Executable Robot

Executable robot research focuses on bridging the gap between high-level human instructions and low-level robot actions, enabling robots to autonomously perform complex tasks. Current efforts leverage large language models (LLMs) to translate natural language instructions, visual demonstrations (e.g., from VR or assembly manuals), or even recipes into executable robot code, often incorporating formalisms like Linear Temporal Logic (LTL) for precise temporal planning. These systems often integrate LLMs with other AI components, such as vision-language models and reinforcement learning, to handle plan execution, error recovery, and environmental awareness, improving task success rates and efficiency. This work has significant implications for automating complex tasks in various domains, from manufacturing and logistics to assistive robotics and home service.