CANVAS：用于直观人机交互的常识感知导航系统
CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction
AI总结：
CANVAS框架结合视觉与语言指令，通过模仿学习实现常识感知导航，在噪声指令下优于ROS NavStack，果园环境成功率67%，真实世界迁移成功率69%。
摘要：
现实生活中的机器人导航不仅仅是到达目的地；它还需要在解决特定场景目标的同时优化运动。人类表达这些目标的一种直观方式是通过抽象线索，如口头命令或粗略草图。此类人类指导可能缺乏细节或带有噪声。尽管如此，我们期望机器人能够按预期进行导航。为了让机器人能够按照人类期望解释并执行这些抽象指令，它们必须与人类共享对基本导航概念的共同理解。为此，我们引入了CANVAS，一种新颖的框架，它结合了视觉和语言指令以实现常识感知导航。其成功源于模仿学习，使机器人能够从人类导航行为中学习。我们提出了COMMAND，一个包含人工标注导航结果的综合数据集，涵盖超过48小时和219公里，旨在模拟环境中训练常识感知导航系统。我们的实验表明，CANVAS在所有环境中均优于强大的基于规则的ROS NavStack系统，在噪声指令下展现出卓越性能。值得注意的是，在果园环境中，ROS NavStack的总成功率为0%，而CANVAS实现了67%的总成功率。即使在未见过的环境中，CANVAS也紧密契合人类演示和常识约束。此外，CANVAS的真实世界部署展示了令人印象深刻的Sim2Real迁移，总成功率为69%，凸显了在模拟环境中从人类演示学习对现实世界应用的潜力。
英文摘要：
Real-life robot navigation involves more than just reaching a destination; it requires optimizing movements while addressing scenario-specific goals. An intuitive way for humans to express these goals is through abstract cues like verbal commands or rough sketches. Such human guidance may lack details or be noisy. Nonetheless, we expect robots to navigate as intended. For robots to interpret and execute these abstract instructions in line with human expectations, they must share a common understanding of basic navigation concepts with humans. To this end, we introduce CANVAS, a novel framework that combines visual and linguistic instructions for commonsense-aware navigation. Its success is driven by imitation learning, enabling the robot to learn from human navigation behavior. We present COMMAND, a comprehensive dataset with human-annotated navigation results, spanning over 48 hours and 219 km, designed to train commonsense-aware navigation systems in simulated environments. Our experiments show that CANVAS outperforms the strong rule-based system ROS NavStack across all environments, demonstrating superior performance with noisy instructions. Notably, in the orchard environment, where ROS NavStack records a 0% total success rate, CANVAS achieves a total success rate of 67%. CANVAS also closely aligns with human demonstrations and commonsense constraints, even in unseen environments. Furthermore, real-world deployment of CANVAS showcases impressive Sim2Real transfer with a total success rate of 69%, highlighting the potential of learning from human demonstrations in simulated environments for real-world applications.
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。
