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cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management Efficient closed-form approaches for pose estimation using Sylvester forms World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning CooperDrive: Enhancing Driving Decisions Through Cooperative Perception SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
Automated Seam Folding and Sewing Machine on Pleated Pant...
Ray Wai Man Kong · 2025-07-31 · via cs.RO updates on arXiv.org

The applied research is the design and development of an automated folding and sewing machine for pleated pants. It represents a significant advancement in addressing the challenges associated with manual sewing processes. Traditional methods for creating pleats are labour-intensive, prone to inconsistencies, and require high levels of skill, making automation a critical need in the apparel industry. This research explores the technical feasibility and operational benefits of integrating advanced technologies into garment production, focusing on the creation of an automated machine capable of precise folding and sewing operations and eliminating the marking operation. The proposed machine incorporates key features such as a precision folding mechanism integrated into the automated sewing unit with real-time monitoring capabilities. The results demonstrate remarkable improvements: the standard labour time has been reduced by 93%, dropping from 117 seconds per piece to just 8 seconds with the automated system. Similarly, machinery time improved by 73%, and the total output rate increased by 72%. These enhancements translate into a cycle time reduction from 117 seconds per piece to an impressive 33 seconds, enabling manufacturers to meet customer demand more swiftly. By eliminating manual marking processes, the machine not only reduces labour costs but also minimizes waste through consistent pleat formation. This automation aligns with industry trends toward sustainability and efficiency, potentially reducing environmental impact by decreasing material waste and energy consumption.