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Meet HITL-TAMP: A New AI Approach to Teach Robots Complex Manipulation Skills Through a Hybrid Strategy of Automated Planning and Human Control

Teaching robots complicated manipulation skills through observation of human demonstrations has shown promising results. Providing extensive manipulation demonstrations is time-consuming and labor costly, making it challenging to scale up this paradigm to real-world long-horizon operations. However, not all facets of a task are created equal. A new study by NVIDIA and Georgia Institute of Technology…

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This AI Research from China Introduces GS-SLAM: A Novel Approach for Enhanced 3D Mapping and Localization

Researchers from Shanghai AI Laboratory, Fudan University, Northwestern Polytechnical University, and The Hong Kong University of Science and Technology have collaborated to develop a 3D Gaussian representation-based Simultaneous Localization and Mapping (SLAM) system named GS-SLAM. The goal of the plan is to achieve a balance between accuracy and efficiency. GS-SLAM uses a real-time differentiable splatting…

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How to implement Adaptive AI in your business | by LeewayHertz

Artificial intelligence has emerged as a powerful technology that can drive substantial transformations in businesses across diverse industries. However, traditional machine learning models have struggled to keep pace with the dynamic nature of our rapidly evolving world, hindering their effectiveness in handling the influx of data generated by the Internet of Things (IoT) and autonomous…

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dbt Core, Snowflake, and GitHub Actions: pet project for Data Engineers | by Kyiv Data Girl – Kateryna | Dec, 2023

Pet Project for Data/Analytics Engineers: Explore Modern Data Stack Tools — dbt Core, Snowflake, Fivetran, GitHub Actions. Photo by Gaining Visuals on UnsplashHere is a simple and fast pet project for Data/Analytics Engineers, who want to kick the tires on Modern Data Stack tools including dbt Core, Snowflake, Fivetran, and GitHub Actions. This hands-on experience…

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Researchers at Stanford Introduce RoboFuME: Revolutionizing Robotic Learning with Minimal Human Input

In many domains that involve machine learning, a widely successful paradigm for learning task-specific models is to first pre-train a general-purpose model from an existing diverse prior dataset and then adapt the model with a small addition of task-specific data. This paradigm is attractive to real-world robot learning since collecting data on a robot is…

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