Body randomization reduces the sim-to-real gap for compliant quadruped locomotion

Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of physical damage. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. Here, we investigate the impact of randomizing body parameters during learning of CPG controllers in simulation. The controllers are evaluated on our physical quadruped robot. We find that body randomization in simulation increases chances of finding gaits that function well on the real robot

Control strategies for cleaning robots in domestic applications: A comprehensive review:

Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches

Diseño de una arquitectura robótica para mapear un lenguaje de acción a comandos de movimiento de bajo nivel para manipulación hábil

This paper gives an overview of a robotic architecture meant for skillful manipulation. This design is meant to close the gap between the high level layer (reasoning and planing layer) and the object model system (physical control layer). This architecture proposes an interface layer that allows, in a meaningful way, to connect atomic tasks with controller inputs. In this paper, we discuss how specific complex tasks can be resolved by this system; we analyze the affordance unit design and, we overview the future challenges in the implemenation of the whole system.Este artículo ofrece una visión general de una arquitectura robótica destinada a la manipulación hábil. Este diseño está destinado a cerrar la brecha entre la capa de alto nivel (capa de razonamiento y planificación) y el sistema de modelo de objetos (capa de control físico). Esta arquitectura propone una capa de interfaz que permite, de manera significativa, conectar tareas básicas con el controlador. En este artículo, discutimos cómo este sistema puede resolver tareas complejas específicas; analizamos el diseño de la unidad de accesibilidad y presentamos una visión general de los desafíos futuros en la implementación de todo el sistema.Universidad de Costa Rica/[322-B6-279]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ingeniería::Instituto Investigaciones en Ingeniería (INII)UCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería EléctricaUCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Ingeniería::Maestría Académica en Ingeniería Eléctric

A Helping Hand: Industrial Robotics, Knowledge and User-Oriented Services

In this paper we discuss AI in industrial robotics. In automatic control, computer vision and optimization, ma- chine learning and data mining algorithms are widely used. However, cognition enabling mechanisms, such as high-level logic and symbolic reasoning, are still limited. This is not due to the lack of available algorithms, rather the bottleneck is knowledge representation, acquisition and transformation between different formalisms. In industrial robotics, cognition is not self-serving, AI tech- nologies are rather a tool to make the user interaction, the system configuration and the task execution as cost efficient as possible. Autonomy is a mean to minimize the human workload. In our approach, we use an online knowledge base that provides libraries with object models and task specifications, and offer services to support the user (and the robot) during programming, deployment and execution

Semantic information for robot navigation: a survey

There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situationsThe research leading to these results has received funding from HEROITEA: Heterogeneous 480 Intelligent Multi-Robot Team for Assistance of Elderly People (RTI2018-095599-B-C21), funded by Spanish 481 Ministerio de Economía y Competitividad. The research leading to this work was also supported project "Robots sociales para estimulacón física, cognitiva y afectiva de mayores"; funded by the Spanish State Research Agency under grant 2019/00428/001. It is also funded by WASP-AI Sweden; and by Spanish project Robotic-Based Well-Being Monitoring and Coaching for Elderly People during Daily Life Activities (RTI2018-095599-A-C22)

Overcoming barriers and increasing independence: service robots for elderly and disabled people

This paper discusses the potential for service robots to overcome barriers and increase independence of elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly people and advances in technology which will make new uses possible and provides suggestions for some of these new applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses the complementarity of assistive service robots and personal assistance and considers the types of applications and users for which service robots are and are not suitable