Toward an idiomatic framework for cognitive robotics

Damgaard, Malte Rørmose; Pedersen, Rasmus; Bak, Thomas

doi:10.1016/j.patter.2022.100533

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…However, complex machine learning methods that have rapidly advanced in a decade are not introduced yet. Sigma [240,241] is a newer cognitive architecture that introduces the generative flow graph, a generalized probabilistic graphical model. Therefore, the model can be implemented using probabilistic programming techniques [242][243][244].…”

Section: Cognitive Architecturesmentioning

confidence: 99%

World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges

Taniguchi¹,

Murata²,

Suzuki³

et al. 2023

Preprint

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Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Importantly, if the aim is to create robots that can continuously develop through interactions with their environment, their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e., controllers. Alternatively, in neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future.

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Section: Cognitive Architecturesmentioning

confidence: 99%

World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges

Taniguchi¹,

Murata²,

Suzuki³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This includes additional appraisal variables, the possibility of adding and using additional deliberate attention mechanisms, together with an implementation of simple mechanisms for deliberate attention proposal and affective responses. In order to make the implementation reusable in the spirit of the framework presented in [5], all of this is implemented as a series of abstract python classes each constituting a probabilistic programming idiom available at [26].…”

Section: Idiom Modifications and Extensionsmentioning

confidence: 99%

“…The next generation of cognitive architectures, based on modern machine learning techniques, has the potential to revolutionize robotics by allowing roboticists to develop such autonomous systems easily. In previous work, we proposed the Generalized Cognitive Hour-glass Model constituting a framework for developing cognitive architectures by composing them from generally applicable probabilistic programming idioms over which powerful general algorithms can perform inference [5]. The idiomatic approach to composing cognitive architectures, encouraged by this framework, allows researchers and practitioners to more easily cooperate by mixing and matching probabilistic programming idioms developed by others while being able to handcraft parts of a system for which current solutions do not suffice.…”

Section: Introductionmentioning

confidence: 99%

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Escaping Local Minima Via Appraisal Driven Responses

Damgaard¹,

Pedersen²,

Bak³

2022

Preprint

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Inspired by the reflective and deliberative control mechanisms used in cognitive architectures such as SOAR and Sigma, we propose an alternative decision mechanism driven by architectural appraisals allowing robots to overcome impasses. The presented work builds on and improves on our previous work on a generally applicable decision mechanism with roots in the Standard Model of the Mind and the Generalized Cognitive Hour-glass Model. The proposed decision mechanism provides automatic context-dependent switching between exploration-oriented, goal-oriented, and backtracking behavior, allowing a robot to overcome impasses. A simulation study of two applications utilizing the proposed decision mechanism is presented demonstrating the applicability of the proposed decision mechanism.

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