Datasets:

Ethan-pooh
/

pact

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
center: list<item: double>
  child 0, item: double
extents: list<item: double>
  child 0, item: double
scale: list<item: double>
  child 0, item: double
target_pose: list<item: list<item: list<item: double>>>
  child 0, item: list<item: list<item: double>>
      child 0, item: list<item: double>
          child 0, item: double
contact_points_pose: list<item: list<item: list<item: double>>>
  child 0, item: list<item: list<item: double>>
      child 0, item: list<item: double>
          child 0, item: double
transform_matrix: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
functional_matrix: list<item: list<item: list<item: double>>>
  child 0, item: list<item: list<item: double>>
      child 0, item: list<item: double>
          child 0, item: double
orientation_point: list<item: null>
  child 0, item: null
contact_points_group: list<item: list<item: int64>>
  child 0, item: list<item: int64>
      child 0, item: int64
contact_points_mask: list<item: bool>
  child 0, item: bool
contact_points_discription: list<item: string>
  child 0, item: string
target_point_discription: list<item: string>
  child 0, item: string
functional_point_discription: list<item: string>
  child 0, item: string
orientation_point_discription: list<item: string>
  child 0, item: string
stable: bool
to
{'center': List(Value('float64')), 'extents': List(Value('float64')), 'scale': List(Value('float64')), 'target_pose': List(List(List(Value('float64')))), 'contact_points_pose': List(List(List(Value('float64')))), 'transform_matrix': List(List(Value('float64'))), 'functional_matrix': List(List(List(Value('float64')))), 'orientation_point': List(List(Value('float64'))), 'contact_points_group': List(List(Value('int64'))), 'contact_points_mask': List(Value('bool')), 'contact_points_discription': List(Value('string')), 'target_point_discription': List(Value('string')), 'functional_point_discription': List(Value('string')), 'stable': Value('bool')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              center: list<item: double>
                child 0, item: double
              extents: list<item: double>
                child 0, item: double
              scale: list<item: double>
                child 0, item: double
              target_pose: list<item: list<item: list<item: double>>>
                child 0, item: list<item: list<item: double>>
                    child 0, item: list<item: double>
                        child 0, item: double
              contact_points_pose: list<item: list<item: list<item: double>>>
                child 0, item: list<item: list<item: double>>
                    child 0, item: list<item: double>
                        child 0, item: double
              transform_matrix: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              functional_matrix: list<item: list<item: list<item: double>>>
                child 0, item: list<item: list<item: double>>
                    child 0, item: list<item: double>
                        child 0, item: double
              orientation_point: list<item: null>
                child 0, item: null
              contact_points_group: list<item: list<item: int64>>
                child 0, item: list<item: int64>
                    child 0, item: int64
              contact_points_mask: list<item: bool>
                child 0, item: bool
              contact_points_discription: list<item: string>
                child 0, item: string
              target_point_discription: list<item: string>
                child 0, item: string
              functional_point_discription: list<item: string>
                child 0, item: string
              orientation_point_discription: list<item: string>
                child 0, item: string
              stable: bool
              to
              {'center': List(Value('float64')), 'extents': List(Value('float64')), 'scale': List(Value('float64')), 'target_pose': List(List(List(Value('float64')))), 'contact_points_pose': List(List(List(Value('float64')))), 'transform_matrix': List(List(Value('float64'))), 'functional_matrix': List(List(List(Value('float64')))), 'orientation_point': List(List(Value('float64'))), 'contact_points_group': List(List(Value('int64'))), 'contact_points_mask': List(Value('bool')), 'contact_points_discription': List(Value('string')), 'target_point_discription': List(Value('string')), 'functional_point_discription': List(Value('string')), 'stable': Value('bool')}
              because column names don't match

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PACT Data and Checkpoints

This repository hosts the data and pretrained policy checkpoints for PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation.

PACT is a self-evolving post-training framework for aligning pretrained diffusion policies with physical safety constraints in embodied manipulation. It uses self-rollouts and automatically computed physical constraints to distill constraint gradients into diffusion policies, improving safety without requiring demonstrations, task rewards, interventions, or outcome annotations.

Website - GitHub - Paper

Highlights

Self-evolving: Aligns diffusion policies from self-rollouts without demonstrations, interventions, rewards, or outcome annotations.
Efficient optimization: Distills constraint gradients into the policy, providing dense supervision across diffusion timesteps.
Curriculum alignment: Progressively tightens constraints to preserve task competence while improving safety.
Foundation-model compatible: Plugs into diffusion-based policies, including flow policies, VLAs, and WAMs, without architecture modifications.
Theoretical control: Bounds policy shift and supports monotone improvement.
Proven in practice: Improves both task success and safety across simulation and real-world manipulation.

Repository Contents

This Hugging Face dataset repository contains file-based assets used by the official PACT codebase:

Pretrained base policy checkpoints for Diffusion Policy on RoboTwin tasks.
Pre-generated instruction datasets used during PACT post-training.
Shared environment metadata required by the released instruction data.

The released task set includes:

handover_apple
handover_block
pick_diverse_bottles
pick_dual_bottles
place_dual_shoes
pour_water_to_cup
stack_blocks_two

This repository is intended for file-based download and use with the official GitHub codebase. The Hugging Face dataset viewer is not the primary interface for these assets.

Quickstart

Install Git LFS if needed, then download the repository:

git lfs install
git clone https://huggingface.co/datasets/Ethan-pooh/pact

Alternatively, download with the Hugging Face CLI:

huggingface-cli download Ethan-pooh/pact \
  --repo-type dataset \
  --local-dir ./pact_hf

The instruction data is provided as data.tar.gz. Untar it and place the extracted files under the PACT repository:

mkdir -p /path/to/PACT/data
tar -xzf ./pact_hf/data.tar.gz -C /path/to/PACT/data

Please refer to the official PACT GitHub repository for installation, post-training, and evaluation commands.

Expected Directory Layout

After downloading, place the pretrained base policy checkpoints under the PACT repository as follows:

PACT/policy/DP/checkpoints/
├── handover_apple-demo_randomized-200-0/600.ckpt
├── handover_block-demo_randomized-200-0/600.ckpt
├── pick_diverse_bottles-demo_randomized-200-0/600.ckpt
├── pick_dual_bottles-demo_randomized-200-0/600.ckpt
├── place_dual_shoes-demo_randomized-200-0/600.ckpt
├── pour_water_to_cup-demo_randomized-200-0/600.ckpt
└── stack_blocks_two-demo_randomized-200-0/600.ckpt

After untarring data.tar.gz, the pre-generated instruction dataset and shared environment metadata should follow this structure:

PACT/data/
├── data/handover_apple/demo_randomized/instructions
├── data/handover_block/demo_randomized/instructions
├── data/pick_diverse_bottles/demo_randomized/instructions
├── data/pick_dual_bottles/demo_randomized/instructions
├── data/place_dual_shoes/demo_randomized/instructions
├── data/pour_water_to_cup/demo_randomized/instructions
├── data/stack_blocks_two/demo_randomized/instructions
└── env_meta.pkl

Once the files are arranged, evaluate a base policy with:

cd policy/DP
bash eval_dr.sh pick_dual_bottles demo_randomized demo_randomized 200 0 600 0

Run PACT post-training from the repository root with:

CUDA_VISIBLE_DEVICES=0,1,2,3 bash policy/DP/on_policy_distill_multigpu.sh pick_dual_bottles onpolicy_randomized 200 0 14

Intended Uses and Limitations

Intended uses

Research on robot manipulation, diffusion policies, and physical safety alignment.
Reproducing the PACT post-training pipeline on RoboTwin tasks.
Evaluating pretrained base policies and post-trained PACT policies using the official codebase.

Limitations

The checkpoints and instruction data are tied to the released RoboTwin task configurations.
Post-training depends on correct installation of RoboTwin, Diffusion Policy, PACT cost functions, and task assets.
Results may vary with simulator version, asset placement, random seeds, and the implemented physical constraint functions.

Safety and responsible use

These assets are intended for simulation-based research in RoboTwin.
Do not interpret simulated safety improvements as a direct guarantee of real-world robot safety without additional validation.

Changelog

2026-06: Initial release of PACT data and pretrained base policy checkpoints on Hugging Face.

Citation

If you find this dataset, checkpoints, or codebase useful, please cite:

@article{wu2026pact,
  title={PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation},
  author={Wu, Lingxuan and Zhu, Zijian and Wang, Lizhong and Ying, Chengyang and Chen, Huayu and Yang, Xiao and Liu, Fangming and Zhu, Jun},
  journal={arXiv preprint arXiv:2606.08414},
  year={2026}
}

Contact

Project page: https://ethan-iai.github.io/pact/
Paper: https://arxiv.org/abs/2606.08414
GitHub: https://github.com/thu-ml/PACT
Hugging Face: https://huggingface.co/datasets/Ethan-pooh/pact

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Paper for Ethan-pooh/pact

PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation

Paper • 2606.08414 • Published 4 days ago