Tensor and Regularization Methods for (Semantic) Deep Learning: Application to Robotic Perception

（语义）深度学习的张量和正则化方法：在机器人感知中的应用

• 批准号: RGPIN-2018-06134
• 负责人: Chaibdraa, Brahim
• 金额: $ 2.48万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=647050
• 关键词: Tensor; Regularization; Methods; Semantic; Deep

Robots have been used for industry and daily routines and they are becoming more and more indispensable for the economy, support and help, games, etc.; nowadays, they are becoming ubiquitous in our societies. During the last decade, deep learning (DL) has pushed robotic systems further and particularly the perception aspects as vision, tactile perception, hearing, grasping, etc. There remains however much to do in this context if we aim to have as a long-term objective a robot with a perception process able to organize, recognize, identify, and interpret percepts in order to represent and understand the environment. To this end, we have identified pressing and important issues that we want to achieve as short-term objectives. More specifically, we aim to contribute to: 1) tensors for deep learning; 2) regularization and optimization for deep learning; 3) semantic deep learning; and apply all these deep learning advancements to 4) robotic perception.*** Investigating tensors for deep learning allows reducing the number of parameters of a deep neural network while quantifying the ability to approximate or learn wide classes of unknown nonlinear functions. Studying the optimization as implicit regularization allows a better generalization while accelerating the learning process. Finally, linking the deep learning to domain model generally reflected by ontologies allows checking inconsistency or consistency, giving semantic explanation, reasoning with commonsense and even extending the learning process to multi-task and multi-domain adaptation. *** We intend to apply our advancements in deep learning to robotic perception. To this end, we aim at: a) developing a visual localization for a long-term autonomy and b) investigating new methods for grasping and tactile perception. Experiments in this context will be conducted on real data and robots. For datasets, we will regularly gather data in Quebec City, over the four seasons to test our visual localization methods and apply them to our robots. For grasping, we will test our algorithms with real robotic arms on many existing datasets and then test them on real objects. We will also apply tactile perception to learning terrain types and see how our robots, equipped with specific sensors, can perform it. ***We expect to make significant scientific contributions in the form of novel applications of advanced machine learning methods and their application to robotic perception. We also expect that our research results will transfer directly to the industry. Finally, we will train 3 Undergraduates, 2 Master's and 4 PhDs with skills and knowledge that will benefit the Canadian industry.***

机器人已被用于工业和日常工作，它们在经济、支持和帮助、游戏等方面变得越来越不可或缺;如今，它们在我们的社会中变得无处不在。 在过去的十年中，深度学习（DL）进一步推动了机器人系统，特别是视觉，触觉感知，听觉，抓握等感知方面，但是如果我们的目标是作为一个长期目标，机器人的感知过程能够组织，识别，识别和解释感知，以表示和理解环境，那么在这种情况下还有很多工作要做。为此，我们确定了我们希望实现的紧迫而重要的问题作为短期目标。更具体地说，我们的目标是：1）深度学习的张量; 2）深度学习的正则化和优化; 3）语义深度学习;并将所有这些深度学习的进步应用于4）机器人感知。研究用于深度学习的张量可以减少深度神经网络的参数数量，同时量化近似或学习广泛类别的未知非线性函数的能力。将优化作为隐式正则化进行研究可以在加速学习过程的同时实现更好的泛化。最后，将深度学习与本体所反映的领域模型联系起来，可以检查不一致性或一致性，给出语义解释，用常识进行推理，甚至将学习过程扩展到多任务和多领域适应。* 我们打算将我们在深度学习方面的进步应用于机器人感知。为此，我们的目标是：a）开发长期自主的视觉定位和B）研究抓握和触觉感知的新方法。这方面的实验将在真实的数据和机器人上进行。对于数据集，我们将定期在魁北克市收集四季的数据，以测试我们的视觉定位方法并将其应用于我们的机器人。对于抓取，我们将在许多现有数据集上使用真实的机器人手臂测试我们的算法，然后在真实的物体上测试它们。我们还将应用触觉感知来学习地形类型，并了解我们配备特定传感器的机器人如何执行它。* 我们希望以先进机器学习方法的新应用及其在机器人感知中的应用的形式做出重大的科学贡献。我们还希望我们的研究成果能够直接转移到行业中。最后，我们将培养3名本科生，2名硕士和4名博士，他们的技能和知识将使加拿大工业受益。