5 research outputs found
Benchmarking Multimodal Variational Autoencoders: GeBiD Dataset and Toolkit
Multimodal Variational Autoencoders (VAEs) have been a 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. Several approaches toward multimodal VAE
learning have been proposed so far, their comparison and evaluation have
however been rather inconsistent. One reason is that the models differ at the
implementation level, another problem is that the datasets commonly used in
these cases were not initially designed for the evaluation of multimodal
generative models. This paper addresses both mentioned issues. First, we
propose a toolkit for systematic multimodal VAE training and comparison.
Second, we present a synthetic bimodal dataset designed for a comprehensive
evaluation of the joint generation and cross-generation capabilities. We
demonstrate the utility of the dataset by comparing state-of-the-art models
How language of interaction affects the user perception of a robot
Spoken language is the most natural way for a human to communicate with a
robot. It may seem intuitive that a robot should communicate with users in
their native language. However, it is not clear if a user's perception of a
robot is affected by the language of interaction.
We investigated this question by conducting a study with twenty-three native
Czech participants who were also fluent in English. The participants were
tasked with instructing the Pepper robot on where to place objects on a shelf.
The robot was controlled remotely using the Wizard-of-Oz technique. We
collected data through questionnaires, video recordings, and a post-experiment
feedback session. The results of our experiment show that people perceive an
English-speaking robot as more intelligent than a Czech-speaking robot (z =
18.00, p-value = 0.02). This finding highlights the influence of language on
human-robot interaction. Furthermore, we discuss the feedback obtained from the
participants via the post-experiment sessions and its implications for HRI
design.Comment: ICSR 202
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators
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. Despite the significant progress of 6D pose estimation
methods, their performance is usually limited for heavily occluded objects,
which is a common case in imitation learning where the object is typically
partially occluded by the manipulating hand. Currently, there is a lack of
datasets that would enable the development of robust 6D pose estimation methods
for these conditions. To overcome this problem, we collect a new dataset
(Imitrob) aimed at 6D pose estimation in imitation learning and other
applications where a human holds a tool and performs a task. The dataset
contains image sequences of three different tools and six manipulation tasks
with two camera viewpoints, four human subjects, and left/right hand. Each
image is accompanied by an accurate ground truth measurement of the 6D object
pose, obtained by the HTC Vive motion tracking device. The use of the dataset
is demonstrated by training and evaluating a recent 6D object pose estimation
method (DOPE) in various setups. The dataset and code are publicly available at
http://imitrob.ciirc.cvut.cz/imitrobdataset.php
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators
This letter 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. Despite the significant progress of 6D pose estimation methods, their performance is usually limited for heavily occluded objects, which is a common case in imitation learning, where the object is typically partially occluded by the manipulating hand. Currently, there is a lack of datasets that would enable the development of robust 6D pose estimation methods for these conditions. To overcome this problem, we collect a new dataset (Imitrob) aimed at 6D pose estimation in imitation learning and other applications where a human holds a tool and performs a task. The dataset contains image sequences of nine different tools and twelve manipulation tasks with two camera viewpoints, four human subjects, and left/right hand. Each image is accompanied by an accurate ground truth measurement of the 6D object pose obtained by the HTC Vive motion tracking device. The use of the dataset is demonstrated by training and evaluating a recent 6D object pose estimation method (DOPE) in various setups.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Learning & Autonomous Contro