Update learning_note.md

learning_note.md

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 ## Learning note
 ### Main insight
-- The model learns the residuals (velocity, gradient of the denoising) if possible. This greatly stabilizes the training.
+- The model should learn the residuals (velocity, gradient of the denoising) if possible. This greatly stabilizes the training.
+- Advantages of diffusion model: 1) capability of modeling multi-modality, 2) stable training, and 3) temporally output consistency.
 ### Scribbles
 - The trained policy does not 100% reach the goal without collision (there is no collision in its training data).
   - Unable to recover from OOD data.
@@ -11,9 +12,11 @@
   - For instance, it collides more on obstacles with a maximum radius of 1.5.
   - Collect more data to make everything interpolations instead of extrapolations.
 - Even though the loss curve appears saturated, the performance of the controller can still improve as training continues.
-  - The training loss curves of the diffusion model are extremely smooth btw.
-- DDPM and DDIM samplers yield the best.
+- The training loss curves of the diffusion model are extremely smooth btw.
+  - On the contrary, it might be difficult to know if the model is overfitting or not by looking at the trajectory as well as the the denoising process.
+- DDPM and DDIM samplers yield the best result.
 - Inference is not in real-time. The controller is set to sun 100Hz.
 
 ### Things that didn't work
-- Tried encoding distances to each obstacle. Did not observe improvement in terms of collision avoidance.
+- Tried encoding distances to each obstacle. Did not observe improvement in terms of collision avoidance.
+- Tried using vision encoder to replace obstacle encoding. Didn't see performance improvement.