Search Results for author:
Found 16 papers, 4 papers with code
ED-VAE: Entropy Decomposition of ELBO in Variational Autoencoders
Traditional Variational Autoencoders (VAEs) are constrained by the limitations of the Evidence Lower Bound (ELBO) formulation, particularly when utilizing simplistic, non-analytic, or unknown prior distributions.
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation
One of the most critical aspects of multimodal Reinforcement Learning (RL) is the effective integration of different observation modalities.
Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement
Interactive Machine Learning (IML) seeks to integrate human expertise into machine learning processes.
Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training
In evaluating our methodology, we focus on two distinct tasks: material classification and grasping success prediction.
CR-VAE: Contrastive Regularization on Variational Autoencoders for Preventing Posterior Collapse
The Variational Autoencoder (VAE) is known to suffer from the phenomenon of \textit{posterior collapse}, where the latent representations generated by the model become independent of the inputs.
Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics
We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios.
Using Probabilistic Movement Primitives in Analyzing Human Motion Difference under Transcranial Current Stimulation
The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks.
Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers
A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems.
SKID RAW: Skill Discovery from Raw Trajectories
Integrating robots in complex everyday environments requires a multitude of problems to be solved.
Learning Human Postural Control with Hierarchical Acquisition Functions
In order to reach similar performance, we developed a hierarchical Bayesian optimization algorithm that replicates the cognitive inference and memorization process for avoiding failures in motor control tasks.
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture
A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems.
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer
A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems.
Experience Reuse with Probabilistic Movement Primitives
Accordingly, for learning a new task, time could be saved by restricting the parameter search space by initializing it with the solution of a similar task.
Learning walk and trot from the same objective using different types of exploration
In quadruped gait learning, policy search methods that scale high dimensional continuous action spaces are commonly used.
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behavior that reflects only a single intention.
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds.
