The development of large language models has demonstrated that large-scale, diverse data can significantly improve model capabilities. But data scale is only one piece of the puzzle. The Transformer architecture, pre-training objectives, scaling laws, and post-training methods like RLHF all work together to produce today’s LLMs.
But if you’ve worked on robotics AI, you know this firsthand: robot data is far harder to come by than language data.
Why is that? What exactly makes robot data so difficult? And are there solutions?
This article tackles the core challenges of embodied intelligence from the perspective of data.
The Unique Challenges of Robot Data
From a data format standpoint, text can typically be discretized into token sequences for processing, whereas robot data inherently involves continuous control, multiple sensors, multiple timescales, and physical interaction. A single robot interaction encompasses RGB/RGB-D images, proprioception (joint states), force/torque readings, motion trajectories, language instructions, environment states, and other multimodal information — characterized by high dimensionality, temporal structure, physical constraints, and interaction dependencies.
1. High Collection Cost
Language data can be crawled from the internet at near-zero cost. Robot data, however, requires a physical robot to execute tasks while simultaneously recording sensor readings.
High-quality robot data collection typically demands expensive hardware, manual teleoperation, and substantial engineering maintenance. Scaling up data for a complex task is far more costly than scaling internet data — it requires not only hardware investment but also data cleaning, annotation, and repeated validation.
2. Annotation Difficulty
Language data annotation is relatively straightforward — classification, named entity recognition, sentiment labeling — all with well-defined label schemas. Robot data annotation is far more complex:
- Action annotation. Robot actions are continuous, high-dimensional sequences. How do you define a “good” action? Take grasping as an example: the action can be a joint trajectory
[q1(t), q2(t)...q7(t)], gripper force, velocity, contact states, and more. The same task admits a vast number of valid solutions — for instance, “open the drawer” can be done by pulling from the left, pulling from the right, two-finger grasp, three-finger grasp, fast pull, or slow pull. Robot data annotation is therefore closer to learning a conditional distributionp(action | observation, task)rather than simplef(x) = label-style label prediction. - Reward annotation. Many tasks lack an explicit reward signal, requiring manual design or learning from demonstrations.
- Scene annotation. 3D scene understanding, object pose estimation, and contact point annotation all demand specialized tools and expertise.
3. Narrow Distribution
Language data covers a vast range of topics, styles, and languages. Robot data, by contrast, typically covers only:
- Specific robot hardware
- Specific task types
- Specific scene environments
This means robot models generalize far less effectively than language models. A policy trained on one robot may fail entirely when transferred to a different one. There is also a deeper issue here — the embodiment gap: it arises not only from morphological differences (e.g., a 7-DoF Franka arm vs. a humanoid robot hand) but also from sensor configurations (RGB camera vs. tactile sensor vs. force-torque), control frequencies (20 Hz vs. 200 Hz), and action space differences. This is precisely why cross-robot datasets like Open X-Embodiment matter — they explore how to leverage cross-embodiment data to improve generalization.
4. Safety Requirements
Mistakes by robot models have direct consequences in the physical world, so safety validation requirements are far more stringent. This means:
- Training data must be filtered to exclude dangerous behaviors
- Rigorous safety validation is required before deployment
- Human supervision and intervention mechanisms are necessary
These safety requirements add further complexity to data collection and model training.
Current Data Acquisition and Scaling Approaches
Faced with these challenges, the industry has developed several major strategies:
1. Teleoperation
The most mainstream data collection method. A human operator controls the robot through teleoperation equipment to complete tasks while sensor data and actions are recorded.
Pros: High data quality; directly demonstrates “good” behavior.
Cons: Expensive, slow, and hard to scale. Operator skill level also affects data quality.
Representative work: Open X-Embodiment dataset, DROID, RoboMimic.
2. Simulation Data
Automatically generating data in simulated environments. Simulations can run massively in parallel, rapidly producing enormous volumes of data.
Pros: Low cost, fast speed, and the ability to generate diverse scenarios and edge cases.
Cons: The Sim-to-Real gap. Policies learned in simulation may fail in the real world.
Representative platforms: NVIDIA Isaac Sim, MuJoCo, Habitat.
Common methods: Domain Randomization, System Identification.
3. Video Data
Learning from human manipulation videos. The internet contains a massive amount of human manipulation footage, offering enormous data volume.
Pros: Nearly unlimited data volume, covering a wide variety of tasks and scenarios.
Cons: Video tells you “what a human did,” but not “how the robot should execute it” — it lacks camera pose, hand-object interaction, force information, proprioception, and more. Video learning is therefore more like world knowledge pretraining rather than direct robot policy data. The core difficulty lies in action grounding: how to extract robot-executable action commands from visual observations. An active research direction is Video → Action: predicting hand trajectories, object affordances, or action primitives from human videos, then mapping them to robot policies.
Representative work: Ego4D, Video Language Models.
4. World Model Generation
Using world models to generate data in “imagination.” World models learn environment dynamics and then generate training data in imagination.
Pros: No real interaction needed; fast data generation. Can produce a variety of hypothetical situations.
Cons: The accuracy of the world model limits the quality of generated data. Long-horizon prediction error accumulates.
Representative work: DreamerV3 generates training trajectories through latent imagination for policy optimization; Genie and other video world models attempt to generate interactive environments for exploration.
World models can produce imagined trajectories usable for training or planning, but the validity of generated data depends heavily on whether the model has learned correct environment dynamics.
5. VLA Data (Vision-Language-Action)
This is an important emerging trend in robot data. Traditional robot data consists of state-action pairs, whereas VLA data unifies images, language instructions, and action trajectories into (image, language instruction, action trajectory) triplets. This data format enables robot models to understand natural language instructions and execute actions guided by visual input.
Pros: Unifies the representations of perception, understanding, and action, improving generalization across tasks and scenarios.
Cons: Requires large-scale, cross-robot, cross-task VLA datasets; collection and annotation costs remain high. In real-world robot settings, generalization is still limited when facing long-tail objects, novel environments, and fine-grained manipulation.
Representative work: RT-1, RT-2, OpenVLA, π0, RoboCat.
The Data Closed Loop: The Ultimate Solution for Robot AI
No single data source is sufficient on its own. The future direction is to build a “data closed loop” — where multiple data sources complement each other, forming a cycle of continuous improvement.
A typical data closed loop includes:
Stage 1: Simulation Pre-training
Large-scale training of a base policy in simulation. Leverages the parallelism of simulation to rapidly explore diverse situations.
Output: An initial policy model with basic task-completion capability.
Stage 2: Real-World Fine-Tuning
Fine-tune the simulation-trained policy with a small amount of real-world data. Real data can come from teleoperation or from autonomous exploration in real environments.
Output: A policy model adapted to the real world.
Stage 3: Autonomous Exploration
Deploy the policy model in the real world, letting the robot autonomously explore and collect new data. During exploration, the robot encounters novel situations and failure cases.
Output: New real-world data, including edge cases and failure cases.
Stage 4: Failure-Driven Data Collection
Not all collected data is equally valuable. The competitive edge in future data closed loops may not lie in who collects more data, but in who knows which data is most worth collecting. Failure cases (falls, collisions, grasp failures) often contain far more learning signal than successful ones — similar to corner case mining in autonomous driving. The core idea is: deploy → identify failures → collect targeted data → fine-tune, rather than blindly scaling up data volume. How to filter high-value samples from massive datasets is the key to data closed loop efficiency.
Stage 5: Iterative Improvement
Retrain the model with the new data to improve the policy. Then return to Stage 2 for further fine-tuning and deployment.
This cycle iterates continuously, with the model’s capabilities steadily improving.
Engineering Challenges of the Data Closed Loop
The concept of a data closed loop is clear, but its engineering implementation faces many challenges:
1. Sim-to-Real Transfer
How do you transfer a simulation-trained policy to the real world? This is the first critical node in the data closed loop. Common methods:
- Domain Randomization
- System Identification
- Progressive Transfer
- Real → Sim → Real: Using real-world data to estimate environment parameters and build digital twins, then training in simulation. This bidirectional closed loop is becoming a new engineering trend.
2. Data Quality Management
Data collected through autonomous exploration varies widely in quality. How do you filter out low-quality data and retain the valuable portions? This requires:
- Automated data filtering mechanisms
- Reward- or success-rate-based filtering
- Data deduplication and diversity assurance
2.5 Data Value
The competitive edge in future robot data closed loops may not lie in who collects the most data, but in whose data has the highest value. Data value depends on multiple dimensions: coverage, diversity, density of failure cases, novelty, and task relevance. A single failure data point at the boundary conditions can be more valuable for training than a hundred routine successful trials.
3. Continual Learning
Models need to keep learning from new data without forgetting old knowledge. In robotics, common approaches include:
- Experience Replay. Mixing old and new data during training to prevent forgetting.
- Skill Library. Saving learned skills as reusable modules; new tasks accelerate learning by composing existing skills.
- Policy Distillation. Distilling knowledge from multiple expert policies into a single general-purpose policy.
- Parameter-Efficient Tuning. Freezing most parameters and fine-tuning only a small number of adapter layers, reducing training cost while preserving base capabilities — though it cannot fully address catastrophic forgetting in continual learning.
4. Infrastructure
A data closed loop requires complete infrastructure support:
- Data storage and management systems
- Training pipelines
- Deployment and monitoring systems
- Version control and experiment tracking
Implications for Practitioners
Data is the core bottleneck for embodied intelligence. For practitioners, there are several takeaways:
Prioritize data engineering. Algorithms matter, but data matters more. People with experience in data collection, processing, and management will be in high demand.
Learn simulation tools. Simulation is a critical link in the data closed loop. Mastering tools like Isaac Sim and MuJoCo is an essential skill.
Understand Sim-to-Real. Methods like domain randomization and system identification are the bridge connecting simulation and reality.
Follow the data closed loop. Large-scale embodied intelligence systems will likely rely on a data-driven continual learning closed loop. Understanding the thinking and methods behind data closed loops is key to grasping industry trends.
Summary
Robot data is far harder to obtain than language data — high collection costs, annotation difficulty, narrow distributions, and strict safety requirements. This is one of the core challenges of embodied intelligence.
Current solutions include teleoperation, simulation data, video data, world model generation, and the emerging VLA data format. The future direction is building data closed loops, where multiple data sources complement each other to form a cycle of continuous improvement.
The engineering implementation of data closed loops faces many challenges, but it is an essential path for embodied intelligence. Whoever builds a robust data closed loop first will gain a decisive competitive advantage.
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