助人机器狗：使用腿式机器人与视觉-语言模型进行开放世界物体取回
Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models
AI总结：
本文提出一种四足移动操作机器人系统，结合前置夹爪、仿真训练的低层控制器和视觉-语言模型，在未见过的室内环境中零样本完成爬床取物等指令任务，成功率达60%。
摘要：
基于学习的方法在四足运动方面已取得强劲表现。然而，若干挑战阻碍了四足机器人学习需要与环境及人类交互的有用室内技能：缺乏用于操作任务的末端执行器、仅使用仿真数据导致语义理解有限，以及室内环境中的可通行性和可达性较低。我们提出了一个用于室内环境中四足移动操作的系统。该系统使用前置夹爪进行物体操作，使用在仿真中通过自我中心深度训练的低层控制器来实现攀爬和全身倾斜等敏捷技能，并使用预训练的视觉-语言模型（VLMs）搭配第三人称鱼眼摄像头和自我中心RGB摄像头进行语义理解和指令生成。我们在两个未见过的环境中评估了该系统，且未进行任何真实世界数据收集或训练。我们的系统能够零样本泛化到这些环境并完成任务，例如在爬过一张大号双人床后，按照用户指令取回一个随机放置的毛绒玩具，成功率为60%。项目网站：https://helpful-doggybot.github.io/
英文摘要：
Learning-based methods have achieved strong performance for quadrupedal locomotion. However, several challenges prevent quadrupeds from learning helpful indoor skills that require interaction with environments and humans: lack of end-effectors for manipulation, limited semantic understanding using only simulation data, and low traversability and reachability in indoor environments. We present a system for quadrupedal mobile manipulation in indoor environments. It uses a front-mounted gripper for object manipulation, a low-level controller trained in simulation using egocentric depth for agile skills like climbing and whole-body tilting, and pre-trained vision-language models (VLMs) with a third-person fisheye and an egocentric RGB camera for semantic understanding and command generation. We evaluate our system in two unseen environments without any real-world data collection or training. Our system can zero-shot generalize to these environments and complete tasks, like following user's commands to fetch a randomly placed stuff toy after climbing over a queen-sized bed, with a 60% success rate. Project website: https://helpful-doggybot.github.io/
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。
