GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments
GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments
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GRIP：对抗环境中语义机器人操作的生成鲁棒推理和感知
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
O. C. Jenkins
中科院分区：
文献类型：
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作者：
Xiaotong Chen;R. Chen;Zhiqiang Sui;Zhefan Ye;Yanqi Liu;R. I. Bahar;O. C. Jenkins
最近的进步导致了机器学习系统的激增，这些系统用于帮助人类完成广泛的任务。然而，我们仍然远远没有准确、可靠和资源高效地运行这些系统。在机器人感知方面，用于目标检测和位姿估计的卷积神经网络(CNN)近年来得到了广泛的应用。然而，众所周知，神经网络在训练过程中存在过度适应问题，并且在不可预见的条件下不那么健壮(这使得它们特别容易受到对抗性情景的影响)。在这项工作中，我们提出了产生式稳健推理和感知(GRIP)作为一个两阶段的目标检测和姿态估计系统，旨在结合区分CNN和产生式推理方法的相对优势来实现稳健估计。我们的结果表明，第二阶段的基于样本的产生式推理能够从CNN的错误目标检测中恢复，并在对抗性条件下产生稳健的估计。我们通过与最先进的基于学习的姿势估计器和在黑暗和杂乱环境中的拾取和放置操作进行比较，证明了握力稳健性的有效性。
Recent advancements have led to a proliferation of machine learning systems used to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and resource-efficient operations of these systems. For robot perception, convolutional neural networks (CNNs) for object detection and pose estimation are recently coming into widespread use. However, neural networks are known to suffer from overfitting during the training process and are less robust under unforeseen conditions (which makes them especially vulnerable to adversarial scenarios). In this work, we propose Generative Robust Inference and Perception (GRIP) as a two-stage object detection and pose estimation system that aims to combine the relative strengths of discriminative CNNs and generative inference methods to achieve robust estimation. Our results show that a second stage of sample-based generative inference is able to recover from false object detections by CNNs, and produce robust estimations in adversarial conditions. We demonstrate the efficacy of GRIP robustness through comparison with state-of-the-art learning-based pose estimators and pick-and-place manipulation in dark and cluttered environments.
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无需几何对象模型即可拾取和放置
DOI：
10.1109/icra.2018.8460553
发表时间：
2018
期刊：
Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA
影响因子：
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作者：
Gualtieri, Marcus;Pas, Andreas ten;Platt, Robert
通讯作者：
Platt, Robert
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DOI：
10.1109/humanoids.2016.7803404
发表时间：
2016
期刊：
2016 IEEE-RAS 16th International Conference on
影响因子：
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作者：
Desingh, Karthik;Jenkins, Odest Chadwicke;Reveret, Lionel;Sui, Zhiqiang
通讯作者：
Sui, Zhiqiang
具有随机同余集的鲁棒 6D 物体姿态估计
DOI：
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发表时间：
2018
期刊：
Proceedings of The British Machine Vision Conference (BMVC
影响因子：
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作者：
Mitash, Chaitanya;Boularias, Abdeslam;Bekris, Kostas
通讯作者：
Bekris, Kostas
DOI：
10.1109/icra.2018.8460538
发表时间：
2017-04
期刊：
2018 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
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作者：
Zhen Zeng;Zheming Zhou;Zhiqiang Sui;O. C. Jenkins
通讯作者：
Zhen Zeng;Zheming Zhou;Zhiqiang Sui;O. C. Jenkins