Scott Lenser

Visual sonar: fast obstacle avoidance using monocular vision

We contribute a fast system for avoiding unknown obstacles on a mobile robot using a simple camera as the only sensor. The vision module detects objects, both known and unknown, around the robot. Unknown objects are detected by paying attention to occlusions of a floor of known colors. Range and angle to the objects is calculated and used to create a radial model of the vicinity of the robot. This modeling component keeps tracks of objects that are currently outside the field of view of the camera allowing the robot to avoid obstacles it is not currently looking at. We show the effectiveness of the vision and modeling algorithms by creating a simple behavior which wanders around while avoiding obstacles.

CMPack: a complete software system for autonomous legged soccer robots

This paper describes a completely implemented, fully autonomous software system for soccer playing quadruped ro\-bots. The system includes real-time color vision, probabilistic localization, quadruped locomotion/motion, and a hierarchical behavior system. Each component was based on well tested algorithms and approaches from other domains. Our design exposed strengths and weaknesses in each component, and led to improvements and extensions that made them more capable in general, as well as better suited for our testing domain. Integrating the components revealed design assumptions that were violated. We describe the problems that arose and how we addressed them. The integrated system was then used at the annual Robo\-Cup robotic soccer competition where we placed third, losing only a single game. We reflect on how our system addressed its goals and what was learned through implementation and testing on real robots.

A Modular Hierarchical Behavior-Based Architecture

This paper describes a highly modular hierarchical behavior-based control system for robots. Key features of the architecture include: easy addition/removal of behaviors, easy addition of specialized behaviors, easy to program hierarchical structure, and ability to execute nonconflicting behaviors in parallel. The architecture uses a unique reward based combinator to arbitrate amongst competing behaviors such as to maximize reward. This behavior system was successfully used in our Sony Legged League entry in RoboCup 2000 where we came in third losing only a single game.

CMRoboBits: Creating an Intelligent AIBO Robot

CMRoboBits is a course offered at Carnegie Mellon University that introduces students to all the concepts needed to create a complete intelligent robot. In particular, the course focuses on the areas of perception, cognition, and action by using the Sony AIBO robot as the focus for the programming assignments. This course shows how an AIBO and its software resources make it possible for students to investigate and work with an unusually broad variety of AI topics within a single semester. While material presented in this article describes using AIBOs as the primary platform, the concepts presented in the course are not unique to the AIBO and can be applied on different kinds of robotic hardware.

Automatic detection and response to environmental change

Robots typically have many sensors, which are underutilized. This is usually because no simple mathematical models of the sensors have been developed or the sensors are too noisy to use techniques, which require simple noise models. We propose to use these underutilized sensors to determine the state of the environment in which the robot is operating. Being able to identify the state of the environment allows the robot to adapt to current operating conditions and the actions of other agents. Adapting to current operating conditions makes robot robust to changes in the environment by constantly adapting to the current conditions. This is useful for adapting to different lighting conditions or different flooring conditions amongst many other possible desirable adaptations. The strategy we propose for utilizing these sensors is to group sensor readings into statistical probability distributions and then compare the probability distributions to detect repeated states of the environment.

Fast Parametric Transitions for Smooth Quadrupedal Motion

This paper describes a motion system for a quadruped robot that performs smooth transitions over requested body trajectories. It extends the generality of path based approaches by introducing geometric primitives that guarantee smoothness while decreasing (and in some cases entirely removing) constraints on when and what types of parameter transitions can be made. The overall motion system for the autonomous Sony legged robot that served as our test-bedis also described. This motion system served as a component in our entry in the RoboCup-2000 world robotic soccer championship, in which we placed third, losing only a single game.

Non-Parametric Time Series Classification

We present an improved state-based prediction algorithm for time series. Given time series produced by a process composed of different underlying states, the algorithm predicts future time series values based on past time series values for each state. Unlike many algorithms, this algorithm predicts a multi-modal distribution over future values. This prediction forms the basis for labelling part of a time series with the underlying state that created it given some labelled example signals. The algorithm is robust to a wide variety of possible types of changes in signals including changes in mean, amplitude, amount of noise, and period. We show results demonstrating that the algorithm successfully segments signals from several robotic sensors generated while performing a variety of simple tasks.

Classification of robotic sensor streams using non-parametric statistics

We extend our previous work on a classification algorithm for time series. Given time series produced by different underlying generating processes, the algorithm predicts future time series values based on past time series values for each generator. Unlike many algorithms, this algorithm predicts a distribution over future values. This prediction forms the basis for labelling part of a time series with the underlying generator that created it given some labelled exam piles. The algorithm is robust to a wide variety of possible types of changes in signals including mean shifts, amplitude changes, noise changes, period changes, and changes in signal shape. We improve upon the speed of our previous approach and show the utility of the algorithm for discriminating between different states of the robot/environment from robotic sensor signals.

CM-Trio-99

The robots used in this competition were generously provided by Sony [3]. The robots are the same as the commercial AIBO robots except for slight hardware changes and programming capabilities. These autonomous robots are about 30cm long and have 18 degrees of freedom. The neck pans ±90° allowing the robot to scan the field with its on board camera. Six uniquely colored landmarks are placed around the field (at the corners and center-line) to help the robots localize. Each team consists of three robots. Like our team last year, CMTrio-98 [5], we divided our team between two identical attackers and one goalie.

Integrating information, planning, and execution monitoring agents

In this paper, we report new work illustrating the integration of information agents, such as Web agents, with planning, and execution monitoring of multiple physical agents. Information agents, external to the planner and to the execution agents, specify the planning mission. The planner generates a plan which is then executed. Web agents again are requested to monitor the planrelevant features of the world. Replanning must occur dynamically when a failure is encountered. These techniques are combined in the CMUExpress architecture. The CMUExpress architecture demonstrates a solution to the integration of planning with real information and execution agents an Interaction Manager, that effectively maintains necessary links of communication and monitoring between the different sets of agents. We consider in particular multiple execution agents that must react to unforseen events while operating in the real world. Hence, in contrast to most information tasks where the world may not change while a query is being processed, we assume that the real world changes while we are solving the problem. In approaching such non-trivial real world problems, we recognize that desirable universal planning solutions may be impossible to reach as the real world is impossible to model completely. Replanning is inevitable, even to support probabilistic and conditional planning. We provide an approach to replanning that allows for the incorporation of guidance to minimally disturb the plan to be refined.