Anticipatory robot control for a partially observable environment using episodic memories

Endo, Yoichiro

doi:10.1109/robot.2008.4543642

Cited by 21 publications

(14 citation statements)

References 20 publications

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“…In one experimental study two robots had the goal of exchanging places while navigating through an area with or without obstacles [8]. In another study, an episodic memory-based approach was proposed for computing anticipatory robot behavior in a partially observable environment, and the results of a robot navigation experiment were presented [9].…”

Section: Related Workmentioning

confidence: 99%

Mathematical modeling of the prediction mechanism of sensory processing in the context of a Bayes filter

Zhang

Suh

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Prediction is a very important element of human intelligence and plays a major role in human behavior, perception, and learning. This paper presents the development of a mathematical model of the prediction mechanism in the context of a Bayes filter, which is the predominant schema used for integrating temporal data in the field of robot mapping and localization problems. We propose a generalized anticipatory Bayes filter that uses revised sensor values obtained from the prediction process at the measurement-update step to enhance the performance of the sensor model. The development of a generalized anticipatory Bayes filter is not only an extension of the original Bayes filter, but also a mathematical model of the human prediction mechanism of sensory processing. This work was verified by experiments using observed data.

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Section: Related Workmentioning

confidence: 99%

Mathematical modeling of the prediction mechanism of sensory processing in the context of a Bayes filter

Zhang

Suh

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Similarly, for robots, the premise here is that experience does matter as well. 8 We argue that the learned experience can be integrated with the cognitive map to enable robot perceive and navigate freely in an environment. It is thus natural to seek inspiration in human experience in order to achieve their adaptivity and cognition.…”

Section: Introductionmentioning

confidence: 99%

A biological-inspired episodic cognitive map building framework for mobile robot navigation

Liu

Cong

Zou

et al. 2017

International Journal of Advanced Robotic Systems

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This article proposes a self-learning method of robotic experience for building episodic cognitive map using biologically inspired episodic memory. The episodic cognitive map is used for robot navigation under uncertainty. Two main challenges which include high computational complexity and perceptual aliasing are addressed. The episodic memory-driving Markov decision process is proposed to simulate the organization of episodic memory by introducing neuron activation and stimulation mechanism. Episodic memory self-learning model and algorithm are presented for building the episodic cognitive map based on episodic memory-driving Markov decision process. Uncertain information is considered to improve mapping performance. The presented method can realize robotic memory real-time storage, incremental accumulation, integration and updating. Based on the episodic cognitive map, the predicted episodic trajectory can simply be computed by activation spreading of state neurons. The experimental results for a mobile robot indicate that the method can efficiently performs learning, localization, mapping and navigation in real-life office environments.

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“…An experimental study was reported previously [6] in which two robots had the goal of exchanging places while navigating through an area with or without obstacles. In another study [7], the author proposed an episodic-memory-based approach for computing anticipatory robot behavior in a partially observable environment, and presented the results of a robotnavigation experiment.…”

Section: Introductionmentioning

confidence: 99%

Integration of a prediction mechanism with a sensor model: An anticipatory Bayes filter

Zhang

Suh

2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-In the task of robot localization, Bayes filters use two processes: the prediction step and the measurement-update step. Briefly, the state transition model is responsible for prediction, and the sensor model is responsible for measurement updates. This paper presents a new approach to the sensor model, called the predictive sensor model, which utilizes a prediction mechanism to improve the efficiency of measurement updates in Bayes filters. By adding sensorial anticipation, we extend the original Bayes filter to an anticipatory Bayes filter. We also propose an entropy-based place-segmentation method for automatic segmentation of sequentially collected visionsensor data. Our place segmentation technique is most useful for node clustering in the process of constructing topological maps. Our work was verified by experiments using observed data.

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Anticipatory robot control for a partially observable environment using episodic memories

Cited by 21 publications

References 20 publications

Mathematical modeling of the prediction mechanism of sensory processing in the context of a Bayes filter

Mathematical modeling of the prediction mechanism of sensory processing in the context of a Bayes filter

A biological-inspired episodic cognitive map building framework for mobile robot navigation

Integration of a prediction mechanism with a sensor model: An anticipatory Bayes filter

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