用大模型让机器人在农田里根据语言描述找东西，又快又准。
Language-Guided Object Search in Agricultural Environments
- 用大模型分析物体间语义关系，规划高效搜索路径。
- 仿真测试路径效率达84%，真实农场成功率80%。
- 适合农业机器人、具身智能研究者参考。
打造能协助农场和花园作业的机器人可减轻农工的身心负担。本文解决农田环境中的目标物体搜索问题，提出一种方法：利用大语言模型（LLM）对环境中已见物体与未见目标物体进行语义推理，从而定位目标。通过建模物体间的语义关联，规划路径以提升定位准确率和效率，同时减少总行程距离，无需依赖房间或区域级别的语义信息。实验表明，该方法优于当前最先进的基线及消融实验。离线测试中路径效率平均为84%，表明预测路径与理想路径高度吻合。在真实农场环境部署于波士顿动力Spot机器人时，系统成功率达80%，路径长度加权成功率为0.67，体现了任务成功率与路径效率之间的合理权衡。
原文摘要 · Abstract (English)
Creating robots that can assist in farms and gardens can help reduce the mental and physical workload experienced by farm workers. We tackle the problem of object search in a farm environment, providing a method that allows a robot to semantically reason about the location of an unseen target object among a set of previously seen objects in the environment using a Large Language Model (LLM). We leverage object-to-object semantic relationships to plan a path through the environment that will allow us to accurately and efficiently locate our target object while also reducing the overall distance traveled, without needing high-level room or area-level semantic relationships. During our evaluations, we found that our method outperformed a current state-of-the-art baseline and our ablations. Our offline testing yielded an average path efficiency of 84%, reflecting how closely the predicted path aligns with the ideal path. Upon deploying our system on the Boston Dynamics Spot robot in a real-world farm environment, we found that our system had a success rate of 80%, with a success weighted by path length of 0.67, which demonstrates a reasonable trade-off between task success and path efficiency under real-world conditions. The project website can be viewed at https://adi-balaji.github.io/losae/
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