Tootfinder

Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots
Boyuan Deng, Luca Rossini, Jin Wang, Weijie Wang, Nikolaos Tsagarakis
https://arxiv.org/abs/2506.05516

Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots
Adaptive recovery from fall incidents are essential skills for the practical deployment of wheeled-legged robots, which uniquely combine the agility of legs with the speed of wheels for rapid recovery. However, traditional methods relying on preplanned recovery motions, simplified dynamics or sparse rewards often fail to produce robust recovery policies. This paper presents a learning-based framework integrating Episode-based Dynamic Reward Shaping and curriculum learning, which dynamically bal…

Silent Failures in Stateless Systems: Rethinking Anomaly Detection for Serverless Computing
Chanh Nguyen, Erik Elmroth, Monowar Bhuyan
https://arxiv.org/abs/2507.04969

Silent Failures in Stateless Systems: Rethinking Anomaly Detection for Serverless Computing
Serverless computing has redefined cloud application deployment by abstracting infrastructure and enabling on-demand, event-driven execution, thereby enhancing developer agility and scalability. However, maintaining consistent application performance in serverless environments remains a significant challenge. The dynamic and transient nature of serverless functions makes it difficult to distinguish between benign and anomalous behavior, which in turn undermines the effectiveness of traditional …

Autonomous Fabrication of Tailored Defect Structures in 2D Materials using Machine Learning-enabled Scanning Transmission Electron Microscopy
Zijie Wu, Kevin M. Roccapriore, Ayana Ghosh, Kai Xiao, Raymond R. Unocic, Stephen Jesse, Rama Vasudevan, Matthew G. Boebinger
https://arxiv.org/abs/2507.01194

Autonomous Fabrication of Tailored Defect Structures in 2D Materials using Machine Learning-enabled Scanning Transmission Electron Microscopy
Materials with tailored quantum properties can be engineered from atomic scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advanced machine learning and automated beam control. As a proof of concept, we achieved controlled fabr…

Reactive Aerobatic Flight via Reinforcement Learning
Zhichao Han, Xijie Huang, Zhuxiu Xu, Jiarui Zhang, Yuze Wu, Mingyang Wang, Tianyue Wu, Fei Gao
https://arxiv.org/abs/2505.24396

Reactive Aerobatic Flight via Reinforcement Learning
Quadrotors have demonstrated remarkable versatility, yet their full aerobatic potential remains largely untapped due to inherent underactuation and the complexity of aggressive maneuvers. Traditional approaches, separating trajectory optimization and tracking control, suffer from tracking inaccuracies, computational latency, and sensitivity to initial conditions, limiting their effectiveness in dynamic, high-agility scenarios. Inspired by recent breakthroughs in data-driven methods, we propose …