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Found 34 papers, 11 papers with code
Modeling Autonomous Shifts Between Focus State and Mind-Wandering Using a Predictive-Coding-Inspired Variational RNN Model
The current study investigates possible neural mechanisms underling autonomous shifts between focus state and mind-wandering by conducting model simulation experiments.
Life, uh, Finds a Way: Systematic Neural Search
We tackle the challenge of rapidly adapting an agent's behavior to solve spatiotemporally continuous problems in novel settings.
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle
Two rewards encouraging efficient exploration are the entropy of action policy and curiosity for information gain.
Development of Compositionality and Generalization through Interactive Learning of Language and Action of Robots
Our results show that generalization in learning to unlearned verb-noun compositions, is significantly enhanced when training variations of task composition are increased.
Comparing Generalization in Learning with Limited Numbers of Exemplars: Transformer vs. RNN in Attractor Dynamics
However, this raises a crucial question about Transformer's generalization in learning (GIL) capacity.
Habits and goals in synergy: a variational Bayesian framework for behavior
The habitual behavior is generated by using prior distribution of intention, which is goal-less; and the goal-directed behavior is generated by the posterior distribution of intention, which is conditioned on the goal.
Morphological Wobbling Can Help Robots Learn
We propose to make the physical characteristics of a robot oscillate while it learns to improve its behavioral performance.
Initialization of Latent Space Coordinates via Random Linear Projections for Learning Robotic Sensory-Motor Sequences
Robot kinematics data, despite being a high dimensional process, is highly correlated, especially when considering motions grouped in certain primitives.
Goal-directed Planning and Goal Understanding by Active Inference: Evaluation Through Simulated and Physical Robot Experiments
We show that goal-directed action planning and generation in a teleological framework can be formulated using the free energy principle.
Active Inference in Robotics and Artificial Agents: Survey and Challenges
Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning.
Emergence of sensory attenuation based upon the free-energy principle
Consequently, shifts between the two sensorimotor contexts triggered transitions from one free-energy state to another in the network via executive control, which caused shifts between attenuating and amplifying prediction-error-induced responses in the sensory areas.
Goal-Directed Planning by Reinforcement Learning and Active Inference
Habitual behavior, which is obtained from the prior distribution of ${z}$, is acquired by reinforcement learning.
Leading or Following? Dyadic Robot Imitative Interaction Using the Active Inference Framework
This study investigated how social interaction among robotic agents changes dynamically depending on the individual belief of action intention.
Learning Memory-Dependent Continuous Control from Demonstrations
This paper builds on the idea of replaying demonstrations for memory-dependent continuous control, by proposing a novel algorithm, Recurrent Actor-Critic with Demonstration and Experience Replay (READER).
Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution
Evolution and development operate at different timescales; generations for the one, a lifetime for the other.
Towards hybrid primary intersubjectivity: a neural robotics library for human science
Human-robot interaction is becoming an interesting area of research in cognitive science, notably, for the study of social cognition.
Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network
It is crucial to ask how agents can achieve goals by generating action plans using only partial models of the world acquired through habituated sensory-motor experiences.
Variational Recurrent Models for Solving Partially Observable Control Tasks
In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy.
Cognitive and motor compliance in intentional human-robot interaction
Embodiment and subjective experience in human-robot interaction are important aspects to consider when studying both natural cognition and adaptive robotics to human environments.
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory
Furthermore, our analysis of comparative experiments indicated that introduction of visual working memory and the inference mechanism using variational Bayes predictive coding significantly improve the performance in planning adequate goal-directed actions.
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
Furthermore, we show that the self-developed compositionality of the network enhances faster re-learning when adapting to a new task that is a re-composition of previously learned sub-goals, than when starting from scratch.
A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition
The model introduces a weighting parameter, the meta-prior, to balance the optimization pressure placed on two terms of a lower bound on the marginal likelihood of the sequential data.
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience.
A Dynamic Neural Network Approach to Generating Robot's Novel Actions: A Simulation Experiment
The results also showed that the different way of learning the basic actions induced the self-organization of the memory structure with the different characteristics, resulting in the generation of different levels of creative actions.
Generating Goal-Directed Visuomotor Plans Based on Learning Using a Predictive Coding-type Deep Visuomotor Recurrent Neural Network Model
The current paper presents how a predictive coding type deep recurrent neural networks can generate vision-based goal-directed plans based on prior learning experience by examining experiment results using a real arm robot.
Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model
The paper examines how model performance during pattern generation as well as predictive imitation varies depending on the stage of learning.
Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model
We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns.
Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning
This study presents a dynamic neural network model based on the predictive coding framework for perceiving and predicting the dynamic visuo-proprioceptive patterns.
Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach
This study investigates how adequate coordination among the different cognitive processes of a humanoid robot can be developed through end-to-end learning of direct perception of visuomotor stream.
Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition
In this paper, inspired by the normalization and detrending methods, we propose adaptive detrending (AD) for temporal normalization in order to accelerate the training of ConvRNNs, especially for convolutional gated recurrent unit (ConvGRU).
Predictive Coding for Dynamic Vision : Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model
The current paper presents a novel recurrent neural network model, the predictive multiple spatio-temporal scales RNN (P-MSTRNN), which can generate as well as recognize dynamic visual patterns in the predictive coding framework.
Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs
The paper presents a neurorobotics cognitive model to explain the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot.
Recognition of Visually Perceived Compositional Human Actions by Multiple Spatio-Temporal Scales Recurrent Neural Networks
The analysis of the internal representation obtained through the learning with the dataset clarifies what sorts of functional hierarchy can be developed by extracting the essential compositionality underlying the dataset.
Achieving Synergy in Cognitive Behavior of Humanoids via Deep Learning of Dynamic Visuo-Motor-Attentional Coordination
The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN).
