基于知识图谱嵌入的鲁棒一次性任务执行
Towards Robust One-shot Task Execution using Knowledge Graph Embeddings
摘要：
要求对任务计划进行多次演示给机器人的最终用户带来了负担。然而，从单个最终用户演示中稳健地执行任务计划是机器人技术中的一个持续挑战。我们解决的问题，一次性任务执行，其中机器人必须概括一个单一的示范或原型的例子，一个新的执行环境的任务计划。我们的方法将任务计划与领域知识相结合，以推断新的执行环境的任务计划组成部分。我们的实验评估表明，我们的知识表示，使更相关的概括，导致显着更高的成功率超过测试基线。我们在一个物理平台上验证了该方法，这导致初始任务计划成功推广到50个执行环境中的38个，其中包括自主机器人操作造成的错误。
英文摘要：
Requiring multiple demonstrations of a task plan presents a burden to end-users of robots. However, robustly executing tasks plans from a single end-user demonstration is an ongoing challenge in robotics. We address the problem of one-shot task execution, in which a robot must generalize a single demonstration or prototypical example of a task plan to a new execution environment. Our approach integrates task plans with domain knowledge to infer task plan constituents for new execution environments. Our experimental evaluations show that our knowledge representation makes more relevant generalizations that result in significantly higher success rates over tested baselines. We validated the approach on a physical platform, which resulted in the successful generalization of initial task plans to 38 of 50 execution environments with errors resulting from autonomous robot operation included.
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。
