Automatic Robotic Development through Collaborative Framework by Large Language Models
This work addresses the problem of automating intricate robot development for non-experts, representing an incremental improvement by applying existing LLM capabilities in a novel collaborative setup.
The authors tackled the challenge of automating complex robot development by proposing a collaborative framework using multiple large language models (LLMs) in distinct roles (analysts, programmers, testers), achieving robot development without requiring specialized knowledge and relying solely on non-experts.
Despite the remarkable code generation abilities of large language models LLMs, they still face challenges in complex task handling. Robot development, a highly intricate field, inherently demands human involvement in task allocation and collaborative teamwork . To enhance robot development, we propose an innovative automated collaboration framework inspired by real-world robot developers. This framework employs multiple LLMs in distinct roles analysts, programmers, and testers. Analysts delve deep into user requirements, enabling programmers to produce precise code, while testers fine-tune the parameters based on user feedback for practical robot application. Each LLM tackles diverse, critical tasks within the development process. Clear collaboration rules emulate real world teamwork among LLMs. Analysts, programmers, and testers form a cohesive team overseeing strategy, code, and parameter adjustments . Through this framework, we achieve complex robot development without requiring specialized knowledge, relying solely on non experts participation.