Author name cluster

Gregory Kuhlmann

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

ICRA Conference 2010 Conference Paper

Toward autonomous scientific exploration of ice-covered lakes - Field experiments with the ENDURANCE AUV in an Antarctic Dry Valley

Shilpa Gulati
Kristof Richmond
Christopher Flesher
Bart P. Hogan
Aniket Murarka
Gregory Kuhlmann
Mohan Sridharan
William C. Stone

Chemical properties of lake water can provide valuable insight into its ecology. Lakes that are permanently frozen over with ice are generally inaccessible to comprehensive exploration by humans. This paper describes the integration of several novel and existing technologies into an autonomous underwater robot, ENDURANCE, that was successfully used for gathering scientific data in West Lake Bonney in Taylor Valley, Antarctica, in December 2008. This paper focuses on three novel technological and algorithmic solutions. First, a robust position estimation system that uses an acoustic beacon to complement traditional dead-reckoning is described. Second, a novel vision-based docking algorithm for locating and ascending a vertical shaft by tracking a blinking light source is presented. Third, a novel profiling system for measuring water properties while causing minimal water disturbance is described. Finally, experimental results from the scientific missions in 2008 in West Lake Bonney are presented.

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AAMAS Conference 2008 Conference Paper

Autonomous Transfer for Reinforcement Learning

Matthew Taylor
Gregory Kuhlmann
Peter Stone

Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typically must be provided either a full model of the tasks or an explicit relation mapping one task into the other. An autonomous agent may not have access to such high-level information, but would be able to analyze its experience to find similarities between tasks. In this paper we introduce Modeling Approximate State Transitions by Exploiting Regression (MASTER), a method for automatically learning a mapping from one task to another through an agent’s experience. We empirically demonstrate that such learned relationships can significantly improve the speed of a reinforcement learning algorithm in a series of Mountain Car tasks. Additionally, we demonstrate that our method may also assist with the difficult problem of task selection for transfer.

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AAAI Conference 2006 Short Paper

Automatic Heuristic Construction for General Game Playing

Gregory Kuhlmann

Creating programs that can play games such as chess, checkers, and backgammon, at a high level has long been a challenge and benchmark for AI. While several game-playing systems developed in the past, such as Deep Blue, Chinook, and TD-gammon have demonstrated competitive play against human players, such systems are limited in that they play only one particular game and they must be supplied with large amounts of expert knowledge. General Game Playing is the more challenging problem of designing an agent capable of playing many different previously unseen games. The first AAAI General Game Playing Competition was held at 2005 AAAI meeting in Pittsburgh in order to promote research in this area. We were one of nine participants in that competition. We survey some of the issues involved in creating a general game playing system and introduce our entry to that event. The main feature of our approach is a novel method for automatically constructing effective search heuristics based on the formal game description. Our agent is fully implemented and tested in a range of different games.

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AAAI Conference 2006 Conference Paper

Automatic Heuristic Construction in a Complete General Game Player

Gregory Kuhlmann

Computer game players are typically designed to play a single game: today’s best chess-playing programs cannot play checkers, or even tic-tac-toe. General Game Playing is the problem of designing an agent capable of playing many different previously unseen games. The first AAAI General Game Playing Competition was held at AAAI 2005 in order to promote research in this area. In this article, we survey some of the issues involved in creating a general game playing system and introduce our entry to that event. The main feature of our approach is a novel method for automatically constructing effective search heuristics based on the formal game description. Our agent is fully implemented and tested in a range of different games.

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AAAI Conference 2006 Conference Paper

Know Thine Enemy: A Champion RoboCup Coach Agent

Gregory Kuhlmann

In a team-based multiagent system, the ability to construct a model of an opponent team’s joint behavior can be useful for determining an agent’s expected distribution over future world states, and thus can inform its planning of future actions. This paper presents an approach to team opponent modeling in the context of the RoboCup simulation coach competition. Specifically, it introduces an autonomous coach agent capable of analyzing past games of the current opponent, advising its own team how to play against this opponent, and identifying patterns or weaknesses on the part of the opponent. Our approach is fully implemented and tested within the RoboCup soccer server, and was the champion of the RoboCup 2005 simulation coach competition.

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ICRA Conference 2005 Conference Paper

Practical Vision-Based Monte Carlo Localization on a Legged Robot

Mohan Sridharan
Gregory Kuhlmann
Peter Stone 0001

Mobile robot localization, the ability of a robot to determine its global position and orientation, continues to be a major research focus in robotics. In most past cases, such localization has been studied on wheeled robots with range finding sensors such as sonar or lasers. In this paper, we consider the more challenging scenario of a legged robot localizing with a limited field-of-view camera as its primary sensory input. We begin with a baseline implementation adapted from the literature that provides a reasonable level of competence, but that exhibits some weaknesses in real-world tests. We propose a series of practical enhancements designed to improve the robot’s sensory and actuator models that enable our robots to achieve a 50% improvement in localization accuracy over the baseline implementation. We go on to demonstrate how the accuracy improvement is even more dramatic when the robot is subjected to large unmodeled movements. These enhancements are each individually straightforward, but together they provide a roadmap for avoiding potential pitfalls when implementing Monte Carlo Localization on vision-based and/or legged robots.

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