1,721,025 research outputs found
Learning Newtonian Physics through Programming Robot Experiments
No full textNovel technology has been applied to improve students’ learning abilities in different disciplines. The research in this field is still finding suitable methodologies, tools, and evaluation mechanisms to devise learning frameworks with high impact on students’ performance. This article describes an instructional method to perform Newtonian physics experiments by programming a mobile robot. Such experiments allow the learners to design, implement and visualize physics concepts, thus using the robot as a cognitive tool or mindtool. An accurate assessment of the students learning gain, involving 29 high-school students, shows that the proposed method provided significant improvements in the students understanding of the first Newton’s law, the second Newton law and the superposition principle. The learning gain has been measured through the Force Concept Inventory questionnaire. From this study, we can state that programming a mobile robot to perform physics experiments can improve knowledge about Newtonian physics, even without giving specific lectures in the subject and with a much shorter lecture plan with respect to traditional lectures
RLupus: cooperation through emergent communication in the werewolf social deduction game
Full text linkThis paper focuses on the emergence of communication to support cooperation in environments modeled as social deduction games (SDG), that are games where players communicate freely to deduce each others' hidden intentions. We first state the problem by giving a general formalization of SDG and a possible solution framework based on reinforcement learning. Next, we focus on a specific SDG, known as The Werewolf, and study if and how various forms of communication influence the outcome of the game. Experimental results show that introducing a communication signal greatly increases the winning chances of a class of players. We also study the effect of the signal's length and range on the overall performance showing a non-linear relationship
Editorial to the 'pattern recognition and artificial intelligence for human behaviour analysis' special section
No full textHRI Users' Studies in the Context of the SciRoc Challenge: Some Insights on Gender-Based Differences
Full text linkIn this paper, we present the outcomes of the first user study designed and evaluated in the context of the Smart City Robotics Challenge (SciRoc Challenge). The study presented in this paper has the main novelty of having been devised and implemented in a realistic environment: a robot competition where robot tasks were developed by participant teams, robots were fully autonomous, and user questionnaires were part of the competition score. Specifically, our study was performed over a scenario configured to instruct a robot to take an elevator of a shopping mall asking for customers support. Leveraging the dedicated questionnaire designed for the tested scenario, we validated the experimental hypothesis if user perception of robots' behaviour may be influenced by the user's gender. In the end, we discuss the results of our study
Image Classification With Small Datasets: Overview and Benchmark
Full text linkImage classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier
Bridging Robotics Education between High School and University: RoboCup@Home Education
No full textComparing to the learning contents in high school level robotics education leads us to discover a gap of missing skill sets required in university level robotics development. Our objective in this work is to bridge this gap by outreaching our development in RoboCup@Home Education. We have conducted our studied in several RoboCup events to establish new local communities. We have run hands-on robot building workshop, and finally gauge the project outcome by organizing the Education Challenge
A reinforcement learning environment for multi-service UAV-enabled wireless systems
No full textWe design a multi-purpose environment for autonomous UAVs offering different communication services in a variety of application contexts (e.g., wireless mobile connectivity services, edge computing, data gathering). We develop the environment, based on OpenAI Gym framework, in order to simulate different characteristics of real operational environments and we adopt the Reinforcement Learning to generate policies that maximize some desired performance. The quality of the resulting policies are compared with a simple baseline to evaluate the system and derive guidelines to adopt this technique in different use cases. The main contribution of this paper is a flexible and extensible OpenAI Gym environment, which allows to generate, evaluate, and compare policies for autonomous multi-drone systems in multi-service applications. This environment allows for comparative evaluation and benchmarking of different approaches in a variety of application contexts
Do Not Make the Same Mistakes Again and Again: Learning Local Recovery Policies for Navigation from Human Demonstrations
No full textIn this letter, we present a human-in-the-loop learning framework for mobile robots to generate effective local policies in order to recover from navigation failures in long-term autonomy. We present an analysis of failure and recovery cases derived from long-term autonomous operation of a mobile robot, and propose a two-layer learning framework that allows to detect and recover from such navigation failures. Employing a learning by demonstration approach, our framework can incrementally learn to autonomously recover from situations it initially needs humans to help with. The learning framework allows for both real-time failure detection and regression using Gaussian processes. Our empirical results on two different failure scenarios indicate that given 40 failure state observations, the true positive rate of the failure detection model exceeds 90%, ending with successful recovery actions in more than 90% of all detected cases
RoboCup@Home Education: A New Format for Educational Competitions
Full text linkIn this paper we describe a novel methodology for holding educational robot competitions that has been developed within the RoboCup@Home Educational initiative. The methodology is based on two main elements: (1) a workshop + competition format where teaching material is interwoven with competitive tasks; (2) a team-centered approach, where rules and teaching material are tailored to participant teams to maximize the educational experience for students. The results and lessons learned after more than five years of organizing these competitions are also discussed in this paper
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