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Datasets:
Joshua69
/
VitacDreamer

Tasks:
Robotics
Image-to-Image
Modalities:
Image
Formats:
imagefolder
Size:
< 1K
Tags:
vitacdreamer
univtac
tactile
visual-tactile
act
robotics
Libraries:
Datasets
License:
Dataset card Data Studio Files
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YAML Metadata Warning:The task_categories "imitation-learning" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

ViTacDreamer Lift Bottle Assets

This dataset repository contains the processed assets used for the lift_bottle/default-50 ViTacDreamer, ACT baseline, and ViTacACT experiments.

Contents

  • data/act_processed/default-50/: processed ACT training episodes generated from UniVTAC lift_bottle/clean.
  • data/act_processed/default-50/vitacdreamer_features_stage2/: cached frozen ViTacDreamer features for accelerated ViTacACT training.
  • checkpoints/vitacdreamer/lift_bottle_stage1/: ViTacDreamer Stage 1 visual-tactile contrastive checkpoint.
  • checkpoints/vitacdreamer/lift_bottle_stage2/: ViTacDreamer Stage 2 cVAE reconstruction checkpoint.
  • checkpoints/act_lift_bottle/default-50/train_config/: ACT baseline checkpoints.
  • checkpoints/act_lift_bottle/default-50/train_config_vitacdreamer/: earlier ViTacACT intermediate checkpoints.
  • checkpoints/act_lift_bottle/default-50/train_config_vitacdreamer_cached/: final cached ViTacACT checkpoints.
  • configs/: release-ready ACT, ViTacACT, and cached ViTacACT training configs with dataset-local paths.
  • scripts/precompute_vitacdreamer_features.py: script used to precompute ViTacDreamer features for cached ViTacACT training.
  • scripts/upload_to_hf.py: resumable uploader for pushing this folder to a Hugging Face dataset repository.

Key Results

  • ACT baseline best validation loss: 0.107912.
  • Cached ViTacACT best validation loss: 0.093634 at epoch 78.

Release Scope

  • Processed ACT dataset episodes: 50
  • Cached ViTacDreamer feature files: 50
  • ViTacDreamer checkpoints: Stage 1 and Stage 2 best checkpoints
  • Policy checkpoints: ACT baseline, online ViTacACT intermediate checkpoints, cached ViTacACT best/last checkpoints

Usage Notes

  • The configs in configs/ assume you run training or evaluation commands from the dataset root.
  • tactile_ckpt is set to null in the release configs because the original private tactile encoder checkpoint is not included in this dataset release.
  • Cached ViTacACT training uses data/act_processed/default-50/vitacdreamer_features_stage2/ and does not need to recompute frozen ViTacDreamer features.
  • If you want to regenerate cached features, use scripts/precompute_vitacdreamer_features.py together with the released Stage 2 checkpoint.

Notes

The cached ViTacACT setup precomputes ViTacDreamer features first, then trains ACT using cached feature tokens to avoid recomputing the frozen ViTacDreamer encoder at every policy-training step.

The assets are intended for research use and reproducibility of the lift_bottle/default-50 experiments.
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