A Break-Down of Research in Artificial Intelligence

Motion Granted

Movement foundation models aim to make AI smarter about the way we move—unlocking breakthroughs in diagnosis, interaction, and embodied intelligence.

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Imagine for a moment that you’re trying to understand someone, not by what they say or what they see, but purely by how they move (the way they stand, walk, reach, or tremble). That subtle tremor in a hand? It might be an early sign of Parkinson’s. A toddler’s awkward step could hint at a developmental delay. Movement, in all its complexity, reveals more about us than we might expect. Yet surprisingly, modern artificial intelligence (AI) still treats human movement as a second-class citizen.

That’s the problem a group of researchers (from Penn) set out to fix in a recent paper titled “Grounding Intelligence in Movement.” While AI has made incredible leaps in processing language and vision (thanks to large models like GPT and powerful image transformers), movement has been largely left behind. In most systems, it’s simply the output (what a robot does after the model has already processed vision and made a decision). Or it’s treated as a raw data stream: motion sensors on a smartwatch, or a pose tracked by a camera. But those are just fragments. There’s no unified way to understand movement as an intelligent signal in itself.

The research argues this gap is holding back critical progress in a wide range of fields—clinical rehabilitation, robotics, eldercare, athletics, even animal behavior studies. The authors point out that movement isn’t just an end-product of thought; in biological systems, it is a form of intelligence. Movement is how organisms explore, adapt, and learn. And if AI is ever going to operate seamlessly in the real world (whether helping a stroke survivor regain their balance or syncing with a coworker robot on an assembly line), it needs to treat movement as a foundational mode of intelligence (not an afterthought).

To address this, the authors don’t build a single new model. Instead, they propose a full framework (essentially a roadmap) for how to bring movement modeling up to the same level as language or vision. Their approach unfolds in three coordinated steps:

Step 1: Aggregate Diverse Movement Data

Right now, data on human and animal movement is scattered across labs, hospitals, and private companies, and every dataset uses its own rules. The authors call for a shared “movement data pile” that standardizes formats, similar to what neuroscience did with brain imaging data in the 2010s. This would allow researchers and developers to train models on a wide variety of real-world movement types, from infant crawling to robotic grasping to clinical gait exams.

Step 2: Pre-Train a Multimodal Model

Just as GPT-4 was trained on massive text corpora to develop a broad understanding of language, the same idea could be applied to movement. The authors suggest building a general-purpose backbone model trained not just on body poses, but also on physics-based forces and simulations. This would enable the model to capture the physical meaning behind motion, not just its shape on screen. Importantly, they emphasize using interpretable, low-dimensional representations—so we’re not just creating black-box predictors, but intelligible systems that can explain why someone is moving the way they are.

Step 3: Evaluate Across Real-World Use Cases

Finally, the model’s value would be measured not in toy benchmarks, but in meaningful tasks: predicting clinical outcomes, enabling smarter rehab robots, transferring knowledge between human and animal motion, and adapting securely in sensitive environments like hospitals, where raw data can’t be easily shared.

The core insight is clear: treating movement as a first-class signal (something that can be learned from and generalized across contexts) could radically expand what AI can do in the physical world.

While the paper is forward-looking and conceptual in nature, it doesn’t just offer theory. The authors also ground their framework in real-world examples and emerging research trends to demonstrate its feasibility. They point to a growing body of experimental work (across robotics, biomechanics, and clinical AI) that proves the core building blocks for movement intelligence already exist. The challenge is to integrate them into a more unified, scalable model.

One illustrative example involves generative models that can mimic human gait. Researchers have shown that when trained on motion-capture datasets (like those capturing people walking under different conditions), AI models can generate new, realistic gait patterns. These aren’t just visual reconstructions; they reflect underlying biomechanical dynamics, capturing how small shifts in joint movement or balance can signal fatigue, impairment, or adaptation. In some cases, the models even generalize to new environments or unseen body types, hinting at their potential for real-world deployment in healthcare or athletics.

Another important experimental direction draws on physics. Rather than just learning “how movement looks,” some models are now learning how it works—integrating principles of force, mass, and torque. These physics-informed models produce far more realistic human meshes and simulated movements. For example, they can differentiate between someone intentionally jumping versus being pushed (something a purely vision-based model might miss). In effect, these models begin to “understand” movement not just as a sequence of frames, but as a causal, physical process unfolding over time.

These aren’t isolated breakthroughs. They represent a growing consensus that intelligent systems need to model motion in ways that go beyond data fitting. They need to infer intent, adapt to new bodies or tools, and learn from noisy, real-world examples. The paper weaves these strands into its broader framework by highlighting how many of today’s most promising results align with the movement modeling roadmap they propose.

To judge whether this approach is successful, the paper outlines a new set of evaluation principles, ones that differ sharply from the standard benchmarks used in computer vision or natural language processing.

First, the authors emphasize cross-domain generalization. If a model is trained on gait data from healthy adults, can it make sense of movement in an elderly population with balance impairments? Could it adapt from a lab-trained robotic policy to a field-deployed industrial arm? Success here would mean the model is learning something fundamental about movement itself, not just overfitting to a narrow task or dataset.

Second, they propose functional validity as a key metric. In other words, can the model be used to make actionable decisions or generate physically plausible alternatives? For instance, could it propose a safer walking pattern for a patient with hip dysplasia? Or simulate what would happen if a robot’s terrain changed from tile to gravel? This goes beyond statistical similarity; it requires a kind of causal intuition.

Third, and importantly, the paper addresses privacy-preserving performance. Many of the most sensitive and valuable movement datasets (like those collected from stroke survivors or Parkinson’s patients) can’t be openly shared. The proposed framework encourages federated learning and differential privacy techniques so that models can still learn across distributed systems without exposing raw data. Success, in this context, would mean delivering accurate results without violating confidentiality, a major concern in both clinical and corporate settings.

Together, these evaluation pillars create a high bar. They ask whether a model trained on movement can generalize, interpret, and act—while respecting the ethical boundaries of sensitive real-world contexts. And although the experiments referenced in the paper are drawn from disparate domains, they consistently point toward the viability of this movement-centric approach. Rather than waiting for one monolithic “movement AI,” we’re already seeing signs of convergence across disciplines—laying the groundwork for a unified, foundational capability that treats motion not as noise, but as knowledge.

As promising as this new approach to modeling movement is, there are clear limitations and careful thought is required to move from conceptual framework to real-world impact. Developing a unified foundation for movement intelligence isn’t just a technical challenge; it involves systemic, ethical, and infrastructural hurdles that must be acknowledged upfront.

One of the biggest roadblocks today is fragmentation. Unlike language or images (which already benefit from massive, standardized datasets scraped from the web), movement data is messy, scattered, and domain-specific. A hospital might record gait in terms of force plate readings. A sports analytics firm might rely on wearable sensors. A robotics lab might use simulated joint angles. Each dataset speaks a different “language,” and translating between them isn’t trivial. Without a shared convention for formatting and interpreting these signals, it’s difficult to build models that generalize across domains, let alone collaborate across institutions.

To tackle this, the authors call for the development of standardized, open-source formats, something like a “BIDS for movement” (referencing the Brain Imaging Data Structure that revolutionized neuroscience data sharing). This could allow researchers, clinicians, and developers to pool data while preserving key contextual details like sensor type, environment, and subject characteristics.

Privacy and ethics also loom large. Movement data is deeply personal. It can reveal everything from a person’s age to their neurological condition, even their identity. In security contexts, gait is already being used for surveillance. In clinical settings, poorly handled motion data could expose sensitive health conditions or treatment plans. The researchers recognize this and advocate for federated learning, a setup where models can be trained across multiple data sources without transferring raw data. Combined with anonymization techniques and strong data governance, this approach could strike a balance between innovation and responsible use.

Beyond infrastructure and ethics, there’s also the issue of benchmarking. If you’re building a language model, you can test it on question-answering, summarization, translation, and so on. But what’s the equivalent for movement? How do you know your model truly “understands” motion? The paper suggests moving away from narrow tasks (like “predict the next pose”) and toward evaluations that require higher-order reasoning. Can the model simulate a plausible recovery from a fall? Can it distinguish between intentional and unintentional movements? Can it adapt its predictions to different species, body types, or goals? These are the sorts of questions that will define success in this emerging domain.

Looking ahead, the potential impact of solving this problem is enormous. In healthcare, earlier detection of neurological disorders could lead to more proactive interventions. In robotics, machines could finally operate in fluid coordination with humans, not just around them. In sports, personalized movement intelligence could prevent injuries and optimize performance. Even in broader research, unified movement models could help scientists decode the behavioral patterns of animals in the wild—offering insights into everything from evolution to cognition.

But perhaps most compelling is the idea that this shift in focus (from static labels to embodied behavior) might help redefine what we mean by intelligence itself. After all, humans don’t think in a vacuum. We think while moving. We learn by doing. The ability to predict, interpret, and adapt movement may not just be a helpful feature for AI—it may be fundamental.

This paper doesn’t pretend to have all the answers. What it offers is a call to action: to treat movement not as a side effect of cognition, but as a core part of it. And in doing so, to open up a new frontier for AI, one grounded not just in pixels or text, but also in the rhythms, mechanics, and meaning of motion.

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