Cognitive control for robot task execution

Ratanaswasd, P.; Gordon, Stephen; Dodd, Will

doi:10.1109/roman.2005.1513818

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…During the past decade there have been several approaches that implemented episodic and semantic long-term memory in artificial systems (e.g., [5,6,10,13,14,15,16,18,22]). We believe that these works addressed only partially some of the main challenges that poses the implementation of long-term memory for social robots, and that still some of the questions described in the next paragraphs need to be answered.…”

Section: Open Questions For Implementing Long-term Memory For Social mentioning

confidence: 99%

Towards Long-Term Memory for Social Robots: Proposing a New Challenge for the RoboCup@Home League

Pavez

Ruiz‐del‐Solar

Amo

et al. 2019

RoboCup 2018: Robot World Cup XXII

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Long-term memory is essential to feel like a continuous being, and to be able to interact/communicate coherently. Social robots need long-term memories in order to establish long-term relationships with humans and other robots, and do not act just for the moment. In this paper this challenge is highlighted, open questions are identified, the need of addressing this challenge in the RoboCup@Home League with new tests is motivated, and a new test is proposed.

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Section: Open Questions For Implementing Long-term Memory For Social mentioning

confidence: 99%

Towards Long-Term Memory for Social Robots: Proposing a New Challenge for the RoboCup@Home League

Pavez

Ruiz‐del‐Solar

Amo

et al. 2019

RoboCup 2018: Robot World Cup XXII

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“…Cognition may provide a solution to overcome the limitations of AI, allowing us to create cognitive robots that can execute new tasks by combining knowledge gathered in previous experiences and emotions (Ratanaswasd et al, 2005). Through cognition, the human brain is able to acquire and process perceptions of the surrounding environment in a very fast and efficient way.…”

Section: Cognitive Robot Frameworkmentioning

confidence: 99%

“…A central agent is responsible for building and organizing the tasks in order to accomplish a requested goal (Ratanaswasd et al, 2005;Alami et al, 2006). Only two levels of tasks exist.…”

Section: Task Managementmentioning

confidence: 99%

“…They employ perceptions of the surrounding environment which are based on precise data from distance sensors (infrared, ultrasonic, laser) in combination with precise odometry for navigation. They may allow robots to perform simple tasks in controlled environments, but are less appropriate for highly dynamic and complex environments (Ratanaswasd et al, 2005). The latter require flexible and adaptive systems, i.e., cognitive ones like our brain.…”

Section: Introductionmentioning

confidence: 99%

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Minimalistic Vision-Based Cognitive Slam

Saleiro

Rodrigues

Buf

2011

Proceedings of the 4th International Conference on Agents and Artificial Intelligence

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Abstract:The interest in cognitive robotics is still increasing, a major goal being to create a system which can adapt to dynamic environments and which can learn from its own experiences. We present a new cognitive SLAM architecture, but one which is minimalistic in terms of sensors and memory. It employs only one camera with pan and tilt control and three memories, without additional sensors nor any odometry. Short-term memory is an egocentric map which holds information at close range at the actual robot position. Long-term memory is used for mapping the environment and registration of encountered objects. Object memory holds features of learned objects which are used as navigation landmarks and task targets. Saliency maps are used to sequentially focus important areas for object and obstacle detection, but also for selecting directions of movements. Reinforcement learning is used to consolidate or enfeeble environmental information in long-term memory. The system is able to achieve complex tasks by executing sequences of visuomotor actions, decisions being taken by goal-detection and goal-completion tasks. Experimental results show that the system is capable of executing tasks like localizing specific objects while building a map, after which it manages to return to the start position even when new obstacles have appeared.

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