Abstract-- Online sensing plays an important role in advancing modern manufacturing. The real-time sensor signals, which can be stored as high-resolution time series data, contain rich information about the operation status. One of its popular usages is online process monitoring, which can be achieved by effective anomaly detection from the sensor signals. However, most existing approaches either heavily rely on labeled data for training supervised models, or are designed to detect only extreme outliers, thus are ineffective at identifying subtle semantic off-track anomalies to capture where new regimes or unexpected routines start. T o address this challenge, we propose an matrix profile-based unsupervised anomaly detection algorithm that captures fabrication cycle similarity and performs semantic segmentation to precisely identify the onset of defect anomalies in additive manufacturing. The effectiveness of the proposed method is demonstrated by the experiments on real-world sensor data.

Water from Air could produce enough daily water for a family of four.

Figure 1: The Slice-100K dataset consists of STL files and their G-code counterparts. However, we do foresee some potential negative societal impacts. We provide additional visualizations to understand the distribution of STL models in Slice-100K. Slicing: We utilize Prusa's Slicer for generating G-code from STL files. Finetuning implementation: For finetuning our translation model, we use a batch size of 32 with 8 gradient accumulation steps.

We build our dataset from triangu-lated meshes derived from Objaverse-XL and Thingi10K datasets.

The optimal design of robotic actuators is a critical area of research, yet limited attention has been given to optimizing gearbox parameters and automating actuator CAD. This paper introduces COMPAct: Computational Optimization and Automated Modular Design of Planetary Actuators, a framework that systematically identifies optimal gearbox parameters for a given motor across four gearbox types, single-stage planetary gearbox (SSPG), compound planetary gearbox (CPG), Wolfrom planetary gearbox (WPG), and double-stage planetary gearbox (DSPG). The framework minimizes mass and actuator width while maximizing efficiency, and further automates actuator CAD generation to enable direct 3D printing without manual redesign. Using this framework, optimal gearbox designs are explored over a wide range of gear ratios, providing insights into the suitability of different gearbox types across various gear ratio ranges. In addition, the framework is used to generate CAD models of all four gearbox types with varying gear ratios and motors. Two actuator types are fabricated and experimentally evaluated through power efficiency, no-load backlash, and transmission stiffness tests. Experimental results indicate that the SSPG actuator achieves a mechanical efficiency of 60-80 %, a no-load backlash of 0.59 deg, and a transmission stiffness of 242.7 Nm/rad, while the CPG actuator demonstrates 60 % efficiency, 2.6 deg backlash, and a stiffness of 201.6 Nm/rad. Code available at: https://anonymous.4open.science/r/COMPAct-SubNum-3408 Video: https://youtu.be/99zOKgxsDho

Planetary exploration missions require robust locomotion systems capable of operating in extreme environments over extended periods. This paper presents the DISTANT (Distant Transmission and Steering Systems) design, a novel approach for relocating rover traction and steering actuators from wheel-mounted positions to a thermally protected warm box within the rover body. The design addresses critical challenges in long-distance traversal missions by protecting sensitive components from thermal cycling, dust contamination, and mechanical wear. A double wishbone suspension configuration with cardan joints and capstan drive steering has been selected as the optimal architecture following comprehensive trade-off analysis. The system enables independent wheel traction, steering control, and suspension management whilst maintaining all motorisation within the protected environment. The design meets a 50 km traverse requirement without performance degradation, with integrated dust protection mechanisms and thermal management solutions. Testing and validation activities are planned for Q1 2026 following breadboard manufacturing at 1:3 scale.

Biomimetic intelligence and robotics are transforming field ecology by enabling lifelike robotic surrogates that interact naturally with animals under real world conditions. Studying avian behavior in the wild remains challenging due to the need for highly realistic morphology, durable outdoor operation, and intelligent perception that can adapt to uncontrolled environments. We present a next generation bio inspired robotic platform that replicates the morphology and visual appearance of the female Houbara bustard to support controlled ethological studies and conservation oriented field research. The system introduces a fully digitally replicable fabrication workflow that combines high resolution structured light 3D scanning, parametric CAD modelling, articulated 3D printing, and photorealistic UV textured vinyl finishing to achieve anatomically accurate and durable robotic surrogates. A six wheeled rocker bogie chassis ensures stable mobility on sand and irregular terrain, while an embedded NVIDIA Jetson module enables real time RGB and thermal perception, lightweight YOLO based detection, and an autonomous visual servoing loop that aligns the robot's head toward detected targets without human intervention. A lightweight thermal visible fusion module enhances perception in low light conditions. Field trials in desert aviaries demonstrated reliable real time operation at 15 to 22 FPS with latency under 100 ms and confirmed that the platform elicits natural recognition and interactive responses from live Houbara bustards under harsh outdoor conditions. This integrated framework advances biomimetic field robotics by uniting reproducible digital fabrication, embodied visual intelligence, and ecological validation, providing a transferable blueprint for animal robot interaction research, conservation robotics, and public engagement.

Abstract-- Modern automated factories increasingly run manufacturing procedures using a matrix of programmable machines, such as 3D printers, interconnected by a programmable transport system, such as a fleet of tabletop robots. T o embed a manufacturing procedure into a smart factory, an operator must: (a) assign each of its processes to a machine and (b) specify how agents should transport parts between machines. The problem of embedding a manufacturing process into a smart factory is termed the Smart Factory Embedding (SFE) problem. State-of-the-art SFE solvers can only scale to factories containing a couple dozen machines. Modern smart factories, however, may contain hundreds of machines. We fill this hole by introducing the first highly scalable solution to the SFE, TS-ACES, the Traffic System based Anytime Cyclic Embedding Solver . We show that TS-ACES is complete and can scale to SFE instances based on real industrial scenarios with more than a hundred machines. I. INTRODUCTION Flexible manufacturing is a key objective of the modern manufacturing industry [1]. A smart factory is flexible if it can be easily reconfigured to produce different products.

Personal use of this material is permitted. Abstract--The rapid increase in the development of humanoid robots and customized manufacturing solutions has brought dexterous manipulation to the forefront of modern robotics. Over the past decade, several expensive dexterous hands have come to market, but advances in hardware design, particularly in servo motors and 3D printing, have recently facilitated an explosion of cheaper open-source hands. Most hands are anthropomorphic to allow use of standard human tools, and attempts to increase dexterity often sacrifice anthropomorphism. We introduce the open-source ISyHand (pronounced easy-hand), a highly dexterous, low-cost, easy-to-manufacture, on-joint servo-driven robot hand. Our hand uses off-the-shelf Dynamixel motors, fasteners, and 3D-printed parts, can be assembled within four hours, and has a total material cost of about 1,300 USD. T o demonstrate the utility of the articulated palm, we use reinforcement learning in simulation to train the hand to perform a classical in-hand manipulation task: cube reorientation. Our novel, systematic experiments show that the simulated ISyHand outperforms the two most comparable hands in early training phases, that all three perform similarly well after policy convergence, and that the ISyHand significantly outperforms a fixed-palm version of its own design. Additionally, we deploy a policy trained on cube reorientation on the real hand, demonstrating its ability to perform real-world dexterous manipulation. The dexterity, strength, robustness, and tactile sensing of the human hand are crucial to the human ability to perceive, manipulate, and use objects.