Robotics Dataset Comparison
Below is a comprehensive overview and comparative analysis of 16 mainstream robotics datasets and frameworks. The report is organized into two parts: first, a summary table that highlights key characteristics, and second, detailed descriptions of each dataset's scope, technical features, advantages, and disadvantages.
Note: This analysis is accurate as of the last modified date, "Mar 28, 2025."
Summary Table
| Dataset / Framework | Scope & Application | Scale & Modalities | Key Advantages | Key Disadvantages |
|---|---|---|---|---|
| 1. LeRobot (GitHub) | Real-world robotics for imitation and reinforcement learning; supports both simulation and physical robots. | Pretrained models and demo datasets; primarily visual and robot state data with temporal (multi-frame) context. | End-to-end learning with community support; integrated simulation environments. | Complex setup; may require substantial computing and sensor calibration. |
| 2. Open X-Embodiment (Website) | Large-scale, multi-embodiment robotic manipulation; pooling data from many institutions. | 1M+ trajectories spanning 22 robot embodiments; heterogeneous real-world data. | Massive diversity enabling cross-robot transfer and positive knowledge sharing. | Heterogeneous quality and potential standardization issues across varied sources. |
| 3. DROID (Website) | In-the-wild robot manipulation for robust imitation learning. | 76K demonstration trajectories (~350 hours) recorded with Franka Panda arms; multiple camera viewpoints. | Diverse, large-scale manipulation data that improves policy robustness. | Mostly limited to manipulation with a specific hardware setup; less diversity in task types. |
| 4. RoboTurk (Website) | Crowdsourced robotic skill learning via teleoperation; real-world demonstration collection. | Pilot and real-world datasets (hundreds to thousands of demos, several hours of data) from teleoperated sessions. | Leverages non-expert, scalable human demonstrations; supports collaborative tasks. | Variation in demonstration quality and potential limits in scale compared to fully automated data collection. |
| 5. MIME (Google Sites) | Imitation learning for robot manipulation using human demonstrations. | Multi-modal data (visual, robot states, actions) collected via teleoperation; moderate number of trajectories. | Focus on high-quality manipulation trajectories; well-suited for imitation learning. | May be smaller in scale and less diverse than some large-scale multi-robot datasets. |
| 6. Meta-World (Website) | Benchmark for multi-task and meta-reinforcement learning in simulation. | 50 distinct simulated manipulation environments; task variations with visual observations. | Standardized benchmark for meta-RL; structured for evaluating generalization. | Limited to simulation and may not capture the full variability of real-world settings. |
| 7. RoboNet (Website) | Open database of real robotic experience for manipulation tasks across multiple platforms. | ~15M video frames, collected from 7 robot platforms with diverse camera viewpoints. | Large-scale, multi-platform real-world data that facilitates cross-robot generalization. | Very high storage and processing requirements; complex data integration. |
| 8. RoboSet (Website) | Multi-task dataset for household (kitchen) manipulation tasks, including language instructions. | 28,500 trajectories (mix of ~9.5K teleop and ~19K kinesthetic demos), recorded with 4 camera views per frame. | Rich, multi-modal data in realistic home environments; supports language-guided sequencing. | Domain-specific (largely kitchens); may not generalize to non-domestic scenarios. |
| 9. BridgeData V2 (Website) | Large-scale robotic manipulation across diverse environments and skills with language annotations. | ~60K trajectories, 24 environments, 13 skills; includes multi-view (fixed, wrist, randomized) RGB (and depth) data plus natural language. | Very diverse and large-scale, ideal for cross-domain generalization and multi-modal learning. | Often collected with a specific robot (e.g. WidowX); complex setup and annotation consistency challenges. |
| 10. RT-1 (Website) | Real-world imitation learning for multi-task manipulation using transformer architectures. | Over 130K episodes covering 700+ tasks from 13 robots; uses visual and language inputs for closed-loop control. | Outstanding generalization and performance on diverse tasks; scalable transformer model. | High training and computational requirements; system complexity may be a barrier. |
| 11. Dobb·E (Website) | Framework for home robotics: learning household manipulation tasks quickly in real homes. | “HoNY” dataset: 13 hours from 22 NYC homes, 5,620 trajectories, RGB and depth at 30 fps; also includes hardware (the “Stick”) for data collection. | Cost-effective, rapid task learning with real household data; designed for generalist home robots. | Domain-specific to domestic settings; quality and consistency can vary with non-expert demonstrations. |
| 12. RH20T (Website) | Comprehensive dataset for contact-rich, multi-modal robot manipulation tasks in the real world. | Millions of human-robot demonstration pairs; modalities include high-resolution RGB, depth, force/torque, audio, tactile, and high-frequency joint data. | Extremely rich multi-modal data enabling detailed analysis and one-shot imitation learning. | Very large and complex; requires significant computational and storage resources; complex data processing pipeline. |
| 13. BC-Z (Website) | Large-scale behavior cloning for robotic manipulation. | (Details are sparser online but BC-Z is designed to support imitation learning with a large number of trajectories.) | Provides a standardized dataset specifically aimed at behavior cloning; useful for benchmarking imitation algorithms. | May offer less diversity outside manipulation tasks and less extensive documentation compared to other datasets. |
| 14. MT-Opt (Website) | Multi-task reinforcement learning at scale across many manipulation skills. | Data collected from 7 robots over 9,600 robot hours spanning 12 tasks; continuous multi-task RL framework. | Enables simultaneous learning across tasks; improves performance especially on underrepresented skills through shared experience. | Demands large-scale infrastructure and careful task specification; complexity in multi-task coordination. |
| 15. VIMA (Website) | General robot manipulation via multimodal prompts (combining language and vision) for unified task specification. | Benchmark with thousands of procedurally generated tabletop task instances; uses imitation learning data alongside transformer-based models. | Unified formulation that “prompts” the robot to perform diverse tasks; highly scalable and sample-efficient. | Primarily demonstrated in benchmark/simulated settings; real-world transfer may require additional adaptation. |
| 16. SPOC (Website) | Imitation learning for long-horizon navigation and manipulation using shortest path imitation (trained in simulation, deployed in the real world). | Trained with RGB-only inputs in simulation; demonstrated on real robots for tasks such as object fetching and navigation. | Robust long-horizon planning; effective sim-to-real transfer with minimal sensing (RGB only); no need for depth or privileged info. | RGB-only perception can limit object recognition; some failure cases persist in challenging real-world scenarios. |
Detailed Comparison
1. LeRobot
Scope & Application:
LeRobot is designed to lower the barrier for robotics research by providing an end-to-end learning framework with integrated pretrained models, diverse datasets, and simulation environments. It is well suited for imitation and reinforcement learning research on both simulated and real robots.
Technical Features:
- Built in PyTorch with modular dataset classes that support multi-frame temporal sampling.
- Offers pretrained policies (e.g. ACT, Diffusion, TDMPC) and supports various robot platforms and environments.
Advantages:
- Community-driven with active contributions and hosted on Hugging Face.
- Facilitates rapid prototyping in robotics with an accessible codebase.
Disadvantages:
- Complexity in data handling (various sensor streams and temporal dynamics) can demand significant compute and expertise.
2. Open X-Embodiment
Scope & Application:
A collaborative effort pooling robot data from 21 institutions, it is aimed at training “generalist” policies across 22 different robot embodiments.
Technical Features:
- Aggregates 1M+ trajectories from diverse robots and tasks.
- Supports learning via transformer-based architectures that can generalize across different embodiments.
Advantages:
- Unmatched diversity, which is ideal for studying cross-robot transfer.
- Large scale increases the potential for generalization.
Disadvantages:
- The heterogeneity of data can introduce inconsistencies; standardizing varied datasets is challenging.
3. DROID
Scope & Application:
Focused on in-the-wild robot manipulation, DROID offers a vast dataset for robust imitation learning using Franka Panda robots.
Technical Features:
- Contains 76K trajectories (~350 hours) across 564 scenes and 86 tasks.
- Multi-camera views (including wrist and exterior images) enable rich visual inputs.
Advantages:
- Large, diverse dataset that significantly boosts policy performance and robustness.
- Extensive coverage of real-world scenarios.
Disadvantages:
- Being collected with a specific hardware platform, its applicability to other robots may be limited.
4. RoboTurk
Scope & Application:
RoboTurk is a crowdsourcing platform that leverages teleoperation for collecting human demonstrations on both simulated and real robotic tasks.
Technical Features:
- Provides datasets with hundreds to thousands of successful demonstrations (e.g. pilot dataset and real-world dataset).
- Includes system features for low-latency teleoperation and human-in-the-loop interventions.
Advantages:
- Enables scalable data collection from non-experts, lowering the cost of obtaining rich demonstrations.
- Proven effectiveness in enabling imitation learning on challenging tasks.
Disadvantages:
- The quality of demonstrations may vary due to differences in human teleoperation skills.
5. MIME
Scope & Application:
MIME targets imitation learning for manipulation, offering human demonstrations that capture complex manipulation behaviors.
Technical Features:
- Multi-modal data including visual inputs and robot state/action trajectories collected through teleoperation.
Advantages:
- Focused on detailed manipulation tasks, making it ideal for imitation learning studies.
Disadvantages:
- Generally smaller in scale compared to some of the largest datasets; might offer limited diversity.
6. Meta-World
Scope & Application:
A simulation benchmark intended for meta-reinforcement learning and multi-task learning, Meta-World comprises 50 distinct manipulation environments.
Technical Features:
- Structured environments with varying goal positions and task variations to test generalization.
Advantages:
- Standardized and well-documented benchmark that is widely used for evaluating meta-RL algorithms.
Disadvantages:
- Limited to simulated settings; real-world complexities (e.g. sensor noise, dynamics variations) are not fully captured.
7. RoboNet
Scope & Application:
RoboNet is an open database of robotic experience collected from 7 different robot platforms, with an emphasis on visual data for manipulation.
Technical Features:
- Contains over 15M video frames and data from multiple camera viewpoints.
Advantages:
- Offers vast amounts of real-world data to study generalization across different robot hardware.
Disadvantages:
- Requires heavy storage and processing; integrating multi-platform data can be challenging.
8. RoboSet
Scope & Application:
A dataset focused on household (kitchen) manipulation tasks, RoboSet provides both kinesthetic and teleoperated demonstrations with language instructions.
Technical Features:
- 28,500 trajectories captured with 4 camera views per frame; tasks are semantically grouped.
Advantages:
- Rich multi-modal information (visual + language) supports language-guided robotic learning.
Disadvantages:
- Domain-specific to kitchen and household scenes; may not generalize to industrial or outdoor scenarios.
9. BridgeData V2
Scope & Application:
Designed to boost generalization in robotic skills, BridgeData V2 spans 24 environments and 13 skills, with natural language annotations for goal conditioning.
Technical Features:
- Approximately 60K trajectories with multi-view RGB (and some depth) data.
- Includes both teleoperated and scripted demonstrations.
Advantages:
- High diversity in environments and tasks; strong support for language-conditioned policy learning.
Disadvantages:
- Often tied to a particular hardware setup (e.g. WidowX 250), and the multi-view setup can complicate data preprocessing.
10. RT-1
Scope & Application:
RT-1 is a state-of-the-art transformer-based model for real-world robotic control trained on a massive dataset of diverse tasks.
Technical Features:
- Over 130K episodes covering more than 700 tasks collected from 13 robots.
- Utilizes vision and natural language inputs to produce discretized action tokens.
Advantages:
- Demonstrates superior performance and generalization, including sim-to-real transfer.
- Scalability through high-capacity transformer models.
Disadvantages:
- Demands extensive data, compute, and engineering expertise; system complexity is high.
11. Dobb·E
Scope & Application:
Dobb·E focuses on home robotics, providing a full stack (hardware, dataset, models) for learning household manipulation tasks with minimal demonstration time.
Technical Features:
- “HoNY” dataset includes 13 hours of data from 22 New York City homes (5,620 trajectories, RGB + depth at 30 fps).
- Includes a low-cost hardware “Stick” for demonstration collection.
Advantages:
- Cost-effective and designed for rapid task learning in domestic environments.
- Demonstrates strong real-world applicability in home settings.
Disadvantages:
- Domain-specific and may not translate to other application areas; non-expert demonstrations can introduce variability.
12. RH20T
Scope & Application:
RH20T is a comprehensive dataset aimed at learning diverse, contact-rich manipulation skills with extensive multi-modal sensor information.
Technical Features:
- Contains millions of demonstration pairs with modalities including high-resolution RGB, depth, force/torque, audio, and tactile sensing.
- Detailed synchronization and calibration across multiple sensors.
Advantages:
- Extremely rich and diverse data ideal for advancing one-shot imitation learning and fine-grained sensor fusion.
- Supports research on contact-rich and dexterous manipulation.
Disadvantages:
- Enormous data volume makes it challenging to store, process, and analyze; high complexity in data format and licensing.
13. BC-Z
Scope & Application:
BC-Z is targeted at behavior cloning for robotic manipulation, providing a large-scale dataset that is useful as a benchmark for imitation learning approaches.
Technical Features:
- Although details are less extensively documented online, BC-Z is positioned alongside other large imitation learning datasets.
Advantages:
- Serves as a standardized resource for evaluating behavior cloning algorithms.
Disadvantages:
- May not offer as much diversity or multi-modal richness as some of the larger, more comprehensive datasets.
14. MT-Opt
Scope & Application:
MT-Opt is a framework for continuous multi-task reinforcement learning designed to learn a wide repertoire of manipulation skills concurrently.
Technical Features:
- Built on data collected from 7 robots over 9,600 hours, spanning 12 tasks with a scalable RL method.
Advantages:
- Effective at sharing experience across tasks, significantly boosting performance on rare tasks.
- Demonstrates both zero-shot and rapid fine-tuning capabilities.
Disadvantages:
- Requires large-scale robotic infrastructure and sophisticated multi-task training pipelines.
15. VIMA
Scope & Application:
VIMA presents a novel formulation in which diverse robot manipulation tasks are “prompted” via interleaved language and visual tokens, unifying task specification.
Technical Features:
- Transformer-based model that leverages multimodal prompts; benchmark includes thousands of procedurally generated tabletop task instances.
Advantages:
- Unified, scalable approach that achieves strong zero-shot generalization and high sample efficiency.
- Allows integration of various forms of task instructions (text + image).
Disadvantages:
- Largely demonstrated in controlled (often simulated or tabletop) settings; additional work may be needed for full real-world deployment.
16. SPOC
Scope & Application:
SPOC focuses on long-horizon navigation and manipulation by imitating shortest paths. Trained entirely in simulation (using RGB-only inputs), it is deployed in the real world without extra sim-to-real adaptation.
Technical Features:
- Uses a transformer-based action decoder conditioned on language instructions and sequential RGB frames.
- Emphasizes a minimalist sensory setup (RGB only) to drive exploration and task completion.
Advantages:
- Achieves robust long-horizon planning and recovery in real-world tasks despite minimal input modalities.
- Trains entirely in simulation and transfers effectively.
Disadvantages:
- RGB-only perception can limit object detection accuracy; some failure cases persist in complex or cluttered real-world scenarios.