ADAPT：在未指定 affordance 约束下评估常识规划的基准测试
ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints
AI总结：
本文提出 ADAPT 模块，通过显式 affordance 推理提升智能具身代理的鲁棒性和任务成功率，展示了领域适应的视觉语言模型在 affordance 推理中的优越性。
摘要：
智能具身代理不应仅仅遵循指令，因为现实世界环境往往涉及意外情况和例外。然而，现有方法通常专注于直接执行指令，而不考虑目标对象是否能实际被操控，意味着它们未能评估可用的 affordances。为了解决这一限制，我们引入了 DynAfford，一个评估在动态环境中物体 affordances 可能随时间变化且未在指令中指定的具身代理的基准。DynAfford 要求代理感知物体状态，推断隐含的前提条件，并相应地调整其行为。为了实现这一能力，我们引入了 ADAPT，一个即插即用的模块，它通过显式 affordance 推理增强现有规划器。实验表明，结合 ADAPT 显著提高了在已见和未见环境中的鲁棒性和任务成功率。我们还展示了用于 affordance 推理后端的领域适应、LoRA 微调的视觉语言模型优于商业 LLM（GPT-4o），突显了任务对齐的 affordance 地基的重要性。
英文摘要：
Intelligent embodied agents should not simply follow instructions, as real-world environments often involve unexpected conditions and exceptions. However, existing methods usually focus on directly executing instructions, without considering whether the target objects can actually be manipulated, meaning they fail to assess available affordances. To address this limitation, we introduce DynAfford, a benchmark that evaluates embodied agents in dynamic environments where object affordances may change over time and are not specified in the instruction. DynAfford requires agents to perceive object states, infer implicit preconditions, and adapt their actions accordingly. To enable this capability, we introduce ADAPT, a plug-and-play module that augments existing planners with explicit affordance reasoning. Experiments demonstrate that incorporating ADAPT significantly improves robustness and task success across both seen and unseen environments. We also show that a domain-adapted, LoRA-finetuned vision-language model used as the affordance inference backend outperforms a commercial LLM (GPT-4o), highlighting the importance of task-aligned affordance grounding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
