3960 papers
This category explores the vast and evolving landscape of research falling between the letters C and R, capturing a diverse array of scientific inquiries from chemistry to robotics. These studies often bridge the gap between theoretical models and real-world applications, offering fresh perspectives on everything from material science to complex biological systems. As these fields advance rapidly, staying updated with the latest findings can be challenging without a clear guide.
At Gist.Science, we ensure you never miss a breakthrough by processing every new preprint in this section directly from arXiv. Our team transforms complex academic manuscripts into accessible content, providing both plain-language overviews for general readers and detailed technical summaries for experts. This dual approach ensures that whether you are a student, researcher, or curious observer, you can grasp the core significance of each study without getting lost in dense jargon.
Below are the latest papers in this dynamic collection, sorted by their recent release dates to keep you at the forefront of discovery.
This study demonstrates that human trust in robotic systems significantly increases when robots acknowledge their errors or limitations, highlighting the critical role of transparency and error acknowledgment in fostering public acceptance and adoption of robotic technologies.
This paper introduces RoMu4o, a ground-based robotic manipulation unit equipped with a 6DOF arm and integrated hyperspectral sensor that autonomously grasps leaves and performs high-fidelity spectral analysis in unstructured orchard environments, achieving success rates of up to 95% in laboratory trials and 70% in field experiments.
This survey provides a comprehensive review of Event-Based Optical Marker Systems (EBOMS), analyzing their underlying principles, asynchronous operation, and robustness in challenging lighting conditions, while outlining key applications and future research directions in this emerging field.
ReLI is a cross-lingual framework that adapts large-scale foundation models to enable autonomous agents to understand natural language instructions, reason semantically, and execute tasks effectively across over 140 diverse languages, including low-resource and creole varieties, thereby overcoming the limitations of existing high-resource language-only systems.
This paper presents a robust immersive bilateral teleoperation framework for beyond-human-scale industrial manipulators that integrates VR, distributed haptics, and a force-sensorless adaptive controller to achieve high-fidelity motion/force transparency and significantly enhanced operator embodiment despite system asymmetries and communication delays.
This paper proposes a real-time, edge-aware lightweight monocular SLAM system for micro drones that fuses deep learning-based depth prediction with inertial data via an extended Kalman filter to achieve accurate 3D semi-dense mapping and autonomous navigation on resource-constrained platforms without relying on global loop closure.
This paper introduces the Lightweight Actuation-Space Energy Modeling (LASEM) framework, which formulates actuation potential energy directly in actuation space to derive a unified partial differential equation for cable-driven continuum robots, thereby achieving a 62.3% computational speedup while natively supporting both force and displacement input modes without explicitly calculating contact forces.
The paper introduces MR-STORM, a distributed sampling-based MPC framework that utilizes massively parallel GPU sampling and dynamic priority schemes to enable scalable, collision-free multi-arm manipulation in complex environments, demonstrating superior performance in both simulation and physical hardware.
The paper introduces WARP-RM, a fully self-supervised reward model that generates dense relative progress signals from time-warped successful demonstrations to enable WARP-BC, a behavior cloning method that significantly improves robot performance on long-horizon tasks by filtering and reweighting action chunks from mixed-quality data.
This paper introduces a robotic electrospinning platform that utilizes a six-axis arm-mounted emitter to deposit nanofiber membranes onto complex 3D conductive geometries, overcoming the field-shielding limitations of conventional planar systems through kinematic control, programmable grounding, and a comprehensive characterization of bio-compatible polymers and scaffold designs.