add equidiff

content/publication/dian_equidiff/cite.bib

@@ -0,0 +1,8 @@
+@inproceedings{
+wang2024equivariant,
+title={Equivariant Diffusion Policy},
+author={Dian Wang and Stephen Hart and David Surovik and Tarik Kelestemur and Haojie Huang and Haibo Zhao and Mark Yeatman and Jiuguang Wang and Robin Walters and Robert Platt},
+booktitle={8th Annual Conference on Robot Learning},
+year={2024},
+url={https://openreview.net/forum?id=wD2kUVLT1g}
+}

content/publication/dian_equidiff/index.md

@@ -0,0 +1,69 @@
+---
+title: "Equivariant Diffusion Policy"
+authors:
+- Dian Wang
+- Stephen Hart
+- David Surovik
+- Tarik Kelestemur
+- Haojie Huang
+- Haibo Zhao
+- Mark Yeatman
+- Jiuguang Wang
+- Robin Walters
+- Robert Platt
+date: "2024-11-09T00:00:00Z"
+doi: ""
+
+# Schedule page publish date (NOT publication's date).
+publishDate: "2022-03-02T00:00:00Z"
+# Publication type.
+# Legend: 0 = Uncategorized; 1 = Conference paper; 2 = Journal article;
+# 3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;
+# 7 = Thesis; 8 = Patent
+publication_types: ["1"]
+
+# Publication name and optional abbreviated publication name.
+publication: In *Conference on Robot Learning 2024*
+publication_short: In *CoRL 2024*
+
+abstract: Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning. However, a drawback of this approach is the need to learn a denoising function, which is significantly more complex than learning an explicit policy. In this work, we propose Equivariant Diffusion Policy, a novel diffusion policy learning method that leverages domain symmetries to obtain better sample efficiency and generalization in the denoising function. We theoretically analyze the SO(2) symmetry of full 6-DoF control and characterize when a diffusion model is SO(2)-equivariant. We furthermore evaluate the method empirically on a set of 12 simulation tasks in MimicGen, and show that it obtains a success rate that is, on average, 21.9% higher than the baseline Diffusion Policy. We also evaluate the method on a real-world system to show that effective policies can be learned with relatively few training samples, whereas the baseline Diffusion Policy cannot.
+
+# Summary. An optional shortened abstract.
+summary: 
+
+tags:
+- Source Themes
+featured: true
+
+links:
+- name: Website
+  url: https://pointw.github.io/equidiff_page/
+url_pdf: https://arxiv.org/pdf/2407.01812
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder. 
+image:
+  caption: 'Image credit: [**Unsplash**](https://unsplash.com/photos/pLCdAaMFLTE)'
+  focal_point: ""
+  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+projects:
+- internal-project
+
+# Slides (optional).
+#   Associate this publication with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+slides:
+---
+
+
+<!-- Markdown & HTML begins here  -->
+
+<meta http-equiv = "refresh" content = " 0 ; url = https://arxiv.org/pdf/2407.01812"/>