Model-Free Learning and Control in a Mobile Robot

Rohrer, Brandon R.; Bernard, Michael L.; Morrow, James D.; Rothganger, Fred; Xavier, Patrick G.

doi:10.1109/icnc.2009.38

Cited by 8 publications

(13 citation statements)

References 10 publications

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“…One important issue, promoted both by Rohrer et al [40,41] and ourselves [10], is the ability to learn even with limited prior knowledge of what is to be learned. Prior knowledge is information intentionally introduced into the system to support learning, often referred to as ontological bias or design bias [10].…”

Section: Discussionmentioning

confidence: 99%

“…Inspiration is taken from the Hierarchical Temporal Memory algorithm [24], with focus on introducing as few assumptions into learning as possible. More recently, it has been applied as a model-free reinforcement learning algorithm for both simulated and physical robots [40,41]. We have also evaluated S-Learning as an algorithm for behavior recognition [9].…”

Section: Sequence Learningmentioning

confidence: 99%

“…A novel learning algorithm, called Predictive Sequence Learning (PSL), is here presented and evaluated. PSL is inspired by S-Learning [42,43], which has previously been applied to robot learning problems as a model-free reinforcement learning algorithm [40,41].…”

Section: Introductionmentioning

confidence: 99%

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Predictive Learning from Demonstration

Billing

Hellström

Janlert

2011

Communications in Computer and Information Science

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Abstract.A model-free learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL is inspired by several functional models of the brain. It constructs sequences of predictable sensory-motor patterns, without relying on predefined higher-level concepts. The algorithm is demonstrated on a Khepera II robot in four different tasks. During training, PSL generates a hypothesis library from demonstrated data. The library is then used to control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. In this way, the robot reproduces the demonstrated behavior. PSL is able to successfully learn and repeat three elementary tasks, but is unable to repeat a fourth, composed behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.

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Section: Discussionmentioning

confidence: 99%

Section: Sequence Learningmentioning

confidence: 99%

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Predictive Learning from Demonstration

Billing

Hellström

Janlert

2011

Communications in Computer and Information Science

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“…It takes inspiration from the Hierarchical Temporal Memory algorithm (George and Hawkins, 2005), with focus on introducing as few assumptions into learning as possible. More recently, it has been applied as a model-free reinforcement learning algorithm for both simulated and physical robots (Rohrer, 2009;Rohrer et al, 2009). We have also evaluated S-Learning as an algorithm for behavior recognition (Billing and Hellstrm, 2008a).…”

Section: Sequence Learningmentioning

confidence: 99%

“…A novel learning algorithm, called Predictive Sequence Learning (PSL), is here presented and evaluated. PSL is inspired by S-Learning (Rohrer and Hulet, 2006a;Rohrer and Hulet, 2006b), which has previously been applied to robot learning problems as a model-free reinforcement learning algorithm (Rohrer, 2009;Rohrer et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Model-Free Learning From Demonstration

Billing

Hellström

Janlert

2010

Proceedings of the 2nd International Conference on Agents and Artificial Intelligence

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Abstract:A novel robot learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated. PSL is a model-free prediction algorithm inspired by the dynamic temporal difference algorithm S-Learning. While S-Learning has previously been applied as a reinforcement learning algorithm for robots, PSL is here applied to a Learning from Demonstration problem. The proposed algorithm is evaluated on four tasks using a Khepera II robot. PSL builds a model from demonstrated data which is used to repeat the demonstrated behavior. After training, PSL can control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. PSL was able to successfully learn and repeat the first three (elementary) tasks, but it was unable to successfully repeat the fourth (composed) behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.

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40 years of cognitive architectures: core cognitive abilities and practical applications

2018

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In this paper we present a broad overview of the last 40 years of research on cognitive architectures. To date, the number of existing architectures has reached several hundred, but most of the existing surveys do not reflect this growth and instead focus on a handful of well-established architectures. In this survey we aim to provide a more inclusive and highlevel overview of the research on cognitive architectures. Our final set of 84 architectures includes 49 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience. To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, action selection, memory, learning, reasoning and metareasoning. In order to assess the breadth of practical applications of cognitive architectures we present information on over 900 practical projects implemented using the cognitive architectures in our list. We use various visualization techniques to highlight the overall trends in the development of the field. In addition to summarizing the current state-of-the-art in the cognitive architecture research, this survey describes a variety of methods and ideas that have been tried and their relative success in modeling human cognitive abilities, as well as which aspects of cognitive behavior need more research with respect to their mechanistic counterparts and thus can further inform how cognitive science might progress.

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Model-Free Learning and Control in a Mobile Robot

Cited by 8 publications

References 10 publications

Predictive Learning from Demonstration

Predictive Learning from Demonstration

Model-Free Learning From Demonstration

40 years of cognitive architectures: core cognitive abilities and practical applications

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