Pragmatic instruction following and goal assistance are crucial in human-robot cooperation. CLIPS outperforms baselines in accuracy and helpfulness by leveraging joint planning, rational speech act theory, and multimodal goal inference. The model successfully resolves ambiguity in instructions, interprets joint intentions, and provides efficient assistance under uncertainty.
People often give ambiguous instructions expecting actions to clarify intentions. CLIPS assists humans by modeling them as cooperative planners communicating joint plans through language. The model uses large language models to evaluate instructions' likelihood given a hypothesized plan.
CLIPS significantly outperforms baselines in accuracy and helpfulness by incorporating pragmatic context into goal inference and assistance. The model's success lies in its ability to interpret ambiguous language, understand joint instructions, and provide effective support even with uncertain goals.
To Another Language
from source content
arxiv.org
Ключові висновки, отримані з
by Tan Zhi-Xuan... о arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.17930.pdfГлибші Запити