Affordance-Aware Interactive Decision-Making and Execution for Ambiguous Instructions

Abstract: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.

Robotics (cs.RO)

arXiv:2602.05273 [cs.RO]

(or arXiv:2602.05273v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2602.05273 Focus to learn more arXiv-issued DOI via DataCite

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