Search Results for author:
Found 14 papers, 3 papers with code
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer
Our findings have important implications for the design of RL algorithms for robot manipulation tasks, and highlight the need for careful consideration of action spaces when training and transferring RL agents for real-world robotics.
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
Our method is "zero-shot" in the sense that it does not require further training for the world model or online interactions with the environment after given the demonstration.
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces
Multi-robot manipulation tasks involve various control entities that can be separated into dynamically independent parts.
Exploration via Empowerment Gain: Combining Novelty, Surprise and Learning Progress
We show that while such an agent is still novelty seeking, i. e. interested in exploring the whole state space, it focuses on exploration where its perceived influence is greater, avoiding areas of greater stochasticity or traps that limit its control.
Learning to Fly via Deep Model-Based Reinforcement Learning
Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications.
Model-based Reinforcement Learning
reinforcement-learning
+1
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations
Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision making.
Unsupervised Real-Time Control through Variational Empowerment
We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control.
Variational Inference with Hamiltonian Monte Carlo
Variational inference lies at the core of many state-of-the-art algorithms.
Unsupervised preprocessing for Tactile Data
Tactile information is important for gripping, stable grasp, and in-hand manipulation, yet the complexity of tactile data prevents widespread use of such sensors.
ML-based tactile sensor calibration: A universal approach
We study the responses of two tactile sensors, the fingertip sensor from the iCub and the BioTac under different external stimuli.
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models.
Efficient Empowerment
This is a natural candidate for an intrinsic reward signal in the context of reinforcement learning: the agent will place itself in a situation where its action have maximum stability and maximum influence on the future.
Fast Adaptive Weight Noise
Marginalising out uncertain quantities within the internal representations or parameters of neural networks is of central importance for a wide range of learning techniques, such as empirical, variational or full Bayesian methods.
Improving approximate RPCA with a k-sparsity prior
A process centric view of robust PCA (RPCA) allows its fast approximate implementation based on a special form o a deep neural network with weights shared across all layers.
