Roboticists and researchers at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) are working to develop a system that can train robots to perform tasks in specific environments effectively. The ongoing research aims to help robots deal with disturbances, distractions, and changes in their operational environments. For this, they have proposed a method to create…
Neural networks have been of immense benefit in the design of robot controllers, boosting the adaptive and effectiveness abilities of these machines. However, their complex nature makes it challenging to confirm their safe execution of assigned tasks. Traditionally, the verification of safety and stability are done using Lyapunov functions. If a Lyapunov function that consistently…
Researchers from the Massachusetts Institute of Technology's (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed an algorithm to mitigate the risks associated with using neural networks in robots. The complexity of neural network applications, while offering greater capability, also makes them unpredictable. Current safety and stability verification techniques, called Lyapunov functions, do not…
As robots are increasingly being deployed for complex household tasks, engineers at MIT are trying to equip them with common-sense knowledge allowing them to swiftly adapt when faced with disruptions. A newly developed method by the researchers merges robot motion data and common-sense knowledge from extensive language models (LLMs).
The new approach allows a robot to…
Researchers from MIT and the MIT-IBM Watson AI Lab have developed a language-based navigational strategy for AI robots. The method uses textual descriptions instead of visual information, effectively simplifying the process of robotic navigation. Visual data traditionally requires significant computational capacity and detailed hand-crafted machine-learning models to function effectively. The researchers' approach involves converting a…
MIT researchers have developed a deep-learning model to improve the efficiency of warehouse robots. The team used a neural network architecture to encode features including the robots' paths, tasks, and obstacles in the warehouse. This enabled the model to predict where congestion was most likely to occur and take measures to counteract it.
The groundbreaking method…
MIT researchers have designed an artificial intelligence solution to help robotic warehouses operate more efficiently. Automated warehouses, which employ hundreds of robots to pick and deliver goods, are becoming more commonplace, especially in industries such as e-commerce and automotive production. However, coordinating this robot workforce to avoid collisions, while also maintaining a high operational pace,…
In an enormous robotic warehouse, hundreds of robots zip back and forth, picking up items and delivering them to human workers for packing and shipping. This is becoming an increasingly common scene in various industries, from e-commerce to automotive manufacturing. However, managing these large numbers of robots, ensuring they reach their destinations effectively, and avoiding…