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Day 1

Work Done

Test Evaluation Script and Environment

Ran the evaluation command from lerobot/diffusion_policy.

python -m lerobot.scripts.eval --policy.path=lerobot/diffusion_pusht --output_dir ./output --env.type=pusht --eval.n_episodes=500 --eval.batch_size=50

And the results:

Mine lerobot/diffusion_pusht Paper
Average max. overlap ratio 0.962 0.955 0.957
Success rate for 500 episodes (%) 64.2 65.4 64.2

Dataset Discovery

I opened up a jupyter notebook playground and fiddled with the data a little bit. Here's the structure of the data with zarr.open_group(path).tree():

/
├── data
│   ├── action (N, 2) float32
│   ├── img (N, 96, 96, 3) float32
│   ├── keypoint (N, 9, 2) float32
│   ├── n_contacts (N, 1) float32
│   └── state (N, 5) float32
└── meta
    └── episode_ends (K,) int64

Initiallly, I compared it to the lerobot/pusht dataset released by HuggingFace. However, the entries are so different that it's difficult to match them. I printed the arrays, displayed the images, trying to get a sense of what those values mean. Here's my attempt:

  • episode_ends marks the ending scene/index of each episode. Use this to split the data into K rounds and label them with episode indices from 0 to K - 1.
  • state: I make my assumptions by simultaneously looking at the corresponding image.
    • The first two numbers are the position of the tooltip.
    • 3rd & 4th are the positions of the T-shaped object.
    • 5th looks like the orientation of the object in radian.
  • img visualizes the current state (potentially given the keypoints and states).

At some point I came up with the idea that I should also check the dataset released by the authors of the original paper. BINGO!

real-stanford/diffusion_policy

Naturally, the next step is to discover the diffusion policy paper and code. Their README suggests running the notebook in colab, but I failed to open it due to some issues. I then turned to the example commands in the README.

I started with the low-dimension setup. Using the exact configuration from the paper, my results (0.944@750 and 0.948@950) seemed to match the authors' checkpoints. However, this is entirely based on the name of the checkpoint. Further investigation is required to determine whether this is a successful reproduction.

WIP

  • Reproducing both image and low-dimension experiments.
  • Naively training on custom v1 dataset with only swapping the dataset itself.

TODO

Focus on real-stanford/diffusion_policy.

  • Look into colab notebooks.
  • Training understanding
    • Policy input/output.
    • How is a diffusion model trained?
    • Hyperparameters.
  • Evaluation understanding:
    • How do validation and test work?
    • Definition of metrics: success rate, reward.
  • PushT environment: compare it to lerobot/gym-pusht.
  • Data preprocessing
    • How image and low-dimension tasks utilize data.
    • Convert to lerobot-style dataset?
  • Setup local wandb?
  • Code cleanup and commit.

Random Notes

AttributeError: 'Space' object has no attribute 'add_collision_handler'

Looks like pymunk removed the method after version 7.0. Running uv add 'pymunk<7' solves the issue.

Environment Preparation for diffusion_policy

Setting up the environment wasn't the easiest. This is a two-year-old project. Huggingface libraries have been moving fast and not afraid of breaking things. Python's dependency management via conda and pip isn't the best1. All three factors lead to hours of fixing module/attribute not found errors and nonexistence of valid version combinations. Eventually, I had a fragile but working environment. Time for running some code!

  1. Let's hope for uv! ↩