Challenges in Robotics

A Multi-Dimensional Problem

Real-world data, crucial for training robust physical AI systems, remains severely limited. Current robotic training heavily depends on datasets collected at significant expense, yet these datasets often lack sufficient diversity or scalability for broad applicability.

To illustrate the magnitude of this challenge, consider the following comparison:

Language vs. Robotics

• Language: Approximately 15 trillion text tokens are included in modern language-model corpora (e.g., FineWeb dataset for Llama 3, Hugging Face).

• Robotics: Approximately 2.4 million robot-motion episodes are available in today's largest open corpus (Open X-Embodiment aggregate, arXiv).

To make the comparison "apples to apples", we convert robotics data in episodes into timesteps:

• Assuming a control frequency of 20 Hz and task duration of 25 seconds, it gives us 500 steps per episode

• The 2.4 million episodes would generate 1.2 billion timesteps

• This equates to roughly 13 thousand times more data for language than robotics.

However, this disparity extends beyond mere scale. Additional complexities make scaling robotics significantly more challenging compared to language or images.

The General Approach to Training LLMs

Mainstream large language models (LLMs) typically rely on a static, "one-off harvest + offline pre-training" approach:

• Capture a static snapshot of web data → offline preprocessing → single extensive pre-training run.

• Researchers assemble a fixed corpus (e.g., Common Crawl, Wikipedia, code repositories), clean and deduplicate it, and then train the model once. According to a 2025 survey titled "LLMs as Repositories of Factual Knowledge," these models are "static artifacts, trained on fixed data snapshots" (arXiv).

• Periodically, this process is repeated for subsequent model generations. For example, Llama 3's creators compiled a 15-trillion-token FineWeb corpus from 96 separate Common Crawl snapshots, all prepared as a fixed batch prior to training.

• While fine-tuning and RLHF (Reinforcement Learning from Human Feedback) introduce limited fresh data, the fundamental bulk-harvest paradigm remains unchanged. Alignment processes use orders-of-magnitude fewer tokens and occur offline, resulting in models shipped as static checkpoints until the next update cycle.

The Unique Challenge of Embodied Systems

Unlike static LLMs, embodied AI systems are inherently dynamic. Static corpora, foundational for models like ChatGPT, quickly become outdated when applied to real-world robotic hardware. The "one-off harvest + offline pre-training" method is ineffective for robotics because robotic policies degrade rapidly under real-world conditions (e.g., when joints heat up). Consequently, embodied AI must adopt a streaming data approach, continuously adapting and evolving rather than relying on fixed, warehoused datasets.

In addition, here are some other challenges in robotics that illustrate why simply increasing data volume is insufficient for advancing embodied AI:

Challenge	What it looks like in practice	Why it's more than a "data scaling" problem
Cost spiral	Each extra 10 k demos can cost $100 k-$1 M in lab time, hardware depreciation, and staff.	Data scaling curves could look super-linear, not cheap “web-crawl” curves.
Reality gap	Sim-trained skills miss unmodeled friction, flex, delays, etc.	Truly covering all edge cases with domain randomization alone might not be possible for generalized robots, so sim-to-real bridges become necessary.
Safety-bound exploration	Robots can’t just “self-play” like AlphaGo, in the real world—bad policies damage hardware or humans.	Limits the amount and diversity of autonomous real-world data you can gather.
Non-stationary world	Factory layouts evolve; lighting, surfaces, and human coworkers change over months.	Policies that were trained on stale datasets quickly become sub-optimal; constant updating is required.
Continual Learning	Fine-tuning a robot's model with today’s data results in catastrophic forgetting of yesterday’s.	Demands sophisticated lifelong-learning algorithms beyond basic fine-tuning.
Hardware divergence & cumulative drift	Two “identical” robots behave differently once tolerances, joint backlash, or firmware drift set in; wear-and-tear changes dynamics daily. Compounding error: the same policy executed for 10k steps drifts into entirely new state space.	Once deployed, new data should be robot-specific. Universal datasets lose value; perpetual fine-tuning or online adaptation becomes necessary.

Scaling Robotics: Bridging the Gap from Simulation to Reality

The journey toward general-purpose robotics hinges critically on the ability to scale data, models, and learning methodologies effectively.

The Role of Simulation and the Sim-to-Real Gap

Simulated environments like NVIDIA’s Omniverse have become indispensable for training robotic systems, enabling high-throughput generation of synthetic data. These environments allow for rapid prototyping, exhaustive testing, and broad scenario coverage. However, despite their scale, simulations struggle to capture the long-tail complexity of real-world physics and human environments—a persistent challenge known as the sim-to-real gap.

To address this, high-fidelity sim-to-real pipelines are emerging that incorporate gap-closing techniques, such as domain randomization, sim-to-real transfer with residual learning, and physics refinement. These pipelines dynamically adapt simulated environments to better match real-world observations, creating a more robust bridge between virtual training and physical deployment.

Real-World Data as a Complementary Foundation

To overcome these limitations, real-world data acquisition becomes essential. Techniques such as video capture, motion capture (mocap), and teleoperation (remote operation of robots by human operators) offer rich datasets that more accurately reflect the unpredictable and nuanced nature of the physical world. These methods not only help validate and fine-tune models trained in simulation but also expand the diversity of data available for training, a key factor in developing robust and adaptive robotic systems.

Transfer Learning and Architectural Innovations

Beyond data collection, advancements in model architectures and training paradigms play a pivotal role in scaling robotics. Transfer learning has emerged as a particularly powerful technique, enabling AI agents to generalize behaviors learned in one domain and apply them across different tasks or environments. This reduces the need for extensive retraining and allows robots to adapt more quickly to new challenges. By reusing prior knowledge, these models require fewer data to perform competently in unfamiliar settings, significantly accelerating development timelines and reducing resource requirements.

Learning at All Levels

One of the most transformative frontiers in scalable robotics is the ability to adapt continuously and autonomously. At the core of this vision lies the development of continual learning pipelines—systems that enable robots to learn incrementally from their own experiences, adapt on-the-fly to changing conditions, and refine their behaviors over time without losing previously acquired knowledge.

Toward Universal Physical Intelligence

The convergence of scalable simulation, real-world data integration, and transfer learning unlocks a path toward universal physical intelligence—robotic systems capable of generalizing across a wide range of environments and tasks. These foundational pillars must be advanced in tandem to realize the vision of autonomous agents that can seamlessly operate in the unstructured, unpredictable conditions of the real world.