AI-সহায়ক শেখার প্ল্যাটফর্ম রোবটদেরকে আক্ষরিক নির্দেশের বাইরে যুক্তি প্রয়োগে সাহায্য করে

এটি ফিডে পাওয়া অংশবিশেষ। এটি সংক্ষিপ্ত বা প্রকাশকের মূল ভাষায় হতে পারে।

Humans are intuitive by nature. We can sense meaning beyond the literal words that we encounter. Now, a new artificial intelligence-assisted learning platform developed by MIT engineers is enabling robots to read larger meaning into a human’s natural language commands. In a study to be presented at the annual Conference on Robot Learning this month, researchers from MIT and New York University introduce FLEX, a flexible feature-editing pipeline for robot motion-planning. With FLEX, humans can interact with a robot by giving it instructions in natural, conversational language. The robot can not only follow the instructions literally but also read into the words to draw larger generalizations that help to improve its motion-planning task. The team demonstrated FLEX in several simulated scenarios, including one in which a robot serves a cup of coffee. In these virtual scenarios, a person interacts with a robotic arm by typing commands in natural language. As the robot moves the coffee cup over a cluttered table, the person might type “Don’t spill on the laptop!” In response, the robot will reason about why it should avoid the laptop. For this, FLEX incorporates a large language model (LLM) — a type of AI system that is trained on enormous libraries of text, including social media posts, video transcriptions, and web content, which the LLM uses to identify larger trends, patterns, and generalizations. “LLMs have seen so many texts from the internet,” says study author Nathan Dennler, a postdoc in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “People might have written about, ‘Oh, my phone dropped in the water and now it doesn’t work,’ or ‘I spilled coffee on my computer and now it doesn’t work.’ So LLMs pick up on the idea that liquids plus devices leads to things not working.”Through the LLM, the robot reasons that it should also avoid bumping into all electronics. In its updated path, the robot adjusts to avoid both the laptop and a nearby pho