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Found 23 papers, 7 papers with code
Confidence-Aware Decision-Making and Control for Tool Selection
To evaluate our theoretical account, we framed the decision-making within the tool selection problem, where the agent has to select the best robot arm for a particular control task.
Adaptive Noise Covariance Estimation under Colored Noise using Dynamic Expectation Maximization
The accurate estimation of the noise covariance matrix (NCM) in a dynamic system is critical for state estimation and control, as it has a major influence in their optimality.
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.
Closed-form control with spike coding networks
The neuroscience theory of Spike Coding Networks (SCNs) offers a fully analytical solution for implementing dynamical systems in recurrent spiking neural networks -- while maintaining irregular, sparse, and robust spiking activity -- but it's not clear how to directly apply it to control problems.
Learning Policies for Continuous Control via Transition Models
It is doubtful that animals have perfect inverse models of their limbs (e. g., what muscle contraction must be applied to every joint to reach a particular location in space).
The Role of Valence and Meta-awareness in Mirror Self-recognition Using Hierarchical Active Inference
The underlying processes that enable self-perception are crucial for understanding multisensory integration, body perception and action, and the development of the self.
Adaptation through prediction: multisensory active inference torque control
Adaptation to external and internal changes is major for robotic systems in uncertain environments.
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.
Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization
In the experiments, we show that applying regularization on membrane potential and spiking output successfully avoids both dead and bursting neurons and significantly decreases the reconstruction error of the spiking auto-encoder.
Robot Localization and Navigation through Predictive Processing using LiDAR
Knowing the position of the robot in the world is crucial for navigation.
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem
Despite the potential of active inference for visual-based control, learning the model and the preferences (priors) while interacting with the environment is challenging.
Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception
Unlike robots, humans learn, adapt and perceive their bodies by interacting with the world.
Multimodal VAE Active Inference Controller
Active inference, a theoretical construct inspired by brain processing, is a promising alternative to control artificial agents.
Robot in the mirror: toward an embodied computational model of mirror self-recognition
Self-recognition or self-awareness is a capacity attributed typically only to humans and few other species.
Deep Active Inference for Partially Observable MDPs
Deep active inference has been proposed as a scalable approach to perception and action that deals with large policy and state spaces.
A deep active inference model of the rubber-hand illusion
Understanding how perception and action deal with sensorimotor conflicts, such as the rubber-hand illusion (RHI), is essential to understand how the body adapts to uncertain situations.
Robot self/other distinction: active inference meets neural networks learning in a mirror
Self/other distinction and self-recognition are important skills for interacting with the world, as it allows humans to differentiate own actions from others and be self-aware.
End-to-End Pixel-Based Deep Active Inference for Body Perception and Action
We present a pixel-based deep active inference algorithm (PixelAI) inspired by human body perception and action.
Tactile Hallucinations on Artificial Skin Induced by Homeostasis in a Deep Boltzmann Machine
In this work, we investigate the generation of tactile hallucinations on biologically inspired, artificial skin.
A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder
This survey presents the most relevant neural network models of autism spectrum disorder and schizophrenia, from the first connectionist models to recent deep network architectures.
Active inference body perception and action for humanoid robots
We present an active inference body perception and action model working for the first time in a humanoid robot.
Sensorimotor learning for artificial body perception
Artificial self-perception is the machine ability to perceive its own body, i. e., the mastery of modal and intermodal contingencies of performing an action with a specific sensors/actuators body configuration.
Attention-based Active Visual Search for Mobile Robots
In this paper, we propose a new model that actively extracts visual information via visual attention techniques and, in conjunction with a non-myopic decision-making algorithm, leads the robot to search more relevant areas of the environment.
