Integrating perception, action and learning

Antonisse, Jim; Wechsler, Harry

doi:10.1145/122344.122347

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Over the years a number of articles have been written about Perception-Action (PA) issues [4,34,41]. Still, there is a great deal to add to this discourse.…”

Section: Perception-action Learningmentioning

confidence: 99%

Exploratory learning structures in artificial cognitive systems

Felsberg

Wiklund

Granlund

2009

Image and Vision Computing

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The major goal of the COSPAL project is to develop an artificial cognitive system architecture, with the ability to autonomously extend its capabilities. Exploratory learning is one strategy that allows an extension of competences as provided by the environment of the system. Whereas classical learning methods aim at best for a parametric generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class, and to apply generalization on a conceptual level, resulting in new models. Incremental or online learning is a crucial requirement to perform exploratory learning.In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning, and in this paper we focus on the organization of cognitive systems for efficient operation. Learning is used over the entire system. It is organized in the form of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception-action cycles. We present a system diagram which explains this process in more detail.We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user ('teacher') and system is a major difference to classical robotics systems, where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems.We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module. We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.

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“…Over the years a number of articles have been written about Perception-Action (PA) issues [4,34,41]. Still, there is a great deal to add to this discourse.…”

Section: Perception-action Learningmentioning

confidence: 99%

Exploratory learning structures in artificial cognitive systems

Felsberg

Wiklund

Granlund

2009

Image and Vision Computing

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Will intelligent vehicles evolve into Human-peer Robots?

Lio

Biral

Galvani

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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This paper aims at stimulating the discussion on the future of Intelligent Vehicles. It is a position paper, indicating converging technologies that, in our opinion, will have to be used in future Intelligent Vehicles. We present a vision according to which Intelligent Vehicles will evolve into Human-peer Robots, here called Co-Drivers. Co-Drivers will be able to "understand" human drivers and to form symbiotic systems with them. The general architecture of Co-Drivers, the building blocks and the technologies that are needed to bring them to life are discussed, pointing out which parts have been already researched and which gaps still remain. We clarify what "understanding driver" actually means and how a joint system can be obtained. The paper will identify research needs and paths, and hopefully trigger interest. I. INTRODUCTION. THE LOST "HORSE"? ANKIND used animals, and especially horses, as transportation systems for thousand years. Last century they were eventually replaced by motor vehicles because of range, speed, capacity and costs. However, something was lost: the intelligence of the animals was traded with power. In a recent book [1], Norman recalls the interaction between a rider and a horse as one example of how future intelligent things should work: Think of skilled horseback riders. The rider "reads" the horse, just as the horse can read its rider. (…). This interaction (…) is of special interest because it is an example of two sentient systems, horse and rider, both intelligent, both interpreting the world and communicating their interpretations to each other. According to Norman, here the key point is the peer-topeer cooperation of two "sentient" systems. In this example, a man and an animal, but the animal stands for a future intelligent artificial system. Norman was not the only one to point out such a smart collaboration. The H-metaphor (i.e., rider-horse metaphor) was also proposed by Flemish, originally in the aerospace Manuscript received January 15, 2012. This work was supported in part by the European Commission under Grants FP6 507075 (PReVENT), FP7 216355 (SAFERIDER), FP7 246587 (interactIVe).

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Integrating perception, action and learning

Cited by 2 publications

References 5 publications

Exploratory learning structures in artificial cognitive systems

Exploratory learning structures in artificial cognitive systems

Will intelligent vehicles evolve into Human-peer Robots?

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