As embodied AI continues to advance, data is quickly becoming the central bottleneck in developing new models. Robot data is expensive to collect, difficult to obtain, and inherently hard to scale [1]; [2]. Yet despite these challenges, real-world data continues to drive many of the most robust and deployable robotics systems. To address this constraint, the industry has increasingly turned to alternatives such as world models, simulation environments, and generative video models to help meet the growing demand for training data [3].
But while these approaches are promising, an important question remains: can simulated data truly replace real-world experience?
We argue that real-world robotic data, despite its cost and complexity, ultimately creates the most durable and defensible moat in embodied AI.
Why is Data Collection so Difficult?
Collecting real-world robot data requires significant infrastructure and coordination. Unlike traditional machine learning datasets, robotics data collection is an operational process involving hardware, people, and physical environments [1].
Hardware Requirements
Data collection requires specialized hardware and capture mechanisms. These vary widely in cost depending on the type of data being collected. For example, egocentric data collection can be relatively inexpensive, sometimes requiring only a single camera or smartphone. In contrast, teleoperation data is significantly more costly, as even the simplest setup requires a robot embodiment, control interfaces, and safety infrastructure.
Trained Operators
Data collection requires a team of trained operators. These operators must be capable of performing tasks consistently while generating high-quality demonstrations. Training operators and maintaining productivity adds both time and financial costs to the data collection process.
Task Design
Task design plays a critical role. Task designers must understand industry needs, define meaningful scenarios, and ensure that demonstrations capture useful behaviors. Without careful task design, large amounts of collected data may have limited training value.
Infrastructure and Data Pipelines
Real-world data collection requires both physical and digital infrastructure. Teams need dedicated environments for data capture, including workspace, objects, and safety setups. In addition, large-scale robotics datasets require substantial storage, processing, and data management pipelines.
Together, these requirements make real-world robotics data collection both costly and difficult to scale. As a result, optimizing data collection workflows becomes a key challenge for organizations building robotics datasets.
Why Real-World Data Remains Critical
Despite the difficulties in collecting it, real-world data presents several advantages over simulated data. These advantages primarily relate to robustness, generalization, and deployment reliability in unconstrained environments. The following sections outline key limitations of simulated data and why real-world data remains critical for building deployable robotic systems.
The Sim-To-Real Gap
The sim-to-real gap refers to the difficulty of transferring policies trained in simulation to real-world environments. This gap exists because simulated physics engines fail to fully capture the stochasticity and complex dynamics of the physical world. These limitations become especially noticeable in tasks involving cloth and deformable objects. Soft-body interactions, contact dynamics, and fine-grained material behavior remain difficult to simulate accurately, often due to constraints in mesh resolution and computational cost. As a result, models trained purely in simulation can perform well in controlled virtual environments but struggle when exposed to real-world variability [4]; [3].
Real-world data helps bypass this issue by cutting out the middleman. Instead of learning from approximations, models are trained directly on the environments in which they will ultimately operate.
While recent advances in simulation, domain randomization, and world models have helped narrow this gap, sim-to-real transfer remains one of the biggest challenges in robotics training today [3].
Generalization Challenges
Simulated environments often struggle to capture many of the complexities present in real-world settings, which makes generalization difficult. In real environments, lighting conditions change, humans move unpredictably around the robot, objects become cluttered, and occlusions frequently occur. These variations introduce a level of uncertainty that is difficult to fully replicate in simulation.
Even when simulators attempt to introduce variability through domain randomization, they typically fail to capture the full diversity and long-tail distribution of real-world scenarios. Small differences, such as sensor noise, object wear, reflections, or unexpected interactions, can significantly impact robot performance.
As a result, models trained primarily in simulation often perform well in controlled environments but struggle when deployed in dynamic, unstructured settings.
Real-world data helps address this challenge by exposing models to natural variability from the start, improving robustness and enabling better generalization across environments [2]; [5].
Reliability and Safety
Robots deployed in real-world environments must meet a high bar for reliability and safety. Unlike purely digital systems, robots interact directly with people, objects, and infrastructure, meaning failures can result in physical damage, safety risks, or costly downtime. As a result, robotic systems must learn to perform actions in ways that avoid both self-damage and unintended impacts on their surroundings.
Simulation plays an important role early in development. It allows teams to safely test behaviors, explore edge cases, and iterate quickly without risking hardware or environments. However, as systems move closer to deployment, training exclusively in simulated environments becomes less effective. Subtle factors such as sensor noise, wear and tear, latency, calibration drift, and unpredictable human interactions are difficult to model accurately, yet they are critical for ensuring reliable performance.
Because of this, achieving production-level reliability ultimately depends on exposure to real-world conditions [1]; [5]. Real-world data enables models to learn from true operational variability, failure cases, and edge scenarios that simulations often miss.
As robotics transitions from controlled demos to real deployments, real-world data becomes essential not just for performance, but for building systems that are safe, robust, and ready for everyday use [6].
Real-World Success Cases
Recent progress in embodied AI further reinforces the importance of real-world data. Several of the most capable robotic systems today rely heavily on real-world interaction and large-scale physical datasets.
π*₀.₆
Physical Intelligence's π*₀.₆ is one such example. The model improves through real-world interaction, incorporating reinforcement learning from deployment experience in addition to demonstration data. By learning from real-world successes, failures, and corrective interventions, the system is able to improve robustness and performance on long-horizon manipulation tasks.
GEN-1
Similarly, Generalist's Gen-1 model was pretrained on large-scale real-world manipulation datasets, including UMI-style data. These datasets emphasize diverse demonstrations collected across tasks, environments, and embodiments. Pretraining on this type of real-world data enables models to develop broader capabilities and adapt more effectively to new scenarios.
These examples highlight a broader trend in robotics: while simulation and synthetic data continue to improve, many of the most capable systems still depend heavily on real-world data for robustness, reliability, and deployment readiness.
The Strategic Importance of Real-World Data
As embodied AI continues to evolve, data is emerging as one of the most important drivers of progress. While simulation, world models, and generative approaches offer promising paths to scale, they remain limited by their ability to fully capture the complexity of the physical world.
Real-world data, despite being difficult and expensive to collect, provides advantages that are difficult to replicate synthetically. It helps close the sim-to-real gap, improves generalization to dynamic environments, and enables the level of reliability required for real-world deployment.
For organizations building embodied AI systems, this creates a clear strategic implication. The ability to collect, curate, and scale real-world robotics data is not just an operational challenge, but a long-term competitive advantage.
As robotics transitions from research prototypes to production systems, real-world data is likely to remain a critical foundation for building robust, deployable, and general-purpose robotic intelligence.
At Nurvai, we're working to make real-world robotics data easier to collect, scale, and deploy. If you're building embodied AI systems and looking to accelerate development, get in touch.
References
[1] D. Kalashnikov, A. Irpan, P. Pastor, et al., "QT-Opt: Scalable deep reinforcement learning for vision-based robotic manipulation," arXiv preprint arXiv:1806.10293, 2018.
[2] O. X.-E. Collaboration, "Open X-Embodiment: Robotic learning datasets and RT-X models," arXiv preprint arXiv:2310.08864, 2023.
[3] F. Muratore, M. Gienger, J. Peters, "A survey on sim-to-real transfer for robotics," Foundations and Trends in Robotics, vol. 9, no. 1–2, pp. 1–142, 2022. doi: 10.1561/2300000079.
[4] J. Tobin, R. Fong, A. Ray, et al., "Domain randomization for transferring deep neural networks from simulation to the real world," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017. arxiv.org/abs/1703.06907
[5] A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, et al., "RT-1: Robotics transformer for real-world control at scale," arXiv preprint arXiv:2212.06817, 2022.
[6] A. Brohan, Y. Chebotar, C. Finn, S. Levine, et al., "RT-2: Vision-language-action models transfer web knowledge to robotic control," arXiv preprint arXiv:2307.15818, 2023.