We have seen the future of robots, and it includes Meta PARTNR. This project basically aims to unlock human-robot collaboration and consists of 100,000 natural language tasks that are designed to study multi-agent reasoning as well as planning.
PARTNR essentially utilizes LLMs (Large Language Models) to generate tasks at scale by incorporating simulation-in-the-loop to ground the LLMs and reduce errors. These 100,000 tasks are used to train AI models through simulation-based human demonstrations, thus furthering the development of complex and adaptable models capable of understanding as well as performing a variety of tasks more effectively.
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Our analysis reveals significant limitations in SoTA LLM-based planners, such as poor coordination and failures in task tracking and recovery from errors. While people solve 93% of tasks, LLMs solve only 30% of tasks,” said Meta.
