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Datasets:
Amazon-FAR
/
far-prism-data

Tasks:
Robotics
Size:
n<1K
ArXiv:
Tags:
humanoid
loco-manipulation
real-to-sim
human-object-interaction
counterfactual-video
video-to-video
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
clip_id: string
clip_index: int64
object_name: string
object_urdf_path: string
batch_index: int64
env_id: int64
primitive_extents_xyz: list<item: double>
  child 0, item: double
contact_surface_projection: string
num_steps: int64
motion_end_reached: bool
terminated: bool
timeout: bool
success: bool
stable_contact_success: bool
final_position_success: bool
no_middle_foot_object_contact_success: bool
status: string
final_object_position_error_m: double
final_object_rotation_error_rad: double
foot_object_contact_frames: int64
middle_foot_object_contact_frames: int64
max_foot_object_contact_force: double
max_middle_foot_object_contact_force: double
min_contact_frames: int64
contact_force_threshold: double
foot_object_contact_force_threshold: double
middle_foot_contact_start_frac: double
middle_foot_contact_end_frac: double
require_final_position_success_for_success: bool
require_no_middle_foot_object_contact_for_success: bool
contact_voxel_size: double
success_position_threshold: double
teacher_rollout_reference_path: string
contact_intervals_path: string
teacher_rollout_ref_body_name: string
teacher_rollout_tracked_body_names: list<item: string>
  child 0, item: string
teacher_rollout_valid_step_count: int64
teacher_rollout_motion_bank_path: string
teacher_rollout_motion_valid_step_count: int64
left_wrist_roll: list<item: int64>
  child 0, item: int64
right_wrist_pitch: list<item: int64>
  child 0, item: int64
left_wrist_pitch: list<item: int64>
  child 0, item: int64
right_elbow: list<item: int64>
  child 0, item: int64
right_wrist: list<item: int64>
  child 0, item: int64
torso: list<item: int64>
  child 0, item: int64
left_elbow: list<item: int64>
  child 0, item: int64
left_wrist: list<item: int64>
  child 0, item: int64
arm: list<item: int64>
  child 0, item: int64
right_wrist_roll: list<item: int64>
  child 0, item: int64
to
{'left_wrist': List(Value('int64')), 'right_wrist': List(Value('int64')), 'torso': List(Value('int64')), 'left_elbow': List(Value('int64')), 'right_elbow': List(Value('int64')), 'left_wrist_roll': List(Value('int64')), 'right_wrist_roll': List(Value('int64')), 'left_wrist_pitch': List(Value('int64')), 'right_wrist_pitch': List(Value('int64')), 'arm': List(Value('int64'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/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/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              clip_id: string
              clip_index: int64
              object_name: string
              object_urdf_path: string
              batch_index: int64
              env_id: int64
              primitive_extents_xyz: list<item: double>
                child 0, item: double
              contact_surface_projection: string
              num_steps: int64
              motion_end_reached: bool
              terminated: bool
              timeout: bool
              success: bool
              stable_contact_success: bool
              final_position_success: bool
              no_middle_foot_object_contact_success: bool
              status: string
              final_object_position_error_m: double
              final_object_rotation_error_rad: double
              foot_object_contact_frames: int64
              middle_foot_object_contact_frames: int64
              max_foot_object_contact_force: double
              max_middle_foot_object_contact_force: double
              min_contact_frames: int64
              contact_force_threshold: double
              foot_object_contact_force_threshold: double
              middle_foot_contact_start_frac: double
              middle_foot_contact_end_frac: double
              require_final_position_success_for_success: bool
              require_no_middle_foot_object_contact_for_success: bool
              contact_voxel_size: double
              success_position_threshold: double
              teacher_rollout_reference_path: string
              contact_intervals_path: string
              teacher_rollout_ref_body_name: string
              teacher_rollout_tracked_body_names: list<item: string>
                child 0, item: string
              teacher_rollout_valid_step_count: int64
              teacher_rollout_motion_bank_path: string
              teacher_rollout_motion_valid_step_count: int64
              left_wrist_roll: list<item: int64>
                child 0, item: int64
              right_wrist_pitch: list<item: int64>
                child 0, item: int64
              left_wrist_pitch: list<item: int64>
                child 0, item: int64
              right_elbow: list<item: int64>
                child 0, item: int64
              right_wrist: list<item: int64>
                child 0, item: int64
              torso: list<item: int64>
                child 0, item: int64
              left_elbow: list<item: int64>
                child 0, item: int64
              left_wrist: list<item: int64>
                child 0, item: int64
              arm: list<item: int64>
                child 0, item: int64
              right_wrist_roll: list<item: int64>
                child 0, item: int64
              to
              {'left_wrist': List(Value('int64')), 'right_wrist': List(Value('int64')), 'torso': List(Value('int64')), 'left_elbow': List(Value('int64')), 'right_elbow': List(Value('int64')), 'left_wrist_roll': List(Value('int64')), 'right_wrist_roll': List(Value('int64')), 'left_wrist_pitch': List(Value('int64')), 'right_wrist_pitch': List(Value('int64')), 'arm': List(Value('int64'))}
              because column names don't match

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PRISM: Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation

Zihan Wang1,2, Zhen Wu1, Pieter Abbeel1,2, Rocky Duan1, Jitendra Malik1,2, Carmelo Sferrazza1, Karen Liu1,3, Guanya Shi1,4, Angjoo Kanazawa1,2

1 Amazon FAR   2 UC Berkeley   3 Stanford University   4 Carnegie Mellon University

Conference on Robot Learning (CoRL 2026)

Project page · Paper · Code · Post

tl;dr: We expand 4 real video demonstrations into diverse training data for humanoid loco-manipulation. A video-to-video model turns a recording of a person picking up a box into counterfactual videos of the same interaction with a bin, barrel or ball. We reconstruct these interactions in 3D, retarget them to a Unitree G1, and train in simulation. The resulting policy runs on the real robot from onboard depth only, with joystick steering, and handles diverse real objects without real-world fine-tuning.

One example per category: counterfactual video (left) and reference replay of the student training motion (right)

Left: generated counterfactual video. Right: reference replay of the student training motion, time-aligned. One example per category (box, bin, barrel, ball); clip ids in previews/preview_clips.txt.

Pipeline

Generated counterfactual video → 3D reconstruction → retargeted motion → learn in simulation → transfer to the real world.

Prompt template used for generation: <VIDEO> In a real-world continuous footage. Preserve the reference video's original background, lighting and camera viewpoint. Replace the box with <CLS>, pick it up and carry with two hands. with <CLS> ∈ {box, bin, barrel, ball}.

Download

The repository contains ~4,900 small files, so a per-file download hits the Hub's API rate limit. Download the single archive instead (317 MB, sha256 eb1fbcd2ef2714218292f9faf98e6cf57ac7f792da0becf89b3ca89d65850f5d):

hf download Amazon-FAR/far-prism-data far-prism-data.tar.gz --repo-type dataset --local-dir .
tar -xzf far-prism-data.tar.gz
cd far-prism-data && sha256sum --check SHA256SUMS --quiet && echo OK

The extracted directory has exactly the layout below. The individual files are kept in the repository for browsing.

What is included

129 clips (box 35, bin 32, barrel 34, ball 28) from the 137 generated clips, keeping the ones whose teacher rollout succeeded. Per-clip index: clips.csv.

data/train-student/
  data/motion_bank/          129 x <clip_id>.npz  (359 frames, 50 fps, robot + object world-frame trajectories, joystick commands)
  data/contact_sidecars/     per-clip hand/arm/torso contact intervals and contact points
  data/robot_assets/         G1 URDF and depth-rendering meshes
  data/checkpoints/          teacher_40000.pt (privileged teacher), box_23000.pt (student initialiser)
  vis/                       129 x <clip_id>.mp4  reference replays (1280x720, 50 fps)
data/raw_video/  data/recon-hoi/  data/train-teacher/     under review, placeholder README only

Clip ids follow prism_cf_<category>_m<seed>_v<variant>.

Usage 

root = "far-prism-data"  # extracted archive, or snapshot_download("Amazon-FAR/far-prism-data", repo_type="dataset")

import csv, numpy as np
clips = list(csv.DictReader(open(f"{root}/clips.csv")))
d = np.load(f"{root}/{clips[0]['student_motion']}")
print(d["joint_pos"].shape, d["object_pos_w"].shape, d["policy_command_xy_yaw"].shape)

Training code: https://github.com/amazon-far/PRISM.

License

This dataset is released under the Apache License 2.0.

Citation 

@article{wang2026counterfactual,
  title={Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation},
  author={Wang, Zihan and Wu, Zhen and Abbeel, Pieter and Duan, Rocky and Malik, Jitendra and Sferrazza, Carmelo and Liu, C. Karen and Shi, Guanya and Kanazawa, Angjoo},
  journal={arXiv preprint arXiv:2609.38172},
  year={2026}
}

Acknowledgements

We thank Chung Min Kim, Arthur Allshire, Hongsuk Choi, Isabella Yu, Junyi Zhang, Jacob Berg, Yen-Jen Wang, Sirui Chen, Charlie Cheng, Jiashun Wang, Siheng Zhao, Youjian Huang, JC Hu, Haochen Wang, Haozhi Qi and Qitao Zhao for their support and valuable feedback.
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Paper for Amazon-FAR/far-prism-data