Can you pass that tool?: Implications of Indirect Speech in Physical Human-Robot Collaboration

Abstract

Indirect speech acts (ISAs) are a natural pragmatic feature of human communication, allowing requests to be conveyed implicitly while maintaining subtlety and flexibility. Although advancements in speech recognition have enabled natural language interactions with robots through direct, explicit commands -- roviding clarity in communication -- the rise of large language models presents the potential for robots to interpret ISAs. However, empirical evidence on the effects of ISAs on human-robot collaboration (HRC) remains limited. To address this, we conducted a Wizard-of-Oz study (N=36), engaging a participant and a robot in collaborative physical tasks. Our findings indicate that robots capable of understanding ISAs significantly improve human's perceived robot anthropomorphism, team performance, and trust. However, the effectiveness of ISAs is task- and context-dependent, thus requiring careful use. These results highlight the importance of appropriately integrating direct and indirect requests in HRC to enhance collaborative experiences and task performance.

Accepted by CHI2025

References in corpus (6)

• Understanding User Perceptions, Collaborative Experience and User Engagement in Different Human-AI Interaction Designs for Co-Creative Systems
• "I am the follower, also the boss": Exploring Different Levels of Autonomy and Machine Forms of Guiding Robots for the Visually Impaired
• A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems
• User-LLM: Efficient LLM Contextualization with User Embeddings
• Reasoning Grasping via Multimodal Large Language Model
• Implicit Communication of Contextual Information in Human-Robot Collaboration