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Found 9 papers, 1 papers with code
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people.
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed.
A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots
This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model(PGM)-based cognitive system to develop a cognitive system for developmental robots by integrating PGMs.
World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges
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.
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning
Therefore, the agents change their actions according to the estimated messages to achieve cooperative tasks.
