Add references

README.md

@@ -35,6 +35,17 @@ The policy takes 1) the latest N step of observation $o_t$ (position and velocit
 - Add a linear layer before the Mish activation to the condition encoder of `ConditionalResidualBlock1D`. This is to prevent the activation from truncating large negative values from the normalized observation.
 
 
+## References
+Papers
+- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://diffusion-policy.cs.columbia.edu/)
+- [3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations](https://3d-diffusion-policy.github.io/)
+
+Videos and Lectures
+- [How Diffusion Models Work](https://www.deeplearning.ai/short-courses/how-diffusion-models-work/)
+- [[论文速览]Diffusion Policy: Visuomotor Policy Learning via Action Diff.[2303.04137]](https://www.bilibili.com/video/BV1Cu411Y7d7)
+- [[論文導讀]Planning with Diffusion for Flexible Behavior Synthesis解說 (含程式碼)](https://youtu.be/ciCcvWutle4)
+- [6.4210 Fall 2023 Lecture 18: Visuomotor Policies (via Behavior Cloning)](https://youtu.be/i-303tTtEig)
+
 ## Learning note
-Refer to `learning_note.md`.
+Refer to [`learning_note.md`](learning_note.md).