novel environments. Individual transformations can be recycled in the design of future systems.

Redundant Sensors for Mobile Robot Navigation

Redundant sensors are needed on a mobile robot so that the accuracy with which it perceives its surroundings can be increased. Sonar and infrared sensors are used here in tandem, each compensating for deficiencies in the other. The robot combines the data from both sensors to build a representation which is more accurate than if either sensor were used alone. Another representation, the curvature primal sketch, is extracted from this perceived workspace and is used as the input to two path planning programs: one based on configuration space and one based on a generalized cone formulation of free space.

Progress in Vision and Robotics

The Vision Flashes are informal working papers intended primarily to stimulate internal interaction among participants in the A.I. Laboratory's Vision and Robotics group. Many of them report highly tentative conclusions or incomplete work. Others deal with highly detailed accounts of local equipment and programs that lack general interest. Still others are of great importance, but lack the polish and elaborate attention to proper referencing that characterizes the more formal literature. Nevertheless, the Vision Flashes collectively represent the only documentation of an important fraction of the work done in machine vision and robotics. The purpose of this report is to make the findings more readily available, but since they are not revised as presented here, readers should keep in mind the original purpose of the papers!

A Distributed Model for Mobile Robot Environment-Learning and Navigation

A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing. 2) Landmark detection: dynamically extracts landmarks from the robot's motion. 3) Map learning: constructs a distributed map of landmarks. The parallel implementation allows for localization in constant time. Spreading of activation computes both topological and physical shortest paths in linear time. The main issues addressed are: distributed, procedural, and qualitative representation and computation, emergent behaviors, dynamic landmarks, minimized communication.

Building Grounded Abstractions for Artificial Intelligence Programming

Most Artificial Intelligence (AI) work can be characterized as either ``high-level'' (e.g., logical, symbolic) or ``low-level'' (e.g., connectionist networks, behavior-based robotics). Each approach suffers from particular drawbacks. High-level AI uses abstractions that often have no relation to the way real, biological brains work. Low-level AI, on the other hand, tends to lack the powerful abstractions that are needed to express complex structures and relationships. I have tried to combine the best features of both approaches, by building a set of programming abstractions defined in terms of simple, biologically plausible components. At the ``ground level'', I define a primitive, perceptron-like computational unit. I then show how more abstract computational units may be implemented in terms of the primitive units, and show the utility of the abstract units in sample networks. The new units make it possible to build networks using concepts such as long-term memories, short-term memories, and frames. As a demonstration of these abstractions, I have implemented a simulator for ``creatures'' controlled by a network of abstract units. The creatures exist in a simple 2D world, and exhibit behaviors such as catching mobile prey and sorting colored blocks into matching boxes. This program demonstrates that it is possible to build systems that can interact effectively with a dynamic physical environment, yet use symbolic representations to control aspects of their behavior.

Mobile P2Ping: A Super-Peer based Structured P2P System Using a Fleet of City Buses

Recently, researchers have introduced the notion of super-peers to improve signaling efficiency as well as lookup performance of peer-to-peer (P2P) systems. In a separate development, recent works on applications of mobile ad hoc networks (MANET) have seen several proposals on utilizing mobile fleets such as city buses to deploy a mobile backbone infrastructure for communication and Internet access in a metropolitan environment. This paper further explores the possibility of deploying P2P applications such as content sharing and distributed computing, over this mobile backbone infrastructure. Specifically, we study how city buses may be deployed as a mobile system of super-peers. We discuss the main motivations behind our proposal, and outline in detail the design of a super-peer based structured P2P system using a fleet of city buses.

Proactive Detection and Recovery of Lost Mobile Phones

This paper describes the successful implementation of a prototype software application that independently and proactively detects whether a mobile phone is lost or misused. When the mobile phone is detected as being lost or misused, the application takes steps to mitigate the impact of loss and to gather evidence. The goal is to aid in the recovery of the mobile phone. The prototype works regardless of the cellular infrastructure the mobile phone is operating in and makes minimum demands on the owner of the mobile phone. The prototype was developed on Nokia 6600 mobile phones that run Symbian Operating System 7.0s. Development was done using Nokia’s Series 60 Developer’s Platform 2.0.