Think, Act, and Ask: Open-World Interactive Personalized Robot Navigation
This addresses the challenge of personalized object navigation for robotics, though it is incremental by extending zero-shot navigation with interaction.
The paper tackles the problem of robots navigating to personalized objects in unknown environments by introducing Zero-shot Interactive Personalized Object Navigation (ZIPON), where agents engage in conversations with users, and proposes the ORION framework using LLMs for sequential decision-making, showing that interactive agents with user feedback significantly improve performance.
Zero-Shot Object Navigation (ZSON) enables agents to navigate towards open-vocabulary objects in unknown environments. The existing works of ZSON mainly focus on following individual instructions to find generic object classes, neglecting the utilization of natural language interaction and the complexities of identifying user-specific objects. To address these limitations, we introduce Zero-shot Interactive Personalized Object Navigation (ZIPON), where robots need to navigate to personalized goal objects while engaging in conversations with users. To solve ZIPON, we propose a new framework termed Open-woRld Interactive persOnalized Navigation (ORION), which uses Large Language Models (LLMs) to make sequential decisions to manipulate different modules for perception, navigation and communication. Experimental results show that the performance of interactive agents that can leverage user feedback exhibits significant improvement. However, obtaining a good balance between task completion and the efficiency of navigation and interaction remains challenging for all methods. We further provide more findings on the impact of diverse user feedback forms on the agents' performance. Code is available at https://github.com/sled-group/navchat.