A conserved interaction that is essential for the biogenesis of histone locus bodies.

Nuclear protein, ataxia-telangiectasia locus (NPAT) and FLICE-associated huge protein (FLASH) are two major components of discrete nuclear structures called histone locus bodies (HLBs). NPAT is a key co-activator of histone gene transcription, whereas FLASH through its N-terminal region functions in 3' end processing of histone primary transcripts. The C-terminal region of FLASH contains a highly conserved domain that is also present at the end of Yin Yang 1-associated protein-related protein (YARP) and its Drosophila homologue, Mute, previously shown to localize to HLBs in Drosophila cells. Here, we show that the C-terminal domain of human FLASH and YARP interacts with the C-terminal region of NPAT and that this interaction is essential and sufficient to drive FLASH and YARP to HLBs in HeLa cells. Strikingly, only the last 16 amino acids of NPAT are sufficient for the interaction. We also show that the C-terminal domain of Mute interacts with a short region at the end of the Drosophila NPAT orthologue, multi sex combs (Mxc). Altogether, our data indicate that the conserved C-terminal domain shared by FLASH, YARP, and Mute recognizes the C-terminal sequence of NPAT orthologues, thus acting as a signal targeting proteins to HLBs. Finally, we demonstrate that the C-terminal domain of human FLASH can be directly joined with its N-terminal region through alternative splicing. The resulting 190-amino acid MiniFLASH, despite lacking 90% of full-length FLASH, contains all regions necessary for 3' end processing of histone pre-mRNA in vitro and accumulates in HLBs.

YARP-ROS Inter-Operation in a 2D Navigation Task.

This paper presents some recent developments in YARP middleware, aimed to improve its integration with ROS. They include a new mechanism to read/write ROS transform frames and a new set of standard interfaces to intercommunicate with the ROS navigation stack. A novel set of YARP companion modules, which provide basic navigation functionalities for robots unable to run ROS, is also presented. These modules are optional, independent from each other, and they provide compatible functionalities to well-known packages available inside ROS framework. This paper also discusses how developers can customize their own hybrid YARP-ROS environment in the way it best suits their needs (e.g., the system can be configured to have a YARP application sending navigation commands to a ROS path planner, or vice versa). A number of available possibilities is presented through a set of chosen test cases applied to both real and simulated robots. Finally, example applications discussed in this paper are also made available to the community by providing snippets of code and links to source files hosted on github repository https://github.com/robotology.

Speech Recognition for the iCub Platform.

This paper describes open source software (available at https://github.com/robotology/natural-speech) to build automatic speech recognition (ASR) systems and run them within the YARP platform. The toolkit is designed (i) to allow non-ASR experts to easily create their own ASR system and run it on iCub and (ii) to build deep learning-based models specifically addressing the main challenges an ASR system faces in the context of verbal human-iCub interactions. The toolkit mostly consists of Python, C++ code and shell scripts integrated in YARP. As additional contribution, a second codebase (written in Matlab) is provided for more expert ASR users who want to experiment with bio-inspired and developmental learning-inspired ASR systems. Specifically, we provide code for two distinct kinds of speech recognition: "articulatory" and "unsupervised" speech recognition. The first is largely inspired by influential neurobiological theories of speech perception which assume speech perception to be mediated by brain motor cortex activities. Our articulatory systems have been shown to outperform strong deep learning-based baselines. The second type of recognition systems, the "unsupervised" systems, do not use any supervised information (contrary to most ASR systems, including our articulatory systems). To some extent, they mimic an infant who has to discover the basic speech units of a language by herself. In addition, we provide resources consisting of pre-trained deep learning models for ASR, and a 2.5-h speech dataset of spoken commands, the VoCub dataset, which can be used to adapt an ASR system to the typical acoustic environments in which iCub operates.

Design and Implementation of a YARP Device Driver Interface: The Depth-Sensor Case.

This work illustrates the design phases leading to the development of a new YARP device interface along with its client/server implementation. In order to obtain a smoother integration and a more reliable software usability, while avoiding common errors during the design phases, a new interface is created in the YARP network when a new family of devices is introduced.

iCub-HRI: A Software Framework for Complex Human-Robot Interaction Scenarios on the iCub Humanoid Robot.

Generating complex, human-like behavior in a humanoid robot like the iCub requires the integration of a wide range of open source components and a scalable cognitive architecture. Hence, we present the iCub-HRI library which provides convenience wrappers for components related to perception (object recognition, agent tracking, speech recognition, and touch detection), object manipulation (basic and complex motor actions), and social interaction (speech synthesis and joint attention) exposed as a C++ library with bindings for Java (allowing to use iCub-HRI within Matlab) and Python. In addition to previously integrated components, the library allows for simple extension to new components and rapid prototyping by adapting to changes in interfaces between components. We also provide a set of modules which make use of the library, such as a high-level knowledge acquisition module and an action recognition module. The proposed architecture has been successfully employed for a complex human-robot interaction scenario involving the acquisition of language capabilities, execution of goal-oriented behavior and expression of a verbal narrative of the robot's experience in the world. Accompanying this paper is a tutorial which allows a subset of this interaction to be reproduced. The architecture is aimed at researchers familiarizing themselves with the iCub ecosystem, as well as expert users, and we expect the library to be widely used in the iCub community.