a new entry in the ongoing series of RoboCup legged league competitions. The team development beg... more a new entry in the ongoing series of RoboCup legged league competitions. The team development began in mid-January of 2003, at which time none of the team members had any familiarity with the Aibos. Without using any RoboCup-related code from other teams, we entered a team in the American Open competition at the end of April, and met with some success at the annual RoboCup competition that took place in Padova, Italy at the beginning of July. In this paper, we describe aspects of (i) our development process and (ii ...
The UT Austin Villa Four-Legged Team for RoboCup 2004 was a second-time entry in the ongoing seri... more The UT Austin Villa Four-Legged Team for RoboCup 2004 was a second-time entry in the ongoing series of RoboCup legged league competitions. The team development began in mid-January of 2003 without any prior familiarity with the Aibos. After entering a fairly non-competitive team in RoboCup 2003, the team made several important advances. By the July 2004 competition place in Lisbon, Portugal, it was one of the top few teams. In this report, we describe both our development process and the technical details of its end ...
2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2012
ABSTRACT Mobile robots equipped with multiple sensors and deployed in real-world domains frequent... more ABSTRACT Mobile robots equipped with multiple sensors and deployed in real-world domains frequently find it difficult to process all sensor inputs, or to operate without any human input and domain knowledge. At the same time, robots cannot be equipped with all relevant domain knowledge in advance, and humans are unlikely to have the time and expertise to provide elaborate and accurate feedback. This paper presents a novel framework that addresses these challenges by integrating high-level logical inference with low-level probabilistic sequential decision-making. Specifically, Answer Set Programming (ASP), a non-monotonic logic programming paradigm, is used to represent, reason with and revise domain knowledge obtained from sensor inputs and high-level human feedback, while hierarchical partially observable Markov decision processes (POMDPs) are used to automatically adapt visual sensing and information processing to the task at hand. Furthermore, a psychophysics-inspired strategy is used to merge the output of logical inference with probabilistic beliefs. All algorithms are evaluated in simulation and on wheeled robots localizing target objects in indoor domains.
Our research focuses on automating the color-learning process on-board a legged robot with limite... more Our research focuses on automating the color-learning process on-board a legged robot with limited computational and memory resources. A key defining feature of our approach is that instead of using explicitly labeled training data it trains autonomously and incrementally, thereby making it robust to re-colorings in the environment. Prior results demonstrated the ability of the robot to learn a color map when given an executable motion sequence designed to present it with good color-learning opportunities based on the known ...
In 2008, UT Austin Villa entered a team in the first Nao competition of the Standard Platform Lea... more In 2008, UT Austin Villa entered a team in the first Nao competition of the Standard Platform League of the RoboCup competition. The team had previous experience in RoboCup in the Aibo leagues. Using this past experience, the team developed an entirely new codebase for the Nao. In 2009, UT Austin combined forces with Texas Tech University, to form TT-UT
2011 IEEE International Conference on Robotics and Automation, 2011
ABSTRACT Mobile robots are increasingly being used in real-world applications due to the ready av... more ABSTRACT Mobile robots are increasingly being used in real-world applications due to the ready availability of high fidelity sensors and the development of sophisticated information processing algorithms. However, one key challenge to the widespread deployment of mobile robots equipped with multiple sensors and processing algorithms is the ability to autonomously tailor sensing and information processing to the task at hand. This paper poses this challenge as the task of planning under uncertainty, and more specifically as an instance of probabilistic sequential decision-making. A novel hierarchy of partially observable Markov decision processes (POMDPs) is incorporated, which uses constrained-convolutional policies and automatic belief propagation to achieve efficient and reliable operation on mobile robots. All algorithms are implemented and evaluated on simulated and physical robot platforms for the task of searching for target objects in dynamic indoor environments.
2009 9th IEEE-RAS International Conference on Humanoid Robots, 2009
Mobile robots equipped with multiple sensors are increasingly being used in specific real-world a... more Mobile robots equipped with multiple sensors are increasingly being used in specific real-world applications, primarily because of the ready availability of high-fidelity sensors. A robot equipped with multiple sensors, however, obtains information about different regions of the scene, in different formats and with varying levels of uncertainty. One open challenge to the widespread deployment of robots is the ability to fully utilize the information obtained from each sensor in order to operate robustly in dynamic environments. This paper presents a probabilistic approach for autonomous multisensor information fusion on a humanoid robot. The robot exploits the known structure of the environment to autonomously model the expected performance of the individual information processing schemes. The learned models are used to effectively merge the available information. As a result, the robot is able to localize mobile obstacles in its environment. The algorithm is fully implemented and tested on a physical robot platform.
2009 6th Latin American Robotics Symposium (LARS 2009), 2009
Recent developments in sensor technology have resulted in the deployment of mobile robots equippe... more Recent developments in sensor technology have resulted in the deployment of mobile robots equipped with multiple sensors, in specific real-world applications. A robot equipped with multiple sensors, however, obtains information about different regions of the scene, in different formats and with varying levels of uncertainty. In addition, the bits of information obtained from different sensors may contradict or complement each
The study of architectures to support intelligent behaviour is certainly the broadest, and arguab... more The study of architectures to support intelligent behaviour is certainly the broadest, and arguably one of the most ill-defined enterprises in AI and Cognitive Science. The basic scientific question we seek to answer is: “What are the trade-offs between the different ways that intelligent systems might be structured?” These trade-offs depend in large part on what kinds of tasks and
a new entry in the ongoing series of RoboCup legged league competitions. The team development beg... more a new entry in the ongoing series of RoboCup legged league competitions. The team development began in mid-January of 2003, at which time none of the team members had any familiarity with the Aibos. Without using any RoboCup-related code from other teams, we entered a team in the American Open competition at the end of April, and met with some success at the annual RoboCup competition that took place in Padova, Italy at the beginning of July. In this paper, we describe aspects of (i) our development process and (ii ...
The UT Austin Villa Four-Legged Team for RoboCup 2004 was a second-time entry in the ongoing seri... more The UT Austin Villa Four-Legged Team for RoboCup 2004 was a second-time entry in the ongoing series of RoboCup legged league competitions. The team development began in mid-January of 2003 without any prior familiarity with the Aibos. After entering a fairly non-competitive team in RoboCup 2003, the team made several important advances. By the July 2004 competition place in Lisbon, Portugal, it was one of the top few teams. In this report, we describe both our development process and the technical details of its end ...
2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2012
ABSTRACT Mobile robots equipped with multiple sensors and deployed in real-world domains frequent... more ABSTRACT Mobile robots equipped with multiple sensors and deployed in real-world domains frequently find it difficult to process all sensor inputs, or to operate without any human input and domain knowledge. At the same time, robots cannot be equipped with all relevant domain knowledge in advance, and humans are unlikely to have the time and expertise to provide elaborate and accurate feedback. This paper presents a novel framework that addresses these challenges by integrating high-level logical inference with low-level probabilistic sequential decision-making. Specifically, Answer Set Programming (ASP), a non-monotonic logic programming paradigm, is used to represent, reason with and revise domain knowledge obtained from sensor inputs and high-level human feedback, while hierarchical partially observable Markov decision processes (POMDPs) are used to automatically adapt visual sensing and information processing to the task at hand. Furthermore, a psychophysics-inspired strategy is used to merge the output of logical inference with probabilistic beliefs. All algorithms are evaluated in simulation and on wheeled robots localizing target objects in indoor domains.
Our research focuses on automating the color-learning process on-board a legged robot with limite... more Our research focuses on automating the color-learning process on-board a legged robot with limited computational and memory resources. A key defining feature of our approach is that instead of using explicitly labeled training data it trains autonomously and incrementally, thereby making it robust to re-colorings in the environment. Prior results demonstrated the ability of the robot to learn a color map when given an executable motion sequence designed to present it with good color-learning opportunities based on the known ...
In 2008, UT Austin Villa entered a team in the first Nao competition of the Standard Platform Lea... more In 2008, UT Austin Villa entered a team in the first Nao competition of the Standard Platform League of the RoboCup competition. The team had previous experience in RoboCup in the Aibo leagues. Using this past experience, the team developed an entirely new codebase for the Nao. In 2009, UT Austin combined forces with Texas Tech University, to form TT-UT
2011 IEEE International Conference on Robotics and Automation, 2011
ABSTRACT Mobile robots are increasingly being used in real-world applications due to the ready av... more ABSTRACT Mobile robots are increasingly being used in real-world applications due to the ready availability of high fidelity sensors and the development of sophisticated information processing algorithms. However, one key challenge to the widespread deployment of mobile robots equipped with multiple sensors and processing algorithms is the ability to autonomously tailor sensing and information processing to the task at hand. This paper poses this challenge as the task of planning under uncertainty, and more specifically as an instance of probabilistic sequential decision-making. A novel hierarchy of partially observable Markov decision processes (POMDPs) is incorporated, which uses constrained-convolutional policies and automatic belief propagation to achieve efficient and reliable operation on mobile robots. All algorithms are implemented and evaluated on simulated and physical robot platforms for the task of searching for target objects in dynamic indoor environments.
2009 9th IEEE-RAS International Conference on Humanoid Robots, 2009
Mobile robots equipped with multiple sensors are increasingly being used in specific real-world a... more Mobile robots equipped with multiple sensors are increasingly being used in specific real-world applications, primarily because of the ready availability of high-fidelity sensors. A robot equipped with multiple sensors, however, obtains information about different regions of the scene, in different formats and with varying levels of uncertainty. One open challenge to the widespread deployment of robots is the ability to fully utilize the information obtained from each sensor in order to operate robustly in dynamic environments. This paper presents a probabilistic approach for autonomous multisensor information fusion on a humanoid robot. The robot exploits the known structure of the environment to autonomously model the expected performance of the individual information processing schemes. The learned models are used to effectively merge the available information. As a result, the robot is able to localize mobile obstacles in its environment. The algorithm is fully implemented and tested on a physical robot platform.
2009 6th Latin American Robotics Symposium (LARS 2009), 2009
Recent developments in sensor technology have resulted in the deployment of mobile robots equippe... more Recent developments in sensor technology have resulted in the deployment of mobile robots equipped with multiple sensors, in specific real-world applications. A robot equipped with multiple sensors, however, obtains information about different regions of the scene, in different formats and with varying levels of uncertainty. In addition, the bits of information obtained from different sensors may contradict or complement each
The study of architectures to support intelligent behaviour is certainly the broadest, and arguab... more The study of architectures to support intelligent behaviour is certainly the broadest, and arguably one of the most ill-defined enterprises in AI and Cognitive Science. The basic scientific question we seek to answer is: “What are the trade-offs between the different ways that intelligent systems might be structured?” These trade-offs depend in large part on what kinds of tasks and
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