Dataset profile
IPEC-COMMUNITY/droid_lerobot Dataset
IPEC-COMMUNITY/droid_lerobot is an Apache-2.0 licensed robotics dataset containing 92,233 episodes totaling 27,044,326 frames captured from Franka robotic arms at 15 frames per second. Created using the LeRobot framework and structured in the RLDS format, the dataset spans 31,308 distinct tasks with 276,699 video sequences organized into 93 chunks of 1,000 episodes each. Robotics teams training vision-language-action models or manipulation policies can use this large-scale Franka dataset for pretraining or fine-tuning, particularly when commercial deployment under Apache-2.0 is required and embodiment alignment with Franka hardware is critical.
Quick facts
- Scale
- 92,233 episodes / 27M frames
- License
- Apache-2.0
- Format
- RLDS / Parquet
- Robot type
- Franka
- Frame rate
- 15 fps
- Commercial use
- Permitted
Dataset composition and structure
The IPEC-COMMUNITY/droid_lerobot dataset comprises 92,233 episodes captured from Franka robotic arms performing 31,308 unique tasks. Each episode is stored in Parquet format following the RLDS (Reinforcement Learning Datasets) schema, with trajectories organized into 93 chunks of approximately 1,000 episodes each. The dataset was generated using LeRobot version 2.0, ensuring consistent data collection protocols and compatibility with modern robotics learning pipelines. With 27,044,326 total frames sampled at 15 frames per second, the collection provides dense temporal coverage suitable for learning dynamic manipulation behaviors. The 276,699 video sequences enable multimodal training where visual observation is paired with proprioceptive state and action data. All episodes belong to a single train split spanning the full 92,233-episode range, allowing teams to implement custom train-validation partitions based on task taxonomy or temporal windows. The chunked Parquet structure enables efficient streaming and selective loading during distributed training, particularly when working with large-batch VLA pretraining or when memory constraints require careful data pipeline design.
Licensing and commercial deployment
Released under the Apache-2.0 license, this dataset permits commercial use, modification, and redistribution with minimal restrictions beyond attribution and license inclusion. Robotics companies developing proprietary manipulation systems can train models on this data and deploy them in production environments without royalty obligations or copyleft requirements that would force disclosure of derivative works. The Apache-2.0 terms are particularly favorable for startups and enterprise teams that need clear intellectual property boundaries when integrating third-party training data into commercial products. Unlike datasets with non-commercial or research-only licenses, teams can use IPEC-COMMUNITY/droid_lerobot for both R&D prototyping and revenue-generating applications such as warehouse automation, kitchen robotics, or manufacturing quality control. The permissive license also allows combination with other Apache-2.0 or MIT-licensed datasets to create larger pretraining corpora without license compatibility conflicts. Organizations should still perform standard due diligence by reviewing the full license text on the Hugging Face repository and confirming that their use case aligns with Apache-2.0 grant terms, especially if redistributing modified versions or embedding the data in SaaS platforms.
Procurement and integration considerations
Teams sourcing this dataset through Hugging Face can leverage the platform's dataset library for streamlined loading and versioning, with the LeRobot tooling providing native compatibility for trajectory replay and visualization. The 463,776 download count indicates active community adoption, suggesting that integration patterns and bug reports are well-documented in forums and GitHub issues. Because the dataset uses Parquet columnar storage, practitioners can apply predicate pushdown and column pruning to load only relevant fields during training, reducing I/O overhead when experimenting with different observation modalities or action spaces. The chunked structure allows incremental downloads, enabling teams to validate pipeline compatibility on a single chunk before committing storage and bandwidth to the full 27-million-frame collection. For procurement teams evaluating multiple Franka datasets, this collection's scale and task diversity make it suitable as a foundation dataset for pretraining, with domain-specific fine-tuning data layered on top for target applications. Organizations should verify that their training infrastructure can handle the 15fps temporal resolution and assess whether downsampling or frame-skipping is necessary for their model architecture. The RLDS schema compatibility means teams already using Open X-Embodiment or other RLDS-formatted datasets can reuse existing dataloaders with minimal modification, accelerating time-to-first-training-run and reducing engineering overhead.
Known limitations and dataset boundaries
While the dataset provides extensive coverage of Franka manipulation tasks, the repository metadata does not specify the exact sensor modalities captured, requiring teams to inspect sample episodes to confirm whether RGB, depth, proprioceptive state, or force-torque data are included. The absence of detailed task taxonomy documentation means practitioners must perform exploratory analysis to understand the distribution of skills represented across the 31,308 tasks, which may range from simple pick-place to complex multi-step assembly depending on the data collection protocol. Teams targeting non-Franka embodiments should carefully evaluate sim-to-real transfer challenges, as policies trained exclusively on Franka kinematics and dynamics may require significant adaptation for robots with different joint configurations or end-effector designs. The single train split without predefined validation or test partitions places the burden on users to implement stratified sampling strategies that prevent data leakage, especially if tasks or objects repeat across episodes. Because the dataset was created using LeRobot tooling, teams unfamiliar with that framework may face a learning curve when attempting to reproduce data collection procedures or understand episode structure nuances. The 15fps capture rate is lower than some state-of-the-art manipulation datasets that use 30fps or 60fps, potentially limiting the ability to learn high-frequency contact-rich skills or fast dynamic motions where temporal resolution is critical for policy performance.
Related pages
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FAQ
What is the IPEC-COMMUNITY/droid_lerobot dataset?
IPEC-COMMUNITY/droid_lerobot is a large-scale robotics dataset containing 92,233 episodes of Franka robotic arm trajectories performing 31,308 distinct manipulation tasks. Created using the LeRobot framework and released under the Apache-2.0 license, it provides 27,044,326 frames captured at 15 frames per second, organized into 93 chunks for efficient storage and streaming. The dataset follows the RLDS format and includes 276,699 video sequences, making it suitable for training vision-language-action models, behavior cloning policies, and world models that require dense temporal coverage of manipulation behaviors in a permissively licensed package.
Can I use this dataset for commercial robotics products?
Yes, the Apache-2.0 license explicitly permits commercial use, modification, and redistribution of this dataset. Robotics companies can train proprietary models on this data and deploy them in revenue-generating applications such as warehouse automation, kitchen assistance, or manufacturing without paying royalties or disclosing derivative works. The permissive terms allow combination with other datasets and integration into SaaS platforms, provided you include the Apache-2.0 license text and provide attribution. This makes IPEC-COMMUNITY/droid_lerobot particularly valuable for startups and enterprise teams that need clear IP boundaries when sourcing training data for production systems.
Who should use the IPEC-COMMUNITY/droid_lerobot dataset?
This dataset is ideal for robotics teams training manipulation policies on Franka hardware or similar 7-DOF arms, especially those requiring large-scale pretraining data with permissive licensing. VLA researchers building foundation models can use the 27 million frames as part of a diverse pretraining corpus, while behavior cloning practitioners benefit from the 92,233 episode-level demonstrations spanning thousands of tasks. Teams working with LeRobot tooling or RLDS-formatted pipelines will find native compatibility, and organizations that have already deployed Franka robots can leverage embodiment alignment to reduce sim-to-real gaps. The dataset is also suitable for academic groups studying manipulation diversity, task generalization, or data scaling laws in robotic learning.
When is this dataset NOT the right choice?
Teams targeting non-Franka embodiments such as mobile manipulators, humanoids, or grippers with radically different kinematics should carefully evaluate transfer learning challenges, as policies may overfit to Franka-specific dynamics. If your application requires high-frequency control for contact-rich tasks like peg insertion or cable manipulation, the 15fps capture rate may be insufficient compared to datasets sampled at 30fps or higher. Organizations needing detailed task annotations, semantic labels, or success/failure flags for each episode will find the metadata sparse and may require significant manual curation. The dataset also lacks modality specification in public documentation, so teams with strict requirements for depth sensing, force-torque feedback, or tactile data should validate sensor coverage before committing to integration. Finally, if your procurement process prohibits Hugging Face as a data source due to compliance or air-gapped infrastructure constraints, alternative hosting arrangements would be necessary.
How do I download and load this dataset efficiently?
The dataset is hosted on Hugging Face and can be loaded using the datasets library with the identifier IPEC-COMMUNITY/droid_lerobot, or through LeRobot-native loaders that provide trajectory replay and visualization tools. Because the data is chunked into 93 Parquet files of approximately 1,000 episodes each, you can download incrementally by specifying chunk ranges, allowing validation on a subset before committing to the full 27-million-frame download. For distributed training, leverage Parquet's columnar format to apply predicate pushdown and column selection, loading only the observation modalities and action dimensions required by your model architecture. Teams with bandwidth constraints should consider downloading chunks in parallel during off-peak hours, and those with limited local storage can stream directly from Hugging Face during training, though this introduces network latency that may bottleneck high-throughput dataloaders.
What frame rate and temporal resolution does this dataset provide?
All episodes in IPEC-COMMUNITY/droid_lerobot are captured at 15 frames per second, yielding a temporal resolution of approximately 67 milliseconds between consecutive frames. This frame rate is suitable for many manipulation tasks involving object grasping, placement, and tool use where motion dynamics occur on timescales of hundreds of milliseconds. However, teams working on high-speed assembly, dynamic catching, or contact-rich insertion tasks may find 15fps insufficient to capture critical transition moments, as rapid state changes between frames can create ambiguity for temporal models or make inverse dynamics estimation noisy. If your application requires finer temporal granularity, consider whether temporal interpolation, frame synthesis, or hybrid approaches combining this dataset with higher-frequency collections can meet your modeling needs while still leveraging the scale and task diversity this dataset provides.
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