Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning

แนวคิดหลัก

The author introduces CLIPS, a Bayesian agent architecture for pragmatic instruction following and goal assistance, modeling humans as cooperative planners communicating joint plans to the assistant. CLIPS uses multimodal Bayesian inference to pragmatically follow ambiguous instructions and provide effective assistance even when uncertain about the goal.

บทคัดย่อ

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.

สถิติ

CLIPS significantly outperforms GPT-4V.
Multimodal LLMs have access to all information but fail to ground it in a coherent theory-of-mind.
CLIPS achieves much higher goal accuracy and cooperative efficiency than other methods.
Unimodal inverse planning struggles with pragmatic context.
Literal instruction following methods disregard pragmatic context.

คำพูด

"CLIPS significantly outperforms GPT-4V."
"Multimodal LLMs have access to all information but fail to ground it in a coherent theory-of-mind."

ข้อมูลเชิงลึกที่สำคัญจาก

Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning

by Tan Zhi-Xuan... ที่ arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17930.pdf

สอบถามเพิ่มเติม

How can CLIPS be adapted for real-world applications beyond simulation

CLIPS can be adapted for real-world applications beyond simulation by integrating it into existing robotic systems. By incorporating CLIPS into robots, they can assist humans in various tasks that require cooperation and communication. For example, in a manufacturing setting, CLIPS could help guide human workers through complex assembly processes by interpreting their instructions and inferring their goals. This would enhance the efficiency and effectiveness of human-robot collaboration on the factory floor. Additionally, in healthcare settings, CLIPS could assist medical professionals by understanding their verbal commands and providing relevant support during procedures or patient care.

What counterarguments exist against the effectiveness of CLIPS in human-robot cooperation

Counterarguments against the effectiveness of CLIPS in human-robot cooperation may include concerns about scalability and generalization to diverse environments. Critics might argue that while CLIPS performs well in controlled simulation settings like Doors, Keys & Gems or VirtualHome, its performance may degrade when faced with real-world variability and unpredictability. Additionally, there could be skepticism about the ability of CLIPS to handle nuanced social cues or ambiguous language that often arise in human communication. Some critics may also question the computational complexity of implementing joint intentionality at scale across different domains.

How might the concept of joint intentionality impact future developments in artificial intelligence

The concept of joint intentionality can have significant implications for future developments in artificial intelligence by fostering more natural and effective interactions between humans and AI systems. By incorporating principles of joint intentionality into AI models, machines can better understand human intentions, collaborate seamlessly with users on shared goals, and adapt to dynamic changes during cooperative tasks. This approach opens up possibilities for creating AI systems that are not only intelligent but also empathetic and socially aware—paving the way for more intuitive interfaces, personalized assistance, and enhanced teamwork between humans and machines.