Factorizing Perception and Policy for Interactive Instruction Following
Factorizing Perception and Policy for Interactive Instruction Following
复制标题
分解交互式指令跟随的感知和策略
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Jonghyun Choi
中科院分区：
文献类型：
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作者：
Kunal Pratap Singh;Suvaansh Bhambri;Byeonghwi Kim;Roozbeh Mottaghi;Jonghyun Choi
基于语言指令执行简单的家务对人类来说是非常自然的，但它仍然是人工智能智能的一个开放性挑战。“交互式指令遵循”任务试图朝着在每一步都在环境中联合导航、交互和推理的构建代理取得进展。为了解决多方面的问题，我们提出了一个模型，将任务分解为具有增强组件的交互式感知和行动策略流，并将其命名为莫卡，一种模块化的以对象为中心的方法。我们经验验证，莫卡优于现有技术的显着利润率的ALFRED基准与改进的泛化。
Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents. The ‘interactive instruction following’ task attempts to make progress towards building agents that jointly navigate, interact, and reason in the environment at every step. To address the multifaceted problem, we propose a model that factorizes the task into interactive perception and action policy streams with enhanced components and name it as MOCA, a Modular Object-Centric Approach. We empirically validate that MOCA outperforms prior arts by significant margins on the ALFRED benchmark with improved generalization.
DOI：
10.1109/cvpr.2019.01282
发表时间：
2018-11
期刊：
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
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作者：
Howard Chen;Alane Suhr;Dipendra Kumar Misra;Noah Snavely;Yoav Artzi
通讯作者：
Howard Chen;Alane Suhr;Dipendra Kumar Misra;Noah Snavely;Yoav Artzi
影响因子：
3.4
作者：
Saha, Homagni;Fotouhi, Fateme;Liu, Qisai;Sarkar, Soumik
通讯作者：
Sarkar, Soumik