ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models
arxiv(2024)
摘要
Incorporating language comprehension into robotic operations unlocks
significant advancements in robotics, but also presents distinct challenges,
particularly in executing spatially oriented tasks like pattern formation. This
paper introduces ZeroCAP, a novel system that integrates large language models
with multi-robot systems for zero-shot context aware pattern formation.
Grounded in the principles of language-conditioned robotics, ZeroCAP leverages
the interpretative power of language models to translate natural language
instructions into actionable robotic configurations. This approach combines the
synergy of vision-language models, cutting-edge segmentation techniques and
shape descriptors, enabling the realization of complex, context-driven pattern
formations in the realm of multi robot coordination. Through extensive
experiments, we demonstrate the systems proficiency in executing complex
context aware pattern formations across a spectrum of tasks, from surrounding
and caging objects to infilling regions. This not only validates the system's
capability to interpret and implement intricate context-driven tasks but also
underscores its adaptability and effectiveness across varied environments and
scenarios. More details about this work are available at:
https://sites.google.com/view/zerocap/home
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