Search Results for author: Karla Stepanova

Found 6 papers, 4 papers with code

Closed Loop Interactive Embodied Reasoning for Robot Manipulation

no code implementations23 Apr 2024 Michal Nazarczuk, Jan Kristof Behrens, Karla Stepanova, Matej Hoffmann, Krystian Mikolajczyk

Embodied reasoning systems integrate robotic hardware and cognitive processes to perform complex tasks typically in response to a natural language query about a specific physical environment.

Robot Manipulation

Bridging Language, Vision and Action: Multimodal VAEs in Robotic Manipulation Tasks

1 code implementation2 Apr 2024 Gabriela Sejnova, Michal Vavrecka, Karla Stepanova

A more lightweight alternative would be the implementation of multimodal Variational Autoencoders (VAEs) which can extract the latent features of the data and integrate them into a joint representation, as has been demonstrated mostly on image-image or image-text data for the state-of-the-art models.

Adaptive Compression of the Latent Space in Variational Autoencoders

no code implementations11 Dec 2023 Gabriela Sejnova, Michal Vavrecka, Karla Stepanova

Variational Autoencoders (VAEs) are powerful generative models that have been widely used in various fields, including image and text generation.

Text Generation

Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators

1 code implementation16 Sep 2022 Jiri Sedlar, Karla Stepanova, Radoslav Skoviera, Jan K. Behrens, Matus Tuna, Gabriela Sejnova, Josef Sivic, Robert Babuska

This paper introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera.

6D Pose Estimation 6D Pose Estimation using RGB +2

Benchmarking Multimodal Variational Autoencoders: CdSprites+ Dataset and Toolkit

1 code implementation7 Sep 2022 Gabriela Sejnova, Michal Vavrecka, Karla Stepanova

Multimodal Variational Autoencoders (VAEs) have been the subject of intense research in the past years as they can integrate multiple modalities into a joint representation and can thus serve as a promising tool for both data classification and generation.

Benchmarking

Where is my forearm? Clustering of body parts from simultaneous tactile and linguistic input using sequential mapping

1 code implementation8 Jun 2017 Karla Stepanova, Matej Hoffmann, Zdenek Straka, Frederico B. Klein, Angelo Cangelosi, Michal Vavrecka

In species that use language, this process is further structured by this interaction, where a mapping between the sensorimotor concepts and linguistic elements needs to be established.

Clustering

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