Counterfactual Subgoal Verification for Open-Vocabulary Indoor Object Search
Keywords:
Open-Vocabulary Navigation, Counterfactual Reasoning, Subgoal Verification, Autonomous Robotics, Vision-Language ModelsAbstract
The integration of large vision-language models into autonomous robotics has significantly advanced the capability of artificial agents to navigate complex indoor environments based on natural language instructions. Among the most challenging tasks in this domain is open-vocabulary indoor object search, wherein a robot is deployed into an unexplored environment and tasked with locating arbitrary, potentially long-tail objects specified by free-form text. While recent hierarchical planning frameworks leverage vision-language models to propose intermediate subgoals for exploration, these models are notoriously prone to hallucination. They frequently generate visually plausible but physically invalid or logically inconsistent subgoals, leading to inefficient trajectories, getting trapped in local minima, or catastrophic task failure. To address this critical bottleneck, this paper introduces a novel framework termed Counterfactual Subgoal Verification. By systematically generating and evaluating counterfactual scenarios prior to subgoal execution, the proposed system assesses whether the proposed intermediate waypoints hold true under modified causal assumptions about the environment. Through extensive simulation across highly realistic indoor environments, the counterfactual verification mechanism is shown to drastically reduce the execution of hallucinatory subgoals. The framework effectively prunes the search space and refines exploration strategies dynamically, leading to significant improvements in both task success rates and path efficiency metrics compared to traditional semantic exploration methods.
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