Autonomous robots can benefit greatly from human-provided semantic characterizations of uncertain task environments and states. However, the development of integrated strategies which let robots model, communicate, and act on such 'soft data' remains challenging. Here, the Human Assisted Robotic Planning and Sensing (HARPS) framework is presented for active semantic sensing and planning in human-robot teams to address these gaps by formally combining the benefits of online sampling-based POMDP policies, multimodal semantic interaction, and Bayesian data fusion. This approach lets humans opportunistically impose model structure and extend the range of semantic soft data in uncertain environments by sketching and labeling arbitrary landmarks across the environment. Dynamic updating of the environment model while during search allows robotic agents to actively query humans for novel and relevant semantic data, thereby improving beliefs of unknown environments and states for improved online planning. Simulations of a UAV-enabled target search application in a large-scale partially structured environment show significant improvements in time and belief state estimates required for interception versus conventional planning based solely on robotic sensing. Human subject studies in the same environment (n = 36) demonstrate an average doubling in dynamic target capture rate compared to the lone robot case, and highlight the robustness of active probabilistic reasoning and semantic sensing over a range of user characteristics and interaction modalities.
翻译:自主机器人可以从人类提供的不确定任务环境和状态的语义特征中受益匪浅。然而,制定综合战略,让机器人建模、交流并使用这种“软数据”仍然具有挑战性。在这里,人类辅助机器人规划和遥感(HARPS)框架(HARPS)展示给人类机器人团队积极进行语义感学和规划,以便通过正式结合基于在线取样的POMDP政策、多式联运语义互动和巴耶斯数据融合的好处,解决这些差距。这种方法让人类在不确定环境中随机地将模型结构扩大和扩大语义软数据的范围,在环境中绘制草图并标注任意的地标注。环境模型动态更新同时允许机器人代理商积极向人类查询新颖和相关语义数据,从而增进对未知环境和状态的信念,改善在线规划。在大规模局部结构化环境中模拟由UOMDP驱动的目标搜索应用显示,时间和信仰状况的估计数大大改进,而常规规划所需要的估计数则仅以机器人感测为基础。同一环境中的人类主题研究主题特征研究,在动态和真实性推理学率中,展示了动态率平均测测测程率率率的模型,展示了比平平平平平级测测程率率率率率。