Tootfinder

Closed Form Time Derivatives of the Equations of Motion of Rigid Body Systems
Andreas Mueller, Shivesh Kumar
https://arxiv.org/abs/2507.11076 https://

Closed Form Time Derivatives of the Equations of Motion of Rigid Body Systems
Derivatives of equations of motion(EOM) describing the dynamics of rigid body systems are becoming increasingly relevant for the robotics community and find many applications in design and control of robotic systems. Controlling robots, and multibody systems comprising elastic components in particular, not only requires smooth trajectories but also the time derivatives of the control forces/torques, hence of the EOM. This paper presents the time derivatives of the EOM in closed form up to secon…

Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
Shuangning Li, Chonghuan Wang, Jingyan Wang
https://arxiv.org/abs/2507.11891

Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has shown that the standard difference-in-means estimator is biased in estimating the global treatment effect (GTE) due to a particular form of interference between experimental units. Specifically, units under the treatment and control algorithms contribute to a shared pool of data that subsequently train both algorithms, resulting in interference between the two groups. The bias …

Function-to-Style Guidance of LLMs for Code Translation
Longhui Zhang, Bin Wang, Jiahao Wang, Xiaofeng Zhao, Min Zhang, Hao Yang, Meishan Zhang, Yu Li, Jing Li, Jun Yu, Min Zhang
https://arxiv.org/abs/2507.11083

Function-to-Style Guidance of LLMs for Code Translation
Large language models (LLMs) have made significant strides in code translation tasks. However, ensuring both the correctness and readability of translated code remains a challenge, limiting their effective adoption in real-world software development. In this work, we propose F2STrans, a function-to-style guiding paradigm designed to progressively improve the performance of LLMs in code translation. Our approach comprises two key stages: (1) Functional learning, which optimizes translation corre…

Bandwidth enhanced current noise suppression with a SERF magnetometer
Tobias Menold, Arianna Bertoluzza, Patrick Hildebrand, Ann-Kathrin Gottschalk, Daniel Braun, J\'ozsef Fort\'agh, Andreas G\"unther
https://arxiv.org/abs/2506.11923

Bandwidth enhanced current noise suppression with a SERF magnetometer
This work investigates the behavior of a spin-exchange relaxation-free (SERF) magnetometer integrated into the feedback branch of a closed-loop control circuit, designed to actively suppress noise from a current source. In this configuration, the magnetometer bandwidth is enhanced by almost two orders of magnitude as compared to the open-loop setup. Incorporating an injection transformer, the proposed system effectively reduces current noise to below 3 nA/sqrt(Hz) over a broad frequency bandwid…

Our pre-conference Barcamp has become a real highlight, offering a fantastic opportunity to connect with fellow attendees and dive into some engaging conversations before the main conference even kicks off. This year, Nick Burch will once again be guiding the flow of ideas and discussions on 15 June, starting at 2:30 pm. It's a great way to warm up, share your thoughts, and meet new people!
Learn more about our barcamp:

Isolating Noisy Labelled Test Cases in Human-in-the-Loop Oracle Learning
Charaka Geethal Kapugama
https://arxiv.org/abs/2506.13273 https://

Isolating Noisy Labelled Test Cases in Human-in-the-Loop Oracle Learning
Incorrectly labelled test cases can adversely affect the training process of human-in-the-loop oracle learning tech-niques. This paper introduces ISONOISE, a technique designed to identify such mislabelled test cases introduced during human-in-the-loop oracle learning. This technique can be applied to programs taking numeric inputs. Given a compromised automatic test oracle and its training test suite, ISONOISE first isolates thetest cases suspected of being mislabelled. This task is performed …

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Arnab Mukherjee, Raju Halder, Joydeep Chandra
https://arxiv.org/abs/2507.11430

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Federated Learning (FL) has undergone significant development since its inception in 2016, advancing from basic algorithms to complex methodologies tailored to address diverse challenges and use cases. However, research and benchmarking of novel FL techniques against a plethora of established state-of-the-art solutions remain challenging. To streamline this process, we introduce FLsim, a comprehensive FL simulation framework designed to meet the diverse requirements of FL workflows in the liter…

CIRO7.2: A Material Network with Circularity of -7.2 and Reinforcement-Learning-Controlled Robotic Disassembler
Federico Zocco, Monica Malvezzi
https://arxiv.org/abs/2506.11748

CIRO7.2: A Material Network with Circularity of -7.2 and Reinforcement-Learning-Controlled Robotic Disassembler
The competition over natural reserves of minerals is expected to increase in part because of the linear-economy paradigm based on take-make-dispose. Simultaneously, the linear economy considers end-of-use products as waste rather than as a resource, which results in large volumes of waste whose management remains an unsolved problem. Since a transition to a circular economy can mitigate these open issues, in this paper we begin by enhancing the notion of circularity based on compartmental dynam…

Tactical Decision for Multi-UGV Confrontation with a Vision-Language Model-Based Commander
Li Wang, Qizhen Wu, Lei Chen
https://arxiv.org/abs/2507.11079 ht…

Tactical Decision for Multi-UGV Confrontation with a Vision-Language Model-Based Commander
In multiple unmanned ground vehicle confrontations, autonomously evolving multi-agent tactical decisions from situational awareness remain a significant challenge. Traditional handcraft rule-based methods become vulnerable in the complicated and transient battlefield environment, and current reinforcement learning methods mainly focus on action manipulation instead of strategic decisions due to lack of interpretability. Here, we propose a vision-language model-based commander to address the iss…

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
Jihao Li, Keqi Zhu, Guodong Lu, I-Ming Chen, Huixu Dong
https://arxiv.org/abs/2506.11570

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
We present a novel under-actuated gripper with two 3-joint fingers, which realizes force feedback control by the deep learning technique- Long Short-Term Memory (LSTM) model, without any force sensor. First, a five-linkage mechanism stacked by double four-linkages is designed as a finger to automatically achieve the transformation between parallel and enveloping grasping modes. This enables the creation of a low-cost under-actuated gripper comprising a single actuator and two 3-phalange fingers…