| --- |
| license: mit |
| pretty_name: Future Sight PushT |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - world-modeling |
| - pusht |
| - tfrecord |
| - video |
| --- |
| |
| # Future Sight PushT |
|
|
| Future Sight PushT is a wind tunnel for studying how training-data scale and |
| composition affect world-model learning and generalization. It pairs large |
| passively collected corpora with smaller, iterative active-exploration corpora |
| in a common PushT environment. This controlled setting helps isolate those |
| effects from the scale and potential contamination of large, heterogeneous |
| datasets. |
|
|
| ## Collection preview |
|
|
| ### Passive collection |
|
|
| <video |
| src="https://huggingface.co/datasets/smbml/future-sight-pusht/resolve/main/assets/passive-preview.mp4" |
| poster="https://huggingface.co/datasets/smbml/future-sight-pusht/resolve/main/assets/passive-preview.png" |
| width="100%" |
| controls muted autoplay loop playsinline preload="metadata"> |
| </video> |
|
|
| Expert PPO, perturbation and recovery, random walk, and random actions: four |
| ways to collect large training corpora with different behavior distributions. |
|
|
| ### Active exploration |
|
|
| <video |
| src="https://huggingface.co/datasets/smbml/future-sight-pusht/resolve/main/assets/active-preview.mp4" |
| poster="https://huggingface.co/datasets/smbml/future-sight-pusht/resolve/main/assets/active-preview.png" |
| width="100%" |
| controls muted autoplay loop playsinline preload="metadata"> |
| </video> |
|
|
| AIM, DADS, DIAYN, ICM, and METRA: smaller collections produced by |
| task-specific exploration methods. The selected AIM episode is from iteration 1; |
| DADS, DIAYN, ICM, and METRA are from iteration 4. |
|
|
| Both videos show selected recorded trajectories, up to 100 frames each. |
| Playback is 10 display frames/s; short episodes repeat, with the frame counter |
| marking each restart. This display rate |
| does not specify collection frequency. These clips illustrate individual |
| trajectories, not aggregate performance or coverage. |
|
|
| Still figures at the 5-second display point: [passive PNG](assets/passive-figure.png) · [PDF](assets/passive-figure.pdf); |
| [active PNG](assets/active-figure.png) · [PDF](assets/active-figure.pdf). |
|
|
| ## Dataset at a glance |
|
|
| | | Passive training | Active training | Evaluation | |
| | --- | ---: | ---: | ---: | |
| | Episodes | ≈1.21 million | 17,000 | 400 | |
| | Compressed size | 486.249 GB | 8.901 GB | 0.239 GB | |
|
|
| Active episode counts include all five collection iterations and both AIM |
| ablations. Each evaluation collection contains 100 episodes. Passive counts are |
| rounded; all sizes are compressed decimal GB. |
|
|
| The previewed training corpora use 144×144 RGB PNG observations and |
| six-dimensional continuous actions and a single camera view. Trajectories are |
| stored as TFRecords, with JSON corpus metadata and CSV per-trajectory metadata. |
| Evaluation records use the same decoded RGB/action dimensions and additionally |
| include PNG segmentation. |
|
|
| ## Collection guide |
|
|
| Passive collections represent data that can be gathered at scale: |
|
|
| | Collection | Behavior | Episodes | Compressed size | |
| | --- | --- | ---: | ---: | |
| | Expert PPO | Task-directed expert trajectories | ≈307,000 | 56.976 GB | |
| | Recovery | Policy rollouts with perturbations and recovery | ≈300,000 | 118.956 GB | |
| | Random walk | Temporally correlated random actions | ≈300,000 | 156.312 GB | |
| | Uniform random | Uniform random actions | ≈300,000 | 154.005 GB | |
|
|
| Active collections represent smaller, task-specific exploration rounds. Each |
| method has five corpora, `iter-0` through `iter-4`, and each iteration has eight |
| shards. |
|
|
| | Method | Role | Episodes, all five iterations | Compressed size | |
| | --- | --- | ---: | ---: | |
| | AIM | Action Influence Maximization | 5,000 | 2.667 GB | |
| | AIM no-MI | AIM mutual-information ablation | 5,000 | 2.617 GB | |
| | AIM no-SN | AIM spectral-normalization ablation | 5,000 | 2.631 GB | |
| | DADS | Dynamics-Aware Discovery of Skills | 500 | 0.260 GB | |
| | DIAYN | Diversity Is All You Need | 500 | 0.223 GB | |
| | ICM | Intrinsic Curiosity Module | 500 | 0.244 GB | |
| | METRA | Metric-aware abstraction | 500 | 0.260 GB | |
|
|
| Evaluation data is separate from training data: |
|
|
| | Corpus | Behavior | Episodes | |
| | --- | --- | ---: | |
| | BC | Behavior-cloned policy rollouts | 100 | |
| | BC failures | Alternating expert and push-away controller segments | 100 | |
| | PushAway | Push-away controller rollouts | 100 | |
| | Interaction | Human teleoperation with simulator segmentation masks | 100 | |
|
|
| ## Files and download |
|
|
| ```text |
| passive-collection/<ppo|recovery|random-walk|uniform-random>/train/shard_N.tar.zst |
| active-exploration/<method>/iter-N/shard_N.tar.zst |
| evaluation/<bc|bc-failures|pushaway|interaction>/shard_0.tar.zst |
| manifest.json |
| ``` |
|
|
| [`manifest.json`](manifest.json) records every archive path, byte size, and |
| SHA-256 checksum. Sizes above are compressed decimal GB; extraction requires |
| additional disk space. |
|
|
| Install the [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/guides/cli), |
| then download only the data needed for an experiment. For one archive: |
|
|
| ```sh |
| hf download smbml/future-sight-pusht evaluation/bc/shard_0.tar.zst \ |
| --repo-type dataset --local-dir ./future-sight-pusht |
| ``` |
|
|
| For one complete active-exploration iteration: |
|
|
| ```sh |
| hf download smbml/future-sight-pusht \ |
| --repo-type dataset \ |
| --include "active-exploration/aim/iter-0/*.tar.zst" \ |
| --local-dir ./future-sight-pusht |
| ``` |
|
|
| Add `--revision FULL_COMMIT_HASH` to pin a reproducible snapshot. Download |
| `manifest.json` at the same revision when verifying sizes or checksums. |
|
|
| ## Extract and read |
|
|
| With GNU tar and `zstd` installed: |
|
|
| ```sh |
| mkdir -p ./future-sight-data |
| tar --zstd -xf ./future-sight-pusht/evaluation/bc/shard_0.tar.zst \ |
| -C ./future-sight-data |
| ``` |
|
|
| This creates `future-sight-data/pusht_bc_1pct_n100/` with TFRecords and metadata. |
| Archive paths organize downloads; extracted top-level directory names reflect |
| the corpus stored in each archive. |
|
|
| For sharded training corpora, extract each shard into a separate directory so |
| shard-local metadata files do not overwrite one another. Read the TFRecords and |
| metadata from all eight directories to use a complete iteration or passive |
| corpus. |
|
|
| ## License |
|
|
| MIT. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @inproceedings{bateman2026training, |
| title = {Training Controllable World Models via Action Influence Maximization}, |
| author = {Bateman, Samuel M. and Yin, Tenny and Zheng, Chongyi and |
| Huang, Lei and Wang, Brian and Eysenbach, Ben and |
| Fern{\'a}ndez Fisac, Jaime and Shah, Dhruv}, |
| booktitle = {Conference on Robot Learning (CoRL)}, |
| year = {2026} |
| } |
| ``` |
|
|
| *Paper link coming soon.* |
|
|
