Enabling robots to explore and act in unfamiliar environments under ambiguous human instructions by interactively identifying task-relevant objects (e.g., cups or beverages for "I'm thirsty") remains challenging for existing vision-language model (VLM)-based methods, due to inefficient reasoning and limited environmental interaction. These factors hinder real-time task planning and execution. To address this, we propose Affordance-Aware Interactive Decision-Making and Execution for Ambiguous Instructions (AIDE), a human-inspired dual-stream framework that integrates interactive exploration with vision-language reasoning: mirroring the complementary deliberative and intuitive processes of human cognition, Multi-Stage Inference (MSI) serves as the decision-making stream and Accelerated Decision-Making (ADM) as the execution stream, enabling zero-shot affordance analysis and interpretation of ambiguous instructions. Experiments in simulation and real-world settings show that AIDE achieves over 80% task planning success rate and more than 95% closed-loop continuous execution accuracy at 10 Hz, surpassing existing VLM-based methods in diverse open scenarios.

翻译：暂无翻译