Recovering from failure by asking for help
Recovering from failure by asking for help
复制标题
通过寻求帮助从失败中恢复
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
D. Rus
中科院分区：
文献类型：
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作者：
Ross A. Knepper;Stefanie Tellex;A. Li;N. Roy;D. Rus
机器人不可避免地会发生故障，而且通常无法自主恢复。我们演示了一种通过使用自然语言向人类伙伴传达其对特定帮助的需求，使机器人能够从故障中恢复的方法。我们的方法会自动检测故障，然后生成有针对性的口语帮助请求，例如“请给我黑色桌子上的白色桌腿。”一旦人类伙伴修复了故障情况，系统就会恢复完全自主。我们提出了一种新颖的逆语义算法来生成有效的帮助请求。与根据机器人动作和感知来解释自然语言的前向语义模型相比，我们的逆向语义算法通过使用广义基础图 ($$hbox {G}^{3}$$G3) 框架模拟人类解释请求的能力来生成请求。为了评估我们方法的有效性，我们提出了基于语料库的在线评估以及端到端用户研究，证明与静态请求帮助相比，我们的方法提高了人工干预的有效性。
Robots inevitably fail, often without the ability to recover autonomously. We demonstrate an approach for enabling a robot to recover from failures by communicating its need for specific help to a human partner using natural language. Our approach automatically detects failures, then generates targeted spoken-language requests for help such as “Please give me the white table leg that is on the black table.” Once the human partner has repaired the failure condition, the system resumes full autonomy. We present a novel inverse semantics algorithm for generating effective help requests. In contrast to forward semantic models that interpret natural language in terms of robot actions and perception, our inverse semantics algorithm generates requests by emulating the human’s ability to interpret a request using the Generalized Grounding Graph ($$hbox {G}^{3}$$G3) framework. To assess the effectiveness of our approach, we present a corpus-based online evaluation, as well as an end-to-end user study, demonstrating that our approach increases the effectiveness of human interventions compared to static requests for help.