Hierarchies of Planning and Reinforcement Learning for Robot Navigation

Solving robotic navigation tasks via reinforcement learning (RL) is challenging due to their sparse reward and long decision horizon nature. However, in many navigation tasks, high-level (HL) task representations, like a rough floor plan, are available. Previous work has demonstrated efficient learning by hierarchal approaches consisting of path planning in the HL representation and using sub-goals derived from the plan to guide the RL policy in the source task. However, these approaches usually neglect the complex dynamics and sub-optimal sub-goal-reaching capabilities of the robot during planning. This work overcomes these limitations by proposing a novel hierarchical framework that utilizes a trainable planning policy for the HL representation. Thereby robot capabilities and environment conditions can be learned utilizing collected rollout data. We specifically introduce a planning policy based on value iteration with a learned transition model (VI-RL). In simulated robotic navigation tasks, VI-RL results in consistent strong improvement over vanilla RL, is on par with vanilla hierarchal RL on single layouts but more broadly applicable to multiple layouts, and is on par with trainable HL path planning baselines except for a parking task with difficult non-holonomic dynamics where it shows marked improvements.Comment: 7 pages, 5 figures, 2021 IEEE International Conference on Robotics and Automation (ICRA), v2: DOI number adde

Estado del arte en robótica cooperativa aplicada al rescate de víctimas

The present article, in the context of a documentary research carried out and interpreted to be taken as a baseline in research for the ROMA Autonomous Mobile Robotics Group of the Francisco José de Caldas District University, describes the state of art of the applied RC to the rescue of victims. The revision is established chronologically in the last fifteen years; in Latin America; and focused mainly in Colombia. They are used as sources: The Google Schoolar database, and articles from the electronic engineering journals indexed for the year 2017 in COLCIENCIAS. As a product thrown, a particular model of communication technology used in CR is presented in the Colombian context.El presente artículo, en el contexto de una investigación documental realizada e interpretada para que fuera tomada como línea de base en investigaciones para el grupo de Robótica Móvil Autónoma ROMA de la Universidad Distrital Francisco José de Caldas, describe el estado de arte de la RC aplicada al rescate de víctimas. Se establece cronológicamente la revisión en los últimos quince años; en Latinoamérica; y enfocada principalmente en Colombia. Se utilizan como fuentes: la base de datos Google Schoolar, y artículos de las revistas de Ingeniería electrónica indexadas para el año 2017 en COLCIENCIAS. Como producto arrojado se presenta un modelo particular de la tecnología de comunicación empleada en RC en el contexto colombiano

Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection

In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation