Development trends in Robotics

Robots are always a frequently used as an example for Mechatronic Systems. Currently the field of robotics is very fast growing. Therefore the overview is definitely not actual and have to be improved. The main goal of this contribution is to make some additional remarks to the existing kinds of robots and introduce some new with special emphasis to Mechatronic Systems like manufacturing automation by Production 4.0

Current trends in robotics in education and computational thinking

Computational thinking-related issues have had a specific track on TEEM Conference since 2016. This is the sixth edition of this track within the 2021 TEEM Conference edition. This year the papers are centered on programming and robotics, but the artificial intelligence topics increase their presence in the track.info:eu-repo/semantics/publishedVersio

A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks

Reward engineering is an important aspect of reinforcement learning. Whether or not the user's intentions can be correctly encapsulated in the reward function can significantly impact the learning outcome. Current methods rely on manually crafted reward functions that often require parameter tuning to obtain the desired behavior. This operation can be expensive when exploration requires systems to interact with the physical world. In this paper, we explore the use of temporal logic (TL) to specify tasks in reinforcement learning. TL formula can be translated to a real-valued function that measures its level of satisfaction against a trajectory. We take advantage of this function and propose temporal logic policy search (TLPS), a model-free learning technique that finds a policy that satisfies the TL specification. A set of simulated experiments are conducted to evaluate the proposed approach

Reinforcement Learning With Temporal Logic Rewards

Reinforcement learning (RL) depends critically on the choice of reward functions used to capture the de- sired behavior and constraints of a robot. Usually, these are handcrafted by a expert designer and represent heuristics for relatively simple tasks. Real world applications typically involve more complex tasks with rich temporal and logical structure. In this paper we take advantage of the expressive power of temporal logic (TL) to specify complex rules the robot should follow, and incorporate domain knowledge into learning. We propose Truncated Linear Temporal Logic (TLTL) as specifications language, that is arguably well suited for the robotics applications, together with quantitative semantics, i.e., robustness degree. We propose a RL approach to learn tasks expressed as TLTL formulae that uses their associated robustness degree as reward functions, instead of the manually crafted heuristics trying to capture the same specifications. We show in simulated trials that learning is faster and policies obtained using the proposed approach outperform the ones learned using heuristic rewards in terms of the robustness degree, i.e., how well the tasks are satisfied. Furthermore, we demonstrate the proposed RL approach in a toast-placing task learned by a Baxter robot

Les nouveaux pays industrialisés : Stratégies de développement industriel – le cas de la Corée du Sud et du Brésil

In the introductory remarks of this article the authors examine the birth of the newly industrialized countries and the emergence of a new international division of labor. After stressing the two modes of the industrial strategy followed by these countries, the authors look at two newly industrialized countries (Brazil and South Korea). These specific countries due to the interplay of both, objective factors (natural resources, location, manpower...) and policy choices have followed divergent development strategies. The authors conclude that it is not so much the classical policy dilemma import substitution vs expert promotion that will determine the future of these semi-industrialized countries, than their ability to master the technological know-how that sustains their industrial development. The new technological trends in robotics and telematics constitute powerful factors of relocation which may threaten the long run growth prospects of the semi-industrialized countries