Equivariant Transporter Network
Equivariant Transporter Network
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DOI：
10.15607/rss.2022.xviii.007
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Hao-zhe Huang;Dian Wang;R. Walters;Robert W. Platt
中科院分区：
文献类型：
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作者：
Hao-zhe Huang;Dian Wang;R. Walters;Robert W. Platt
Transporter Net是最近提出的挑选和放置框架，它能够从极少数专家演示中学习良好的操纵策略。Transporter Net样本效率如此之高的一个关键原因是，该模型将旋转等效方差合并到Pick模块中，即该模型立即将学习到的Pick知识推广到以不同方向呈现的对象。本文提出了一种新版本的传送网，它对拾取和放置方向都是等变的。因此，我们的模型除了一如既往地概括挑选知识外，还立即将地点知识概括到不同的地点方向。归根结底，我们的新模型比基准Transporter Net模型更有样本效率，并实现了更好的挑选和放置成功率。
Transporter Net is a recently proposed framework for pick and place that is able to learn good manipulation policies from a very few expert demonstrations. A key reason why Transporter Net is so sample efficient is that the model incorporates rotational equivariance into the pick module, i.e. the model immediately generalizes learned pick knowledge to objects presented in different orientations. This paper proposes a novel version of Transporter Net that is equivariant to both pick and place orientation. As a result, our model immediately generalizes place knowledge to different place orientations in addition to generalizing pick knowledge as before. Ultimately, our new model is more sample efficient and achieves better pick and place success rates than the baseline Transporter Net model.