Search Results for author:
Found 11 papers, 5 papers with code
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.
Maximum Causal Entropy Inverse Constrained Reinforcement Learning
To address this challenge, we propose a novel method that utilizes the principle of maximum causal entropy to learn constraints and an optimal policy that adheres to these constraints, using demonstrations of agents that abide by the constraints.
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.
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.
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.
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.
