The IKEA Furniture Assembly Environment is one of the first benchmarks for
testing and accelerating the automation of complex manipulation tasks. The
environment is designed to advance reinforcement learning from simple toy tasks
to complex tasks requiring both long-term planning and sophisticated low-level
control. Our environment supports over 80 different furniture models, Sawyer
and Baxter robot simulation, and domain randomization. The IKEA Furniture
Assembly Environment is a testbed for methods aiming to solve complex
manipulation tasks. The environment is publicly available at
this https URL

本研究介绍了 IKEA 家具组装环境，旨在测试自动化复杂操作任务的效率。此环境支持 80 多种不同的家具模型，并公开提供，可用于解决复杂操纵任务的方法测试。

长视角复杂操作任务的 IKEA 家具组装环境

IKEA Furniture Assembly Environment for Long-Horizon Complex  Manipulation Tasks

Human pose forecasting is an important problem in computer vision with
applications to human-robot interaction, visual surveillance, and autonomous
driving. Usually, forecasting algorithms use 3D skeleton sequences and are
trained to forecast for a few milliseconds into the future. Long-range
forecasting is challenging due to the difficulty of estimating how long a
person continues an activity. To this end, our contributions are threefold: (i)
we propose a generative framework for poses using variational autoencoders
based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a
pose-based action classifier, which we argue better reflects the subjective
quality of pose forecasts than distance in coordinate space; (iii) last, for
evaluation of the new model, we introduce a 480,000-frame video dataset called
Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling
and disassembling furniture. We demonstrate promising results for our approach
on both Ikea FA and the existing NTU RGB+D dataset.

本研究提出了一种基于深度马尔科夫模型的变分自编码器生成姿势的框架，探讨姿势预测的新方法，并基于一个床具拼装视频数据集进行验证。