May 12, 2024, 5:43 a.m. | Mohammad Asjad


The robotics field has historically vacillated between two primary architectural paradigms: modular hierarchical policies and end-to-end policies. Modular hierarchies employ rigid layers such as symbolic planning, trajectory generation, and tracking, while end-to-end policies utilize high-capacity neural networks to map sensory input directly to actions. The emergence of large language models (LLMs) has renewed interest in […]

The post UC Berkeley Researchers Introduce Learnable Latent Codes as Bridges (LCB): A Novel AI Approach that Combines the Abstract Reasoning Capabilities of Large …

abstract ai paper summary ai shorts applications artificial intelligence berkeley capabilities editors pick hierarchical language language model language models large language large language model large language models low machine learning modular novel novel ai planning policies reasoning researchers robotics staff tech news technology tracking trajectory uc berkeley while

More from / MarkTechPost

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Manager, Business Intelligence

@ Revlon | New York City, United States