PlanIt：一种从大规模偏好反馈学习规划路径的众包方法
PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large Scale Preference Feedback
摘要：
我们考虑的问题，学习用户的偏好超过机器人轨迹的环境中丰富的对象和人类。这是具有挑战性的，因为定义良好轨迹的标准随环境中的用户、任务和交互而变化。我们使用机器人学习的成本函数表示轨迹偏好，并使用它在新环境中生成良好的轨迹。我们设计了一个众包系统- PlanIt，其中非专家用户标记的机器人的轨迹段。PlanIt允许我们收集大量的用户反馈，并使用PlanIt中的弱标签和噪声标签来学习模型的参数。我们在122个不同的环境中测试我们的方法，用于机器人导航和操作任务。我们广泛的实验表明，学习的成本函数在人类环境中生成首选轨迹。我们的众包系统是公开的，用于可视化学习成本和提供偏好反馈：\url{http://planit.cs.cornell.edu}
英文摘要：
We consider the problem of learning user preferences over robot trajectories for environments rich in objects and humans. This is challenging because the criterion defining a good trajectory varies with users, tasks and interactions in the environment. We represent trajectory preferences using a cost function that the robot learns and uses it to generate good trajectories in new environments. We design a crowdsourcing system - PlanIt, where non-expert users label segments of the robot's trajectory. PlanIt allows us to collect a large amount of user feedback, and using the weak and noisy labels from PlanIt we learn the parameters of our model. We test our approach on 122 different environments for robotic navigation and manipulation tasks. Our extensive experiments show that the learned cost function generates preferred trajectories in human environments. Our crowdsourcing system is publicly available for the visualization of the learned costs and for providing preference feedback: \url{http://planit.cs.cornell.edu}
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。
