From ImageNet to BEHAVIOR-1K and the evolution of structured data in AI

Sarthak Bhardwaj -

Introduction

Over the past decade, some of the biggest leaps in artificial intelligence have been driven not just by better algorithms, but by better data. When Fei-Fei Li introduced the ImageNet dataset in 2009, it gave researchers a common foundation of structured training data that unlocked new levels of performance in computer vision . Today, Li and colleagues are tackling a new frontier: embodied AI and robotics. Their latest effort, BEHAVIOR-1K, aims to do for household robots what ImageNet did for image recognition – provide a large-scale, standardized benchmark to accelerate learning and innovation. This blog post explores the parallels between ImageNet and BEHAVIOR-1K, and why curated data is again the key to progress. We will examine how structured datasets have historically spurred breakthroughs, the challenges of creating such standards for robots, and how simulation-based learning in BEHAVIOR-1K lays the groundwork for embodied intelligence.

ImageNet: Structured Data that Sparked a Revolution

ImageNet began as an ambitious vision to supply AI with the data it desperately needed. At a time when most AI research focused on modeling techniques, Fei-Fei Li recognized that lack of rich training data was a major bottleneck . She set out to label millions of images across a vast ontology of object categories. The result was ImageNet: a database of 14 million labeled images organized into over 20,000 categories, from common objects like “balloon” and “strawberry” to thousands of others . Each category had hundreds of examples, and the annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC) focused on a 1.2 million image subset spanning 1,000 object classes . This uniform dataset allowed researchers worldwide to train and compare models on the exact same task.

Crucially, ImageNet’s scale and consistency set the stage for a breakthrough. In 2012, a deep convolutional neural network (CNN) called AlexNet was trained on ImageNet’s 1.2 million images and achieved a top-5 error rate of 15.3%, dramatically better than the 26.2% achieved by the next best approach . This 10+ percentage point improvement was unprecedented – effectively halving the error rate and it demonstrated the power of combining big data with new algorithms (and GPUs for training) . The shockingly strong result captured the attention of the entire tech industry . In hindsight, ImageNet is often credited for kickstarting the deep learning revolution in vision. It proved that with enough structured, labeled data, neural networks could far exceed previous limits. Over the next few years, vision models rapidly improved by “learning from data” rather than relying on hand-engineered features. ImageNet’s influence also spread beyond vision, validating the general strategy of feeding high-quality datasets to machine learning systems to unlock new capabilities.

The Challenge of Standardized Data in Robotics

While fields like computer vision and natural language processing have flourished with large benchmark datasets (e.g. ImageNet for images, and analogously massive text corpora for language), robotics has long lacked a shared standard. Until very recently, there was no “ImageNet for robots.” Instead, nearly every robotics lab defined its own tasks, environments, and evaluation criteria. One group might train a robot to stack blocks on a custom test rig, while another taught a robot to navigate an office – each using different setups and metrics. This fragmentation made it difficult to compare results or measure collective progress . Unlike classifying static images, robotics involves dynamic, embodied tasks with physics, sensors, and actuators, which are far harder to capture in a single dataset.

Several challenges have stood in the way of standardized robotic training data:

  • Diverse Task Contexts: Robots operate in many environments (kitchens, hospitals, factories, etc.) and perform varied tasks (from grasping objects to cleaning floors). Capturing this diversity in one benchmark is complex, whereas ImageNet could cover many concepts with just photos.
  • Multi-Modal and Temporal Data: A robot’s experience includes video, sensor readings, and continuous control actions – not just a single snapshot. Defining a dataset for long-horizon activities (e.g. making a bed or preparing a meal) is far more involved than labeling images.
  • Experimental Setup Differences: Hardware differences (robot arms vs. wheeled robots) and environmental variations meant that even when two teams tackled “the same” task, their conditions differed. Without a common platform, algorithms couldn’t be fairly benchmarked side by side .
  • Cost and Safety of Real Data: Gathering millions of real-world robot trials is expensive and can be unsafe (robots breaking or causing damage). This limited the size of any single real-robot dataset and discouraged sharing a unified large-scale experiment.

These issues have made robotic learning data the “wild west” compared to the curated world of ImageNet. Researchers recognized that if robotics was to advance toward general-purpose intelligence, it needed the equivalent of an ImageNet: a large, shared set of tasks and environments that the community could rally around . This is the gap that BEHAVIOR-1K is designed to fill.

BEHAVIOR-1K: An ImageNet for Embodied AI

BEHAVIOR-1K (Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments) is a bold attempt to bring the standardization and scale of ImageNet into the domain of embodied AI. Led by Fei-Fei Li’s lab at Stanford, the project introduces a comprehensive benchmark of 1,000 realistic household tasks that robots should be able to perform . The tasks were not invented arbitrarily by researchers – they were chosen based on extensive human surveys asking “What do you want robots to do for you?” . In this way, BEHAVIOR-1K is grounded in real human needs: it focuses on activities that people actually spend time on and want help with in daily life . Many of these activities are things like cooking meals, cleaning various rooms, organizing items, or caring for spaces, each potentially requiring dozens of steps and complex interactions. In fact, “making a pizza from scratch” is one example task on the list, and the team notes it is actually among the easier of the 1,000 tasks !

A humanoid robot performing a simple household chore, the kind of everyday task targeted by the BEHAVIOR-1K benchmark. By training in simulation on hundreds of such tasks, AI systems can learn to act and adapt in realistic home scenarios.

To make these 1,000 activities learnable and benchmarkable, BEHAVIOR-1K provides a rich simulation framework and a structured definition for each task. Some key features and design principles include:

  • Diverse 3D Environments: The tasks are instantiated across 50 interactive environments – including houses, apartments, gardens, offices, restaurants, and more that serve as realistic backdrops . These virtual environments are stocked with over 10,000 objects, from appliances and furniture down to utensils and food items, all modeled with rich physical properties . This ensures a wide variety of settings (a kitchen vs. a garden) and objects (a stove, a sink, toys, plants, etc.) for the tasks.
  • High-Fidelity Simulation: BEHAVIOR-1K is built on top of NVIDIA’s Isaac Sim platform combined with Stanford’s OmniGibson simulator . The simulation offers realistic physics and visuals – including support for fluids (liquids), deformable objects like cloth, transparent and reflective surfaces, and even heat and thermal effects . Robots can interact with water (pouring or cleaning), handle soft items like clothing, and experience true-to-life collisions and friction. This level of detail is aimed at reducing the “reality gap,” so that behaviors learned in the simulator will transfer plausibly to the physical world.
  • Formal Task Definitions: Each task is specified rigorously using a Behavior Domain Definition Language (BDDL). In plain terms, this means every activity comes with a script of its initial conditions, the required objects and tools, and a clear goal or success criterion for completion . For example, a task “setting a dining table” would define that the table starts empty, required objects include plates, utensils, etc., and success is achieved when each place at the table has the proper settings. This structured definition ensures that an AI agent (or a human evaluating the agent) can automatically determine if the task was done correctly.
  • Taxonomy of Objects and Actions: In designing the benchmark, the developers organized the thousands of objects using an extended synset hierarchy modeled on WordNet (the same lexical database used to structure ImageNet categories) . This means objects are grouped into categories (like “fruit” or “electronic device”), allowing tasks to be described abstractly. A task might say “pick up fruit from the kitchen counter and put it in the fridge,” and during simulation this could be instantiated with an apple in one run or an orange in another – both belonging to the fruit category . This approach echoes ImageNet’s use of WordNet to ensure broad coverage of concepts, and it helps AI agents generalize by learning categories of objects rather than one specific item.
  • Multiple Robot Platforms: The simulator supports a range of common robot embodiments – for instance, robotic arms like Franka or mobile manipulators like Fetch and Tiago . Participants can choose their robot model, but all operate under the same task definitions and physics. This flexibility makes the benchmark useful for testing algorithms across different hardware, while still keeping the evaluation consistent.

With these components, BEHAVIOR-1K offers a controlled yet rich playground to train and test embodied agents. It introduces long-horizon challenges that require a robot to navigate, manipulate objects, and sequence actions intelligently to achieve human-relevant goals . As Fei-Fei Li describes it, “This is not just a benchmark – it’s a call to imagine and create intelligent agents that can truly assist people in their daily lives.” It represents a step toward generalist AI systems that “understand, reason, and act in the complex world of humans” . In essence, BEHAVIOR-1K is positioned to be a “North Star” for the robotics and AI community – a shared reference point to guide and measure progress in making robots genuinely useful in everyday human environments .

Simulation and the Sim-to-Real Pipeline

A core pillar of BEHAVIOR-1K is that all training and evaluation take place in simulation. This is a practical necessity: collecting a comparable volume of real-world robotic experience would be prohibitively slow, expensive, and potentially dangerous. By using a high-fidelity simulator, researchers can generate essentially unlimited interaction data without wearing out real robots or risking accidents. Simulation also allows for systematic variation – one can easily reset a scene, try hundreds of strategies, or introduce random variations in lighting or object arrangement to improve robustness. As one robotics engineer bluntly put it, without enough training data even the fanciest robot is “just an expensive paperweight”, and in robotics “data is everything” . BEHAVIOR-1K leverages simulation to ensure robots get that data in a safe, scalable way.

The value of this sim-to-real pipeline is evident: researchers can train robot policies on thousands or even millions of simulated trials, then transfer the learned skills to physical robots. For example, an agent might practice 100,000 instances of grasping and moving objects under varied conditions in the simulator – something that could be done in a weekend on a cluster of machines . Such breadth of experience is impossible to achieve with real robots in the same time frame. By the time the policy is deployed on a real robot, it has already seen a vast array of scenarios. Of course, a known challenge is the sim-to-real gap: differences between the virtual world and reality can cause a policy that worked well in simulation to fail on a real robot. BEHAVIOR-1K’s answer to this is to maximize realism (through detailed physics and diverse environments) and to encourage techniques like domain randomization (varying simulator parameters) to make policies more transferable. In fact, the BEHAVIOR-1K team has already conducted an initial study by training a robot in a simulated apartment and then deploying it in a real-world apartment, to gauge how well the learned behaviors carry over . This experiment helps calibrate the simulation-to-reality gap and guide improvements to the simulator and algorithms.

Simulation isn’t a silver bullet, no virtual environment can capture every nuance of the physical world, but it dramatically lowers the barrier to acquiring large-scale embodied experience. The BEHAVIOR-1K platform thus serves as a generator of structured robot data: a place where new learning algorithms can be stress-tested on realistic tasks before risking real hardware . Over time, as successful strategies are ported to real robots, the feedback can flow back into improving the simulator, closing the loop in the sim-to-real pipeline. This approach of “train in sim, then adapt to real” is becoming a standard in robotics because it offers the best of both worlds – the scale and controllability of simulated data, and the grounding and validation of real-world deployment .

From Narrow Skills to Generalist Robots: Task Diversity and Generalization

One of the most exciting aspects of BEHAVIOR-1K is its emphasis on task generalization. In traditional robotics research, a project might focus on a single skill, say, grasping a box or navigating to a point. But human beings expect service robots to be multi-talented helpers, able to tackle whatever household chore or errand comes up. By compiling 1,000 different tasks, BEHAVIOR-1K implicitly demands a move away from one-trick robots toward generalist agents. The ultimate goal is to spur development of a single robot (or AI policy) that can learn to do many things, even things it wasn’t explicitly programmed for . As co-project lead Jiajun Wu noted, the target is “a single robot that can do all of these things”, and establishing common benchmarks now will help align the community toward that goal .

Several features of BEHAVIOR-1K support task generalization: the use of a common action and perception space (the robot has to use the same sensors and effectors across tasks), the overlapping object categories between tasks, and the requirement in the 2025 BEHAVIOR Challenge that teams must attempt a broad suite of 50 tasks rather than specialize on one . In the upcoming challenge, a robot is not judged by how well it can do a single job like “make toast,” but by its overall performance across dozens of distinct tasks spanning cooking, cleaning, tidying, fetching, and more . This incentivizes solutions that transfer learning from one task to another. For instance, an agent that has learned how to “pick up and store toys” might reuse that capability when trying to “gather and shelve books”, both involve similar underlying skills of recognizing objects, grasping them, and placing them in appropriate locations.

Early results underscore how far we have to go. The BEHAVIOR-1K paper reports that these 1000 activities are long-horizon and manipulation-heavy, posing a serious challenge to state-of-the-art robot planners and learning models . No current system can complete anywhere near all tasks reliably, many fail at basic subtasks or get lost in long sequences. But this difficulty is by design: the benchmark exposes the gaps in today’s methods and provides clear metrics to drive progress. As one researcher put it, BEHAVIOR-1K offers the “hill-climbing signal” that robotics has been missing. In other words, it gives the field a way to quantitatively measure improvement on meaningful tasks, much like ImageNet did for vision. With a public leaderboard and competitions (the BEHAVIOR Challenge at NeurIPS 2025), the community now has a focal point to compare approaches and push each other toward higher levels of performance . If widely adopted, BEHAVIOR-1K could become the standard baseline for training practical home robots, accelerating the development of robots that smoothly handle everyday tasks.

Training Data: The Core Bottleneck in Scaling AI

Looking at the big picture, whether it’s teaching algorithms to recognize images or to manipulate objects, training data remains the fundamental bottleneck for scaling AI capabilities. We have seen this pattern in multiple domains. In computer vision, performance shot up once millions of labeled images became available (e.g. ImageNet and beyond). In natural language processing, the advent of enormous text corpora and structured QA datasets enabled the rise of powerful language models. Each time, when the data hurdle was overcome, models grew more general and robust. Now, as AI moves beyond static tasks into embodied, interactive settings, the data challenge is rearing its head again in a new form. Robotic agents require not just images or text, but rich interactive experiences, sequences of perceptions and actions, to learn how to act intelligently. Yet obtaining and annotating such experiential data is a massive undertaking.

BEHAVIOR-1K’s simulation-driven approach is one solution, effectively generating a flood of synthetic experience for robots. But even with simulation, carefully curating and scaling the right data is non-trivial. The BEHAVIOR project team had to invest years of effort to survey human needs, formalize 1000 tasks, model thousands of objects, and integrate physics, all to construct a training dataset (albeit a virtual one) for embodied AI . This underscores a broader point: as we push AI into more complex real-world applications, from self-driving cars to domestic robots to industrial automation, the hardest part is often assembling the data pipeline. Algorithms and compute power have advanced rapidly, but they can only reach their potential when fed with adequate quantity and quality of data. In robotics especially, data collection and labeling is expensive and does not naturally scale the way digital data did. This is why companies and research groups are increasingly focusing on scalable data pipelines, leveraging simulation, augmentation, and other techniques to produce the massive training sets needed for the next generation of AI.

At Labellerr, we have observed this core challenge across many domains. Whether it’s annotating sensor data for autonomous vehicles, compiling diverse medical images for a diagnostic AI, or generating training scenarios for a warehouse robot, the story is similar, the bottleneck is building and managing the right data at scale. Our efforts are therefore centered on scalable data pipelines that can supply AI projects with the breadth and depth of examples they need. The emergence of benchmarks like BEHAVIOR-1K is encouraging, because it provides a clear target and a source of structured data for everyone to use. In the long run, solving the data problem, through initiatives like these and through robust data engineering practices, is what will unlock AI applications far beyond today’s static tasks.

Conclusive Thoughts

The trajectory from ImageNet to BEHAVIOR-1K highlights a consistent lesson in AI: breakthroughs follow when we get the data right. Fei-Fei Li’s landmark vision in 2009 was that a robust, labeled dataset could transform computer vision research, and it did. Now, with BEHAVIOR-1K, a similar vision is being applied to embodied AI and robotics.

By providing a rich common benchmark of simulated tasks, BEHAVIOR-1K is laying the foundation for progress in a field that desperately needs it. There is a long road ahead before we have robots as competent and general as the benchmark demands. But having a “North Star” dataset to guide and measure that journey is a pivotal first step. As researchers and practitioners, if we can combine ever-improving algorithms with the structured training data to learn from, we will continue to scale AI’s capabilities from recognizing pixels to truly understanding and interacting with the world around us.