让机器人提前预判并应对人机协作中的失败
Anticipate, Adapt, Act: A Hybrid Framework for Task Planning
- 结合LLM预测与概率决策模型，动态分析任务风险
- 在VirtualHome环境中性能显著优于现有基线
- 适合需要人机协同的智能机器人系统
在复杂场景中实现人机有效协作，机器人需具备预见并适应失败的能力。尽管当前先进AI规划系统和大语言模型表现优异，但任务及其结果的不确定性仍构成挑战。为此，本文提出一种混合框架，融合大语言模型的通用预测能力与关系动态影响图语言的概率序贯决策能力。针对任意任务，机器人会评估任务及人类执行能力，预测因能力不足或缺乏相关领域对象导致的潜在失败，并采取预防或恢复行动。在VirtualHome 3D仿真环境中的实验表明，该方法相比现有最先进基线有显著性能提升。
原文摘要 · Abstract (English)
Anticipating and adapting to failures is a key capability robots need to collaborate effectively with humans in complex domains. This continues to be a challenge despite the impressive performance of state of the art AI planning systems and Large Language Models (LLMs) because of the uncertainty associated with the tasks and their outcomes. Toward addressing this challenge, we present a hybrid framework that integrates the generic prediction capabilities of an LLM with the probabilistic sequential decision-making capability of Relational Dynamic Influence Diagram Language. For any given task, the robot reasons about the task and the capabilities of the human attempting to complete it; predicts potential failures due to lack of ability (in the human) or lack of relevant domain objects; and executes actions to prevent such failures or recover from them. Experimental evaluation in the VirtualHome 3D simulation environment demonstrates substantial improvement in performance compared with state of the art baselines.
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