Algorithms, Artificial Intelligence, Civil and environmental engineering, Computer science and technology, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine learning, MIT Schwarzman College of Computing, Research, Robots, School of Engineering, UncategorizedJune 7, 202432Views0Likes0Comments
Researchers at MIT have developed an artificial intelligence tool to improve the efficiency of robotic warehouses. The researchers, drawing from their work in moderating traffic congestion, created a deep-learning model that can process information about the warehouse, including robot paths, tasks, and obstacles, to optimize warehouse functionality. By grouping the robots into smaller units, the…
In crowded robotic warehouses, managing hundreds of robots to navigate efficiently and avoid collisions is a complex challenge. This has prompted a group of MIT researchers to create a deep-learning model to tackle the problem. Using AI, the team has developed an innovative model that encapsulates details about the warehouse environment - the robots, planned…
As automated warehouses become increasingly popular in various industries, ensuring the efficiency and safety of hundreds of robots navigating such spaces is a significant challenge. Notably, even top-performing algorithms struggle to keep pace with the demands of e-commerce or manufacturing. To address this, a group of MIT researchers, who use AI to mitigate traffic congestion,…
Navigating hundreds of robots in a warehouse without causing accidents is a growing challenge for many industries, from e-commerce to automotive production. To address this issue, a team of researchers at the Massachusetts Institute of Technology (MIT) has developed an AI-based approach that increases efficiency and reduces congestion.
The team devised a deep learning model that…
MIT researchers have developed a technique to train robots on multiple tasks by combining and optimising data from a variety of sources. At the core of their work is a type of generative AI known as a 'diffusion model', which learns from a specific dataset to complete a task. However, the particular innovation here lies…
A team of MIT researchers have developed a novel cryptographic ID tag to tackle product counterfeiting. This tag, which is remarkably smaller and cost-efficient than traditional radio frequency tags (RFIDs), uses terahertz waves to perform authentication. The small size of terahertz waves and their greater frequency compared to radio waves make them ideal for this…
MIT researchers have developed an anti-tampering ID tag that provides improved security compared to traditional radio frequency ID (RFID) tags that are commonly used for authentication.
The new tag, which is smaller, cheaper, and more secure than RFIDs, uses terahertz (THz) waves for authentication. However, like traditional RFIDs, it faced a vulnerability where counterfeiters could…
The ability to confirm the authenticity of products has become a paramount need in our world today, especially with the rise of counterfeiting. The most common method often used is radio frequency tags or RFIDs, which confirms the authenticity of a product but at a size and cost disadvantage. However, a new research by the…
MIT researchers have advanced their previously developed cryptographic ID tag that uses terahertz waves instead of radio frequency (RFID) technology, to bolster its security and undermine counterfeiting efforts. The initial model of their tag had a major flaw in that it could be peeled off a genuine item and reattached to a counterfeit, thereby tricking…