Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation

Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable, and thus we propose Partially Observable Markov Decision Processes (POMDPs) as a decision-making tool for these applications. One of the issues with POMDPs is that the prediction models are usually handcrafted. In this paper, we use machine learning techniques to build prediction models from observations. A novel technique is employed to discover points of interest (goals) in the environment, and a variant of Growing Hidden Markov Models (GHMMs) is used to learn the transition probabilities of the POMDP. The approach is applied to an autonomous telepresence robot

An extension of GHMMs for environments with occlusions and automatic goal discovery for person trajectory prediction

This work is partially funded by the EC-FP7 under grant agreement no. 611153 (TERESA) and the project PAIS-MultiRobot, funded by the Junta de Andalucía (TIC-7390). I. Perez-Hurtado is also supported by the Postdoctoral Junior Grant 2013 co-funded by the Spanish Ministry of Economy and Competitiveness and the Pablo de Olavide University.Robots navigating in a social way should use some knowledge about common motion patterns of people in the environment. Moreover, it is known that people move intending to reach certain points of interest, and machine learning techniques have been widely used for acquiring this knowledge by observation. Learning algorithms such as Growing Hidden Markov Models (GHMMs) usually assume that points of interest are located at the end of human trajectories, but complete trajectories cannot always be observed by a mobile robot due to occlusions and people going out of sensor range. This paper extends GHMMs to deal with partial observed trajectories where people's goals are not known a priori. A novel technique based on hypothesis testing is also used to discover the points of interest (goals) in the environment. The approach is validated by predicting people's motion in three different datasets.Universidad Pablo de Olavide. Departamento de Deporte e InformáticaPostprin

HuNavSim: A ROS 2 Human Navigation Simulator for Benchmarking Human-Aware Robot Navigation

This work presents the Human Navigation Simulator (HuNavSim), a novel open-source tool for the simulation of different human-agent navigation behaviors in scenarios with mobile robots. The tool, the first programmed under the ROS 2 framework, can be employed along with different well-known robotics simulators like Gazebo. The main goal is to ease the development and evaluation of human-aware robot navigation systems in simulation. Besides a general human-navigation model, HuNavSim includes, as a novelty, a rich set of individual and realistic human navigation behaviors and a complete set of metrics for social navigation benchmarking.Comment: Pre-print version of the paper submitted to the RA-L Journa

Diffuse reflectance spectroscopy for estimation of soil organic matter and texture in burned and abandoned cropland areas

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Mejora del aprendizaje de SQL con realimentación semántica

Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaFALSEsubmitte

Aprendizaje de comportamientos de navegación en planificadores RRT*

[Resumen] Este trabajo presenta un algoritmo para el aprendizaje de comportamientos de navegación a partir de demostraciones usando árboles de exploración aleatoria óptimos (RRT*) como planificador de caminos. El algoritmo de aprendizaje combina las técnicas de Inverse Reinforcement Learning (IRL) y RRT* para aprender los pesos de la función de coste a partir de trayectorias de demostración. Esta función de coste puede ser usada más tarde en el algoritmo RRT* permitiendo al robot reproducir el comportamiento deseado en distintos escenarios. El método ha sido probado primero en simulación y luego usando trayectorias reales de un robot en el laboratorio.https://doi.org/10.17979/spudc.978849749808

Ecological and genetic determinants of Pepino mosaic virus emergence

Virus emergence is a complex phenomenon, which generally involves spread to a new host from a wild host, followed by adaptation to the new host. Although viruses account for the largest fraction of emerging crop pathogens, knowledge about their emergence is incomplete. We address here the question of whether Pepino mosaic virus (PepMV) emergence as a major tomato pathogen worldwide could have involved spread from wild to cultivated plant species and host adaptation. For this, we surveyed natural populations of wild tomatoes in southern Peru for PepMV infection. PepMV incidence, genetic variation, population structure, and accumulation in various hosts were analyzed. PepMV incidence in wild tomatoes was high, and a strain not yet reported in domestic tomato was characterized. This strain had a wide host range within the Solanaceae, multiplying efficiently in most assayed Solanum species and being adapted to wild tomato hosts. Conversely, PepMV isolates from tomato crops showed evidence of adaptation to domestic tomato, possibly traded against adaptation to wild tomatoes. Phylogenetic reconstructions indicated that the most probable ancestral sequence came from a wild Solanum species. A high incidence of PepMV in wild tomato relatives would favor virus spread to crops and its efficient multiplication in different Solanum species, including tomato, allowing its establishment as an epidemic pathogen. Later, adaptation to tomato, traded off against adaptation to other Solanum species, would isolate tomato populations from those in other hosts

HuNavSim: Human Navigation Simulator for Social Navigation Evaluation

[Resumen] En este trabajo se presenta el simulador HuNavSim (”Human Navigation Simulator”), una herramienta de c´odigo libre para la simulación de agentes humanos con comportamientos individualizados. La herramienta, la cual ha sido programada bajo el framework ROS2, puede ser usada junto a simuladores empleados en robótica como Gazebo. El objetivo principal de esta herramienta es facilitar el desarrollo y evaluación en simulación de sistemas de navegación de robots en espacios compartidos con humanos. Para ello, HuNavSim incluye un conjunto realista de comportamientos humanos y un completo conjunto de métricas para la evaluación de la navegación.[Abstract] This work presents the Human Navigation Simulator (HuNavSim), an open-source tool for the simulation of different humanagent navigation behaviors in scenarios with mobile robots. The tool, programmed under the ROS 2 framework, can be employed along with different well-known robotics simulators like Gazebo. The main goal is to ease the development and evaluation of human-aware robot navigation systems in simulation. To do so, besides a general human- navigation model, HuNavSim includes a rich set of individual human navigation behaviors and a complete set of metrics for social navigation benchmarking.Ministerio Ciencia e Innovación; PLEC2021-007868Ministerio Ciencia e Innovación; TED2021-132476B-I0

Barriers to teaching communication skills in Spanish medical schools: a qualitative study with academic leaders.

Background: In recent years, Spanish medical schools (MSs) have incorporated training in communication skills (CS), but how this training is being carried out has not yet been evaluated. Objective: To identify the barriers to the introduction and development of CS teaching in Spanish MSs. Methods: In a previous study, 34 MSs (83% of all MSs in Spain) were invited to participate in a study that explored the factual aspects of teaching CS in these schools. The person responsible for teaching CS at each school was contacted again for this study and asked to respond to a single open-ended question. Two researchers independently conducted a thematic analysis of the responses. Results: We received responses from 30 MSs (85.7% of those contacted and 73% of all MSs in Spain). Five main thematic areas were identified, each with different sub-areas: negative attitudes of teachers and academic leaders; organisation, structure and presence of CS training in the curriculum; negative attitudes of students; a lack of trained teachers; and problems linked to teaching methods and necessary educational logistics. Conclusions: The identified barriers and problems indicate that there are areas for improvement in teaching CS in most Spanish MSs. There seems to be a vicious circle based on the dynamic relationship and interdependence of all these problems that should be faced with different strategies and that requires a significant cultural shift as well as decisive institutional support at the local and national levels. The incorporation of CS training into MS curricula represents a major challenge that must be addressed for students to learn CS more effectively and avoid negative attitudes towards learning CS.post-print853 K