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Found 39 papers, 5 papers with code
Active Inference and Intentional Behaviour
First, we simulate the aforementioned in vitro experiments, in which neuronal cultures spontaneously learn to play Pong, by implementing nested, free energy minimising processes.
Supervised structure learning
This paper concerns structure learning or discovery of discrete generative models.
Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference for navigation in Multi-Room Maze Environments
Cognitive maps play a crucial role in facilitating flexible behaviour by representing spatial and conceptual relationships within an environment.
Bridging Cognitive Maps: a Hierarchical Active Inference Model of Spatial Alternation Tasks and the Hippocampal-Prefrontal Circuit
Through a series of simulations, we demonstrate that the model's dual layers acquire effective cognitive maps for navigation within physical (HC map) and task (mPFC map) spaces, using a biologically-inspired approach: a clone-structured cognitive graph.
Integrating cognitive map learning and active inference for planning in ambiguous environments
Living organisms need to acquire both cognitive maps for learning the structure of the world and planning mechanisms able to deal with the challenges of navigating ambiguous environments.
FOCUS: Object-Centric World Models for Robotics Manipulation
Understanding the world in terms of objects and the possible interplays with them is an important cognition ability, especially in robotics manipulation, where many tasks require robot-object interactions.
Inferring Hierarchical Structure in Multi-Room Maze Environments
Cognitive maps play a crucial role in facilitating flexible behaviour by representing spatial and conceptual relationships within an environment.
Active Inference in Hebbian Learning Networks
This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents.
Object-Centric Scene Representations using Active Inference
Representing a scene and its constituent objects from raw sensory data is a core ability for enabling robots to interact with their environment.
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with the ability to control and influence the environment.
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning
This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation.
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
In this work, we study the URLB and propose a new method to solve it, using unsupervised model-based RL, for pre-training the agent, and a task-aware fine-tuning strategy combined with a new proposed hybrid planner, Dyna-MPC, to adapt the agent for downstream tasks.
Disentangling Shape and Pose for Object-Centric Deep Active Inference Models
Recently, deep learning methods have been proposed to learn a hidden state space structure purely from data, alleviating the experimenter from this tedious design task, but resulting in an entangled, non-interpreteable state space.
Home Run: Finding Your Way Home by Imagining Trajectories
When studying unconstrained behaviour and allowing mice to leave their cage to navigate a complex labyrinth, the mice exhibit foraging behaviour in the labyrinth searching for rewards, returning to their home cage now and then, e. g. to drink.
Learning Generative Models for Active Inference using Tensor Networks
The ability of tensor networks to represent the probabilistic nature of quantum states as well as to reduce large state spaces makes tensor networks a natural candidate for active inference.
The Free Energy Principle for Perception and Action: A Deep Learning Perspective
The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states of the world, i. e., they minimize their free energy.
Contrastive Active Inference
In this work, we propose a contrastive objective for active inference that strongly reduces the computational burden in learning the agent's generative model and planning future actions.
Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach
Currently however, people detection systems used on drones are solely based on standard cameras besides an emerging number of works discussing the fusion of imaging and event-based cameras.
Disentangling What and Where for 3D Object-Centric Representations Through Active Inference
In this paper, we propose an active inference agent that actively gathers evidence for object classifications, and can learn novel object categories over time.
Towards bio-inspired unsupervised representation learning for indoor aerial navigation
Aerial navigation in GPS-denied, indoor environments, is still an open challenge.
LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping
Biologically inspired algorithms for simultaneous localization and mapping (SLAM) such as RatSLAM have been shown to yield effective and robust robot navigation in both indoor and outdoor environments.
A learning gap between neuroscience and reinforcement learning
Historically, artificial intelligence has drawn much inspiration from neuroscience to fuel advances in the field.
Curiosity-Driven Exploration via Latent Bayesian Surprise
The human intrinsic desire to pursue knowledge, also known as curiosity, is considered essential in the process of skill acquisition.
Dynamic Narrowing of VAE Bottlenecks Using GECO and L0 Regularization
In case the dimensionality is not predefined, this parameter is usually determined using time- and resource-consuming cross-validation.
Deep Active Inference for Autonomous Robot Navigation
Active inference is a theory that underpins the way biological agent's perceive and act in the real world.
Rhythm, Chord and Melody Generation for Lead Sheets using Recurrent Neural Networks
Music that is generated by recurrent neural networks often lacks a sense of direction and coherence.
Learning Perception and Planning with Deep Active Inference
Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy.
Learning to Catch Piglets in Flight
To develop and test our method, we start with an easy to identify object: a stuffed Piglet.
A Survey on Distributed Machine Learning
The demand for artificial intelligence has grown significantly over the last decade and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration.
Software Engineering Practices for Machine Learning
In the last couple of years we have witnessed an enormous increase of machine learning (ML) applications.
Bayesian policy selection using active inference
Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task.
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations
In this work we explore the generalization characteristics of unsupervised representation learning by leveraging disentangled VAE's to learn a useful latent space on a set of relational reasoning problems derived from Raven Progressive Matrices.
Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification
In this paper, we try to open the black box of the CNN by inspecting and visualizing the learned feature maps, in the field of dermatology.
Learning to Grasp from a Single Demonstration
Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world.
Privacy Aware Offloading of Deep Neural Networks
Deep neural networks require large amounts of resources which makes them hard to use on resource constrained devices such as Internet-of-things devices.
Transfer Learning with Binary Neural Networks
Binary neural networks are attractive in this case because the logical operations are very fast and efficient when implemented in hardware.
Decoupled Learning of Environment Characteristics for Safe Exploration
However, when learning a task using reinforcement learning, the agent cannot distinguish the characteristics of the environment from those of the task.
Sensor Fusion for Robot Control through Deep Reinforcement Learning
Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy.
Lazy Evaluation of Convolutional Filters
In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network.
