oxe-auge2025cc-by-4.0
language_table_train_115000_120000_augmented
Augmented robot manipulation dataset with multi-embodiment visual and proprioceptive observations paired with natural language instructions, created through cross-embodiment augmentation of the Language Table dataset.
Downloads168
Episodes5000
Why This Matters for Physical AI
This dataset enables cross-embodiment policy learning by augmenting real robot trajectories across multiple platforms with aligned visual and proprioceptive observations, advancing scalable multi-robot learning from language instructions.
Technical Profile
- Modalities
- rgbproprioceptionlanguage
- Robot Embodiments
- google_robotjacokinova3kuka_iiwapandasawyerur5e
- Action Space
- joint_positions
- Environment
- lab
- Task Types
- manipulationlanguage-guided
- Episodes
- 5000
- Data Format
- parquet
- Annotation Types
- language_instructions
- License
- cc-by-4.0
Community Signals
Top 50% by downloads
Academic Citations20
- Beyond performance: Explaining generalisation failures of Robotic Foundation Models in industrial simulation2025 · Biomimetic Intelligence and Robotics
- Empowering natural human–robot collaboration through multimodal language models and spatial intelligence: Pathways and perspectives2025 · Robotics and Computer-Integrated Manufacturing
- Deep networks for few-shot manipulation learning from scratch2025 · Robotics and Autonomous Systems
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