ACM应用感知TAP(ACM Transactions on Applied Perception)旨在通过发表有助于统一这些领域研究的高质量论文来增强计算机科学与心理学/感知之间的协同作用。该期刊发表跨学科研究,在跨计算机科学和感知心理学的任何主题领域都具有重大而持久的价值。所有论文都必须包含感知和计算机科学两个部分。主题包括但不限于:视觉感知:计算机图形学,科学/数据/信息可视化,数字成像,计算机视觉,立体和3D显示技术。听觉感知:听觉显示和界面,听觉听觉编码,空间声音,语音合成和识别。触觉:触觉渲染,触觉输入和感知。感觉运动知觉:手势输入,身体运动输入。感官感知:感官整合,多模式渲染和交互。 官网地址:http://dblp.uni-trier.de/db/journals/tap/

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We introduce the transport-and-pack(TAP) problem, a frequently encountered instance of real-world packing, and develop a neural optimization solution based on reinforcement learning. Given an initial spatial configuration of boxes, we seek an efficient method to iteratively transport and pack the boxes compactly into a target container. Due to obstruction and accessibility constraints, our problem has to add a new search dimension, i.e., finding an optimal transport sequence, to the already immense search space for packing alone. Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search. In our work, we represent the transport constraints using a precedence graph and train a neural network, coined TAP-Net, using reinforcement learning to reward efficient and stable packing. The network is built on an encoder-decoder architecture, where the encoder employs convolution layers to encode the box geometry and precedence graph and the decoder is a recurrent neural network (RNN) which inputs the current encoder output, as well as the current box packing state of the target container, and outputs the next box to pack, as well as its orientation. We train our network on randomly generated initial box configurations, without supervision, via policy gradients to learn optimal TAP policies to maximize packing efficiency and stability. We demonstrate the performance of TAP-Net on a variety of examples, evaluating the network through ablation studies and comparisons to baselines and alternative network designs. We also show that our network generalizes well to larger problem instances, when trained on small-sized inputs.

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