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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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Efficient Inference-Time Scaling for Flow Models: Enhancing Sampling Diversity and Compute Allocation

Recent advancements in AI scaling laws have shifted from merely increasing model size and training data to optimizing inference-time computation. This approach, exemplified by models like OpenAI o1 and DeepSeek R1, enhances model performance by leveraging additional computational resources during inference. Test-time budget forcing has emerged as an efficient technique in LLMs, enabling improved performance…

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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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