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2008 ASEE Annual Conference and Exposition

作者： Ciaraldi, Michael Cobb, Eben Cyganski, David Gennert, Michael Demetroiu, Michael Looft, Fred Michalson, William Miller, Bradley Rong, Yiming Schachterle, Lance Stafford, Kenneth Tryggvason, Gretar Van De Ven, James Worcester Polytechnic Institute Worcester Polytechnic Institute 100 Institute Rd. Worcester MA 01609 United States Department of Computer Science Department of Mechanical Engineering Department of Electrical and Computer Engineering Robotics Engineering Program Robotics Resource Center Department of Mechanical Engineering Department of English

In the spring of 2007, Worcester Polytechnic Institute introduced a BS degree program in robotics engineering. The degree program is a collaborative effort, involving faculty from the departments of Computer Science, Electrical and Computer engineering and Mechanical engineering. The motivation for establishing the program is two-fold: needs of the rapidly growing robotics industry and student interest as demonstrated by numerous high school robotics competitions. The program relies in part on already existing courses, but the core curriculum consists of five new "unified robotics engineering" courses. Although robotics engineering is not recognized as a distinct engineering field by ABET, the program is designed to be accreditable under the "General engineering" ABET criteria. While the program is less than a year old, there is already significant student interest. © American Society for engineering Education, 2008.

关键词： engineering education

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Innovative thinking scale of electrical engineering students in MOOC learning at MBKM activities

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International Journal of Continuing engineering Education and Life-Long Learning 2025年第3-4期35卷 351-363页

作者： Fauzan, Jahril Nur Pawinanto, Roer Eka Mulyanti, Budi Study Program of Electrical Engineering Education Universitas Pendidikan Indonesia Bandung40154 Indonesia Study Program of Industrial Automation and Robotics Engineering Education Universitas Pendidikan Indonesia Bandung40154 Indonesia Universitas Pendidikan Indonesia Bandung40154 Indonesia

The objectives of this research endeavour encompass three main aspects: 1) the creation of a novel measurement tool, referred to as the innovative thinking scale (ITS), which aims to evaluate variations in innovative thinking among individuals;2) the evaluation of the effectiveness and reliability of the innovative thinking scale;3) the investigation of the associations between different indicators of the innovative thinking scale. Using a quantitative research design, a two-stage study is undertaken to ascertain the validity and reliability of the ITS instrument. Content validity and construct validity are established through an intensive procedure involving a panel of experts in the field of telecommunication engineering and the application of Pearson correlation analysis. The sample for this study consisted of 43 students pursuing a degree in electrical engineering, with a specific focus on telecommunication engineering. These students are selected based on their participation in the freedom of learning independent campus. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： engineering education

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Reward Generation via Large Vision-Language Model in Offline Reinforcement Learning

Reward Generation via Large Vision-Language Model in Offline...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Younghwan Luu, Tung M. Lee, Donghoon Yoo, Chang D. Electrical Engineering KAIST Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of

ISBN: (纸本)9798350368741

In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risky. However, designing dense reward signals for offline dataset requires significant human effort and domain expertise. Reinforcement learning with human feedback (RLHF) has emerged as an alternative, but it remains costly due to the human-in-the-loop process, prompting interest in automated reward generation models. To address this, we propose Reward Generation via Large Vision-Language Models (RG-VLM), which leverages the reasoning capabilities of LVLMs to generate rewards from offline data without human involvement. RG-VLM improves generalization in long-horizon tasks and can be seamlessly integrated with the sparse reward signals to enhance task performance, demonstrating its potential as an auxiliary reward signal. © 2025 IEEE.

关键词： Large Vision-Language Model Offline Reinforcement Learning Reward Labeling

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Sample Efficient Reinforcement Learning via Large Vision Language Model Distillation

Sample Efficient Reinforcement Learning via Large Vision Lan...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Donghoon Luu, Tung M. Lee, Younghwan Yoo, Chang D. Robotics Program KAIST Daejeon Korea Republic of Electrical Engineering KAIST Daejeon Korea Republic of

ISBN: (纸本)9798350368741

Recent research highlights the potential of multimodal foundation models in tackling complex decision-making challenges. However, their large parameters make real-world deployment resource-intensive and often impractical for constrained systems. Reinforcement learning (RL) shows promise for task-specific agents but suffers from high sample complexity, limiting practical applications. To address these challenges, we introduce LVLM to Policy (LVLM2P), a novel framework that distills knowledge from large vision-language models (LVLM) into more efficient RL agents. Our approach leverages the LVLM as a teacher, providing instructional actions based on trajectories collected by the RL agent, which helps reduce less meaningful exploration in the early stages of learning, thereby significantly accelerating the agent's learning progress. Additionally, by leveraging the LVLM to suggest actions directly from visual observations, we eliminate the need for manual textual descriptors of the environment, enhancing applicability across diverse tasks. Experiments show that LVLM2P significantly enhances the sample efficiency of baseline RL algorithms. The code is available at https://***/i22024/LVLM2P. © 2025 IEEE.

关键词： Knowledge Distillation Reinforcement Learning Vision Language Model

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Micro-electrode array (MEA) electrophysiological signal analysis by descriptive stochastic tests of the probability density structure

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Research on Biomedical engineering 2025年第2期41卷 1-13页

作者： Faria, Vinícius Naves Rezende Ramos, Marina Abadia Martinoia, Sérgio Destro-Filho, João-Batista Postgraduate Program in Electrical Engineering Universidade Federal de MG Uberlândia Brazil Postgraduate Program in Biomedical Sciences Universidade Federal de MG Uberlândia Brazil Division of Informatics Bioengineering Robotics and System Engineering Università Degli Studi Di Genova Genova Italy

Purpose: The primary objective of this study is to present, analyze, and quantitatively characterize the electrical signals obtained from neuronal cultures coupled to micro-electrode arrays (MEAs). Special attention is given to the temporal evolution of electrical activity across distinct geometric regions of the MEA devices. Methods: The proposed methodology involves signal acquisition, classical analysis based on spike and burst detection, and, critically, the characterization of the probability density structure of the signals. This is achieved through the quantification of Gaussianity—expressed as the percentage of segments non-Gaussian (PSNG)—and stationarity— expressed as the percentage of segments non-stationary (PSNE). Results: Initial analyses showed that first-order power spectral density quantifiers lacked the statistical robustness required to effectively describe the complex components of MEA signals. In contrast, PSNG and PSNE demonstrated strong potential in capturing the different stages of neuronal culture development. Cluster analysis further supported the discriminative capacity of these quantifiers. Conclusion: The phase corresponding to decreased neuronal activity and onset of cell death—a biologically critical stage—exhibited the strongest correlation among the studied variables. These results suggest a discernible biological organization and highlight the relevance of PSNG and PSNE as promising tools for assessing neuronal culture dynamics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Cell death

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Wake Homing Torpedo Guidance Using a Hierarchical Deep Reinforcement Learning Framework

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IEEE Access 2025年 13卷 72938-72952页

作者： Hwang, Kunchul Kim, Jinwhan KAIST Robotics Program Daejeon34141 Korea Republic of Agency for Defense Development Gyeongsangnam-do Changwon51667 Korea Republic of KAIST Department of Mechanical Engineering Daejeon34141 Korea Republic of

This paper proposes a novel Hierarchical Deep Reinforcement Learning (HRL) framework for wake homing torpedo guidance, applying the Discrete Event System Specification (DEVS) formalism to design high-level policies and reward shaping functions. Wake homing torpedo guidance generates course commands to enable the torpedo to follow the wake trajectory of a target ship. When the target ship evades the incoming torpedo, the wake trajectory becomes curved, often causing the torpedo to lose track due to the narrow detection range of the wake detection sensor. This necessitates a sophisticated algorithm to consistently track the target ship, particularly in scenarios where the torpedo exits and re-enters the wake trajectory in noisy environments. While heuristic algorithms can handle typical wake trajectories, developing a robust solution for unknown trajectories remains a significant challenge. To address this, we apply a novel reinforcement learning approach to develop the guidance logic and compare its performance with a conventional algorithm-based method. The performance and effectiveness of the proposed approach are demonstrated through numerical simulations. © 2013 IEEE.

关键词： Deep reinforcement learning

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Improvement of Localization Algorithm for Indoor and Outdoor Navigation of Autonomous Robots

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Journal of Institute of Control, robotics and Systems 2025年第5期31卷 502-509页

作者： Cho, Joonhyuk Kim, Bongsang Moon, Heechang Industrial Convergence Interdepartmental Program Autonomous Vehicle Intelligent Robotics The Graduate School of Hongik University Korea Republic of Mechanical & System Design Engineering Hongik University Korea Republic of

Location estimation for waypoint-based autonomous driving robots primarily relies on SLAM (Simultaneous Localization and Mapping) and GNSS (Global Navigation Satellite System) technologies. GNSS performs well in open areas but struggles with signal issues, while SLAM offers high accuracy in feature-rich environments but faces challenges in open spaces. This study integrates SLAM and GNSS, enabling seamless switching between systems when one encounters difficulties. The combined system ensures consistency by aligning coordinate systems and directional data. Experiments in indoor, forest, and open environments demonstrate that the switching algorithm enables stable autonomous driving in diverse conditions, effectively leveraging the strengths of both systems. © ICROS 2025.

关键词： Global positioning system

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Reward Generation via Large Vision-Language Model in Offline Reinforcement Learning

Reward Generation via Large Vision-Language Model in Offline...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Younghwan Lee Tung M. Luu Donghoon Lee Chang D. Yoo Electrical Engineering KAIST Daejeon South Korea Robotics Program KAIST Daejeon South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Reinforcement learning Signal processing Real-time systems Human in the loop Data models Cognition Acoustics Labeling Speech processing

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Sample Efficient Reinforcement Learning via Large Vision Language Model Distillation

Sample Efficient Reinforcement Learning via Large Vision Lan...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Donghoon Lee Tung M. Luu Younghwan Lee Chang D. Yoo Robotics Program KAIST Daejeon South Korea Electrical Engineering KAIST Daejeon South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent research highlights the potential of multi-modal foundation models in tackling complex decision-making challenges. However, their large parameters make real-world deployment resource-intensive and often impractical for constrained systems. Reinforcement learning (RL) shows promise for task-specific agents but suffers from high sample complexity, limiting practical applications. To address these challenges, we introduce LVLM to Policy (LVLM2P), a novel framework that distills knowledge from large vision-language models (LVLM) into more efficient RL agents. Our approach leverages the LVLM as a teacher, providing instructional actions based on trajectories collected by the RL agent, which helps reduce less meaningful exploration in the early stages of learning, thereby significantly accelerating the agent’s learning progress. Additionally, by leveraging the LVLM to suggest actions directly from visual observations, we eliminate the need for manual textual descriptors of the environment, enhancing applicability across diverse tasks. Experiments show that LVLM2P significantly enhances the sample efficiency of baseline RL algorithms. The code is available at https://***/i22024/LVLM2P

关键词： Visualization Limiting Foundation models Decision making Signal processing algorithms Reinforcement learning Cognition Trajectory Complexity theory Speech processing

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A Novel Hybrid Ureteroscope Tracking for Robotic-Assisted Retrograde Intrarenal Surgery via Recognition of Pathway With Lumen Identification

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IEEE robotics and Automation Letters 2025年第6期10卷 6368-6375页

作者： Han, Jung-Min Kwon, Dong-Soo Kyung, Ki-Uk Korea Advanced Institute of Science and Technology Department of the Robotics Program Daejeon34141 Korea Republic of Roen Surgical Inc. Daejeon34051 Korea Republic of Korea Advanced Institute of Science and Technology Department of Mechanical Engineering Daejeon34141 Korea Republic of

Resolving disorientation of the surgeon caused by wrong recognition of scope's position, which often increases procedural time and workload, remains a significant challenge in robotic-assisted retrograde intrarenal surgery (RIRS). This letter introduces a novel hybrid ureteroscope tracking algorithm that integrates low-latency lumen identification with robotic motion data to enhance intrarenal navigation. The system estimates the ureteroscope's position on the centerline of the kidney by recognizing its pathway. In validation tests using a 3D-printed phantom, the proposed method achieved an average localization success rate of 89.2% for major calyx entry and 84.1% for minor calyx entry, with an average computation time of 0.26 seconds, ensuring low-latency operation. Usability testing with ten novice participants demonstrated a 44.5% reduction in cognitive workload (NASA-TLX), improved task success rates, and reduced manipulation effort. These results indicate that the proposed tracking algorithm significantly enhances ureteroscope navigation, improving efficiency and reducing the surgeon's cognitive load in robotic-assisted RIRS. © 2016 IEEE.

关键词： Medical robotics

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