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DragMesh-2

Reinforcement Learning
PyTorch
robotics
manipulation
hand-object-interaction
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  1. .gitattributes +0 -34
  2. README.md +110 -0
  3. checkpoints/main_table/03_state_only_ppo/object_12583/handle_1/policy.pth +3 -0
  4. checkpoints/main_table/03_state_only_ppo/object_45261/handle_7/policy.pth +3 -0
  5. checkpoints/main_table/03_state_only_ppo/object_45661/handle_3/policy.pth +3 -0
  6. checkpoints/main_table/03_state_only_ppo/object_46440/handle_5/policy.pth +3 -0
  7. checkpoints/main_table/03_state_only_ppo/object_7310/handle_1/policy.pth +3 -0
  8. checkpoints/main_table/04_flat_history_ppo/object_12583/handle_1/policy.pth +3 -0
  9. checkpoints/main_table/04_flat_history_ppo/object_45261/handle_7/policy.pth +3 -0
  10. checkpoints/main_table/04_flat_history_ppo/object_45661/handle_3/policy.pth +3 -0
  11. checkpoints/main_table/04_flat_history_ppo/object_45936/handle_1/policy.pth +3 -0
  12. checkpoints/main_table/04_flat_history_ppo/object_46440/handle_5/policy.pth +3 -0
  13. checkpoints/main_table/04_flat_history_ppo/object_48513/handle_2/policy.pth +3 -0
  14. checkpoints/main_table/04_flat_history_ppo/object_7310/handle_1/policy.pth +3 -0
  15. checkpoints/main_table/05_gla_no_aux/object_12583/handle_1/policy.pth +3 -0
  16. checkpoints/main_table/05_gla_no_aux/object_45261/handle_7/policy.pth +3 -0
  17. checkpoints/main_table/05_gla_no_aux/object_45661/handle_3/policy.pth +3 -0
  18. checkpoints/main_table/05_gla_no_aux/object_45936/handle_1/policy.pth +3 -0
  19. checkpoints/main_table/05_gla_no_aux/object_46440/handle_5/policy.pth +3 -0
  20. checkpoints/main_table/05_gla_no_aux/object_48513/handle_2/policy.pth +3 -0
  21. checkpoints/main_table/05_gla_no_aux/object_7310/handle_1/policy.pth +3 -0
  22. checkpoints/main_table/06_pica_no_gla_aux_v2c/object_12583/handle_1/policy.pth +3 -0
  23. checkpoints/main_table/06_pica_no_gla_aux_v2c/object_45261/handle_7/policy.pth +3 -0
  24. checkpoints/main_table/06_pica_no_gla_aux_v2c/object_45661/handle_3/policy.pth +3 -0
  25. checkpoints/main_table/06_pica_no_gla_aux_v2c/object_45936/handle_1/policy.pth +3 -0
  26. checkpoints/main_table/06_pica_no_gla_aux_v2c/object_46440/handle_5/policy.pth +3 -0
  27. checkpoints/main_table/06_pica_no_gla_aux_v2c/object_48513/handle_2/policy.pth +3 -0
  28. checkpoints/main_table/06_pica_no_gla_aux_v2c/object_7310/handle_1/policy.pth +3 -0
  29. checkpoints/main_table/07_pica_ours/object_12583/handle_1/policy.pth +3 -0
  30. checkpoints/main_table/07_pica_ours/object_45261/handle_7/policy.pth +3 -0
  31. checkpoints/main_table/07_pica_ours/object_45661/handle_3/policy.pth +3 -0
  32. checkpoints/main_table/07_pica_ours/object_45936/handle_1/policy.pth +3 -0
  33. checkpoints/main_table/07_pica_ours/object_46440/handle_5/policy.pth +3 -0
  34. checkpoints/main_table/07_pica_ours/object_48513/handle_2/policy.pth +3 -0
  35. checkpoints/main_table/07_pica_ours/object_7310/handle_1/policy.pth +3 -0
  36. checkpoints/main_table/08_gru_ppo/object_12583/handle_1/policy.pth +3 -0
  37. checkpoints/main_table/08_gru_ppo/object_45261/handle_7/policy.pth +3 -0
  38. checkpoints/main_table/08_gru_ppo/object_45661/handle_3/policy.pth +3 -0
  39. checkpoints/main_table/08_gru_ppo/object_45936/handle_1/policy.pth +3 -0
  40. checkpoints/main_table/08_gru_ppo/object_46440/handle_5/policy.pth +3 -0
  41. checkpoints/main_table/08_gru_ppo/object_48513/handle_2/policy.pth +3 -0
  42. checkpoints/main_table/08_gru_ppo/object_7310/handle_1/policy.pth +3 -0
  43. checkpoints/main_table/09_transformer_ppo/object_12583/handle_1/policy.pth +3 -0
  44. checkpoints/main_table/09_transformer_ppo/object_45261/handle_7/policy.pth +3 -0
  45. checkpoints/main_table/09_transformer_ppo/object_45661/handle_3/policy.pth +3 -0
  46. checkpoints/main_table/09_transformer_ppo/object_45936/handle_1/policy.pth +3 -0
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  48. checkpoints/main_table/09_transformer_ppo/object_48513/handle_2/policy.pth +3 -0
  49. checkpoints/main_table/09_transformer_ppo/object_7310/handle_1/policy.pth +3 -0
  50. model_manifest.jsonl +49 -0
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README.md ADDED
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+ ---
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+ library_name: pytorch
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+ tags:
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+ - reinforcement-learning
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+ - robotics
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+ - manipulation
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+ - hand-object-interaction
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+ - pytorch
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+ license: other
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+ ---
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+
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+ # DragMesh-2 Evaluation Checkpoints
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+
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+ This repository contains the PyTorch policy checkpoints used for the
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+ DragMesh-2 main-table evaluation. The release includes seven policy variants
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+ evaluated on seven object-part tasks, for a total of 49 checkpoints.
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+
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+ The corresponding hand-object interaction trajectories are available in the
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+ [DragMesh-2 dataset](https://huggingface.co/datasets/AIGeeksGroup/DragMesh-2).
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+ The referenced objects and actionable parts originate from
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+ [GAPartNet](https://pku-epic.github.io/GAPartNet/); GAPartNet assets are not
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+ included in this model repository.
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+
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+ ## Repository layout
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+
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+ ```text
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+ checkpoints/
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+ main_table/
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+ <experiment>/
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+ object_<object_id>/
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+ handle_<part_id>/
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+ policy.pth
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+ model_manifest.jsonl
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+ ```
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+
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+ Each path explicitly identifies the evaluated experiment, source object, and
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+ manipulated part. `model_manifest.jsonl` records the normalized checkpoint
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+ path, original relative path, object category, file size, and SHA-256 digest.
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+
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+ ## Policy variants
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+
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+ | Experiment | Variant |
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+ | --- | --- |
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+ | `03_state_only_ppo` | State-only PPO baseline |
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+ | `04_flat_history_ppo` | Flat-history PPO baseline |
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+ | `05_gla_no_aux` | GLA without the auxiliary objective |
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+ | `06_pica_no_gla_aux_v2c` | PICA without the GLA auxiliary objective (`v2c`) |
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+ | `07_pica_ours` | DragMesh-2 PICA policy |
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+ | `08_gru_ppo` | GRU PPO baseline |
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+ | `09_transformer_ppo` | Transformer PPO baseline |
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+
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+ The numeric prefixes preserve the main-table experiment numbering used during
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+ evaluation.
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+
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+ ## Evaluation tasks
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+
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+ | Category | Object ID | Part ID |
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+ | --- | --- | --- |
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+ | Dishwasher | `12583` | `handle_1` |
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+ | Microwave | `7310` | `handle_1` |
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+ | StorageFurniture | `45261` | `handle_7` |
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+ | StorageFurniture | `45661` | `handle_3` |
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+ | StorageFurniture | `45936` | `handle_1` |
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+ | StorageFurniture | `46440` | `handle_5` |
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+ | StorageFurniture | `48513` | `handle_2` |
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+
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+ ## Checkpoint format
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+
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+ Each `policy.pth` is a PyTorch training checkpoint with these top-level fields:
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+
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+ - `model`: policy state dictionary;
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+ - `optimizer`: optimizer state;
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+ - `running_mean_std`: observation normalization state;
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+ - `reward_mean_std`: reward normalization state;
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+ - `epoch`, `frame`, and `last_mean_rewards`: training metadata;
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+ - `env_state`: serialized environment state when available.
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+
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+ Load checkpoints only in a trusted environment. With PyTorch 2.6 or later,
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+ the checkpoint can be read in weights-only mode by allowlisting the NumPy
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+ scalar types stored in its training metadata:
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+
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+ ```python
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+ from pathlib import Path
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+
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+ import numpy as np
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+ import torch
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+
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+ checkpoint_path = Path(
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+ "checkpoints/main_table/07_pica_ours/"
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+ "object_45661/handle_3/policy.pth"
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+ )
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+
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+ safe_globals = [
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+ np.core.multiarray.scalar,
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+ np.dtype,
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+ np.dtypes.Float32DType,
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+ ]
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+
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+ with torch.serialization.safe_globals(safe_globals):
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+ checkpoint = torch.load(
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+ checkpoint_path,
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+ map_location="cpu",
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+ weights_only=True,
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+ )
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+
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+ policy_state_dict = checkpoint["model"]
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+ ```
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+
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+ The policy architecture and observation configuration must match the
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+ corresponding DragMesh-2 experiment when restoring a checkpoint.
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