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Meta Reality Labs Research Introduces Sonata: Advancing Self-Supervised Representation Learning for 3D Point Clouds

3D self-supervised learning (SSL) has faced persistent challenges in developing semantically meaningful point representations suitable for diverse applications with minimal supervision. Despite substantial progress in image-based SSL, existing point cloud SSL methods have largely been limited due to the issue known as the “geometric shortcut,” where models excessively rely on low-level geometric features like surface…

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This AI Paper Introduces an LLM+FOON Framework: A Graph-Validated Approach for Robotic Cooking Task Planning from Video Instructions

Robots are increasingly being developed for home environments, specifically to enable them to perform daily activities like cooking. These tasks involve a combination of visual interpretation, manipulation, and decision-making across a series of actions. Cooking, in particular, is complex for robots due to the diversity in utensils, varying visual perspectives, and frequent omissions of intermediate…

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VideoMind: A Role-Based Agent for Temporal-Grounded Video Understanding

LLMs have shown impressive capabilities in reasoning tasks like Chain-of-Thought (CoT), enhancing accuracy and interpretability in complex problem-solving. While researchers are extending these capabilities to multi-modal domains, videos present unique challenges due to their temporal dimension. Unlike static images, videos require understanding dynamic interactions over time. Current visual CoT methods excel with static inputs but…

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Evaluating potential cybersecurity threats of advanced AI

Artificial intelligence (AI) has long been a cornerstone of cybersecurity. From malware detection to network traffic analysis, predictive machine learning models and other narrow AI applications have been used in cybersecurity for decades. As we move closer to artificial general intelligence (AGI), AI's potential to automate defenses and fix vulnerabilities becomes even more powerful. But…

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Sensor-Invariant Tactile Representation for Zero-Shot Transfer Across Vision-Based Tactile Sensors

Tactile sensing is a crucial modality for intelligent systems to perceive and interact with the physical world. The GelSight sensor and its variants have emerged as influential tactile technologies, providing detailed information about contact surfaces by transforming tactile data into visual images. However, vision-based tactile sensing lacks transferability between sensors due to design and manufacturing…

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