Educational features of Malaysian robot contest

The educational experiences from robot contest of entry, junior and advance level are presented based on guided constructionism approach in education that combines hands on guidance with hands-on experience. The aim of the competition as a whole are to allow the student to (i) conceptualise the robot (ii) manage the non-deterministic characteristic of the environment and (iii)manage integrated hardware and software development projects. Indeed with this knowledge the student should be able to win a number of international robot tournaments

The RoCKIn Project

The goal of the project “Robot Competitions Kick Innovation in Cognitive Systems and Robotics” (RoCKIn), funded by the European Commission under its 7th Framework Program, has been to speed up the progress toward smarter robots through scientific competitions. Two challenges have been selected for the competitions due to their high relevance and impact on Europe’s societal and industrial needs: domestic service robots (RoCKIn@Home) and innovative robot applications in industry (RoCKIn@Work). The RoCKIn project has taken an approach to boosting scientific robot competitions in Europe by (i) specifying and designing open domain test beds for competitions targeting the two challenges; (ii) developing methods for scoring and benchmarking that allow to assess both particular subsystems as well as the integrated system; and (iii) organizing camps to build up a community of new teams, interested to participate in robot competitions. A significant number of dissemination activities on the relevance of robot competitions were carried out to promote research and education in robotics, as to researchers and lay citizens. The lessons learned during RoCKIn paved the way for a step forward in the organization and research impact of robot competitions, contributing for Europe to become a world leader in robotics research, education, and technology transfer

DIP-RL: Demonstration-Inferred Preference Learning in Minecraft

In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal. However, in many unstructured real-world settings, such a reward signal is unknown and humans cannot reliably craft a reward signal that correctly captures desired behavior. To solve tasks in such unstructured and open-ended environments, we present Demonstration-Inferred Preference Reinforcement Learning (DIP-RL), an algorithm that leverages human demonstrations in three distinct ways, including training an autoencoder, seeding reinforcement learning (RL) training batches with demonstration data, and inferring preferences over behaviors to learn a reward function to guide RL. We evaluate DIP-RL in a tree-chopping task in Minecraft. Results suggest that the method can guide an RL agent to learn a reward function that reflects human preferences and that DIP-RL performs competitively relative to baselines. DIP-RL is inspired by our previous work on combining demonstrations and pairwise preferences in Minecraft, which was awarded a research prize at the 2022 NeurIPS MineRL BASALT competition, Learning from Human Feedback in Minecraft. Example trajectory rollouts of DIP-RL and baselines are located at https://sites.google.com/view/dip-rl.Comment: Paper accepted at The Many Facets of Preference Learning Workshop at the International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA, 202

The principles of Educational Robotic Applications (ERA): a framework for understanding and developing educational robots and their activities

The original educational robots were the Logo Turtles. They derived their rationale from constructionism. How has this changed? This paper postulates ten principles that underpin the effective utilisation of robotic devices within education settings. We argue that they form a framework still sympathetic to constructionism that can guide the development, application and evaluation of educational robots. They articulate a summary of the existing knowledge as well as suggesting further avenues of research that may be shared by educationists and designers. The principles also provide an evaluative framework for Educational Robotic Applications (ERA). This paper is an overview of the ideas, which we will develop in future papers

Multi-Agent Task Allocation for Robot Soccer

This is the published version. Copyright De GruyterThis paper models and analyzes task allocation methodologies for multiagent systems. The evaluation process was implemented as a collection of simulated soccer matches. A soccer-simulation software package was used as the test-bed as it provided the necessary features for implementing and testing the methodologies. The methodologies were tested through competitions with a number of available soccer strategies. Soccer game scores, communication, robustness, fault-tolerance, and replanning capabilities were the parameters used as the evaluation criteria for the mul1i-agent systems