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For example, if the pick object is rotated or translated, then the optimal pick action should also rotate or translate. The same is true for the place pose; if the desired place pose changes, then the place action should also transform accordingly. A recently proposed pick and place framework known as Transporter Net (Zeng, Florence, Tompson, Welker, Chien, Attarian, Armstrong, Krasin, Duong, Sindhwani et al., 2021) captures some of these symmetries, but not all. This paper analytically studies the symmetries present in planar robotic pick and place and proposes a method of incorporating equivariant neural models into Transporter Net in a way that captures all symmetries. The new model, which we call Equivariant Transporter Net, is equivariant to both pick and place symmetries and can immediately generalize pick and place knowledge to different pick and place poses. We evaluate the new model empirically and show that it is much more sample-efficient than the non-symmetric version, resulting in a system that can imitate demonstrated pick and place behavior using very few human demonstrations on a variety of imitation learning tasks.<\/jats:p>","DOI":"10.1177\/02783649231225775","type":"journal-article","created":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T01:41:48Z","timestamp":1704505308000},"page":"550-571","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Leveraging symmetries in pick and place"],"prefix":"10.1177","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8737-7959","authenticated-orcid":false,"given":"Haojie","family":"Huang","sequence":"first","affiliation":[{"name":"Khoury College of Computer Science, Northeastern University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dian","family":"Wang","sequence":"additional","affiliation":[{"name":"Khoury College of Computer Science, Northeastern University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arsh","family":"Tangri","sequence":"additional","affiliation":[{"name":"Khoury College of Computer Science, Northeastern University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robin","family":"Walters","sequence":"additional","affiliation":[{"name":"Khoury College of Computer Science, Northeastern University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Platt","sequence":"additional","affiliation":[{"name":"Khoury College of Computer Science, Northeastern University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,1,5]]},"reference":[{"key":"bibr1-02783649231225775","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3003865"},{"key":"bibr2-02783649231225775","doi-asserted-by":"publisher","DOI":"10.1117\/12.57955"},{"key":"bibr3-02783649231225775","unstructured":"Bishop CM (1994) Mixture density networks."},{"key":"bibr4-02783649231225775","volume-title":"International Conference on Learning Representations","author":"Cesa G","year":"2021"},{"key":"bibr5-02783649231225775","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967983"},{"key":"bibr6-02783649231225775","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01428"},{"key":"bibr7-02783649231225775","first-page":"2990","volume-title":"International Conference on Machine Learning","author":"Cohen T","year":"2016"},{"key":"bibr8-02783649231225775","volume-title":"International Conference on Learning Representations","author":"Cohen TS","year":"2017"},{"key":"bibr9-02783649231225775","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812057"},{"key":"bibr10-02783649231225775","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196714"},{"key":"bibr11-02783649231225775","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01198"},{"key":"bibr12-02783649231225775","unstructured":"Devin C, Rowghanian P, Vigorito C, et al. 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