基于双记忆递归自组织网络的终身三维物体识别与抓取合成
Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networks
摘要：
人类在终身的环境中学习识别和操纵新物体，而不会忘记在非平稳和连续条件下先前获得的知识。在自治系统中，智能体还需要减轻类似的行为，以不断学习新的对象类别并适应新的环境。在大多数传统的深度神经网络中，由于灾难性遗忘的问题，这是不可能的，新获得的知识会覆盖现有的表示。此外，大多数最先进的模型在识别物体或抓握预测方面都很出色，而这两项任务都使用视觉输入。处理这两项任务的组合架构非常有限。在本文中，我们提出了一种混合模型架构，包括一个动态增长的双记忆递归神经网络（GDM）和一个自动编码器，以解决对象识别和抓取同时进行。自动编码器网络负责为给定的对象提取一个紧凑的表示，作为GDM学习的输入，并负责预测逐像素对跖抓取配置。GDM部分被设计为在实例和类别级别上识别对象。我们使用内在记忆重放来解决灾难性遗忘的问题，其中情景记忆在没有外部感觉信息的情况下周期性地重放神经激活轨迹。为了在终身环境中广泛评估所提出的模型，由于缺乏连续的3D对象数据集，我们生成了一个合成数据集。实验结果表明，该模型可以在持续学习的场景中同时学习物体表征和抓取。
英文摘要：
Humans learn to recognize and manipulate new objects in lifelong settings without forgetting the previously gained knowledge under non-stationary and sequential conditions. In autonomous systems, the agents also need to mitigate similar behavior to continually learn the new object categories and adapt to new environments. In most conventional deep neural networks, this is not possible due to the problem of catastrophic forgetting, where the newly gained knowledge overwrites existing representations. Furthermore, most state-of-the-art models excel either in recognizing the objects or in grasp prediction, while both tasks use visual input. The combined architecture to tackle both tasks is very limited. In this paper, we proposed a hybrid model architecture consists of a dynamically growing dual-memory recurrent neural network (GDM) and an autoencoder to tackle object recognition and grasping simultaneously. The autoencoder network is responsible to extract a compact representation for a given object, which serves as input for the GDM learning, and is responsible to predict pixel-wise antipodal grasp configurations. The GDM part is designed to recognize the object in both instances and categories levels. We address the problem of catastrophic forgetting using the intrinsic memory replay, where the episodic memory periodically replays the neural activation trajectories in the absence of external sensory information. To extensively evaluate the proposed model in a lifelong setting, we generate a synthetic dataset due to lack of sequential 3D objects dataset. Experiment results demonstrated that the proposed model can learn both object representation and grasping simultaneously in continual learning scenarios.
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。
