A robot that keeps moving after major physical damage sounds like a scene built to unsettle people. For Skild AI, it is evidence that robot control may be moving toward a more flexible kind of artificial intelligence.
The startup, led by cofounder and CEO Deepak Pathak, is working on what he calls an "omni-bodied brain": one AI system intended to work across many robots and many tasks. The goal is not simply to make one machine more capable, but to train a model that can transfer what it has learned from one body to another.
Why robot AI needs a broader training path
Robotics has a data problem. Pathak argues that common training methods, including teleoperation and simulation for a specific system, do not produce enough data to create the kind of leap that language models achieved for chatbots.
Skild AI’s answer is to train a single algorithm across many physical robot designs and many tasks. Over time, that process is meant to create a model with a broader sense of how bodies interact with the world.
The company calls the larger system Skild Brain. For an academic paper, its researchers built a smaller version named LocoFormer. That model is central to the company’s argument: a robot AI can become more useful if it learns patterns that are not tied to one fixed body.
Pathak describes the ambition directly: "Any robot, any task, one brain. It is absurdly general." The important claim is not that every problem is solved, but that the model is being shaped to handle unfamiliar hardware and sudden changes without starting from zero.
How LocoFormer learns to cope with change
LocoFormer is trained with large-scale RL on many procedurally generated robots, using aggressive domain randomization. In plain language, the system is exposed to a wide spread of robot bodies and conditions during training, rather than being tuned only for one machine.
The model also learns through online experience. When it falls in early trials, that experience can help improve later control. This matters because real robots do not operate in clean, fixed environments. They meet uneven terrain, hardware limits, weight changes and failures.
Pathak compares part of the approach to in-context learning in large language models. In that setting, a model can work through a difficult task by breaking it down and feeding its own intermediate reasoning back into the context. For robots, the parallel is physical: the system senses what is happening and tries to adjust its control strategy to the new situation.
The result is an AI model built to rebuild useful internal representations when the body or environment changes. According to the source, LocoFormer can respond to large disturbances such as morphological changes, motor failures and weight changes.
What the experiments showed
In one experiment, the Skild team trained the algorithm on many walking robots with different shapes. It was then tested on real two- and four-legged robots that had not appeared in the training data. The system was still able to control them and make them walk.
The most striking examples came when the robot body changed in extreme ways. The model adapted when legs were tied together, cut off or made longer. In the source’s most vivid demonstration, a four-legged robot continued crawling after all four legs were hacked off with a chainsaw.
Other tests showed different forms of adaptation:
- A four-legged robot placed on its hind legs sensed the ground under those legs and moved as if it were a humanoid.
- A quadruped robot with wheels and legs kept going after two of its motors were deactivated.
- That wheeled quadruped adapted by balancing on two wheels like an unsteady bicycle.
These examples are unusual because the model is not just recovering from one expected failure. It is being asked to reinterpret what kind of body it has and how that body can still move.
Beyond walking robots
Skild AI is also testing the same general approach for robot manipulation. The company trained Skild Brain on a range of simulated robot arms and found that the model could control unfamiliar hardware.
The robot arm work also included environmental change. The model adapted to a sudden reduction in lighting, which matters because manipulation tasks depend heavily on reading the surrounding scene correctly enough to act.
Pathak says the startup is already working with some companies that use robot arms. The source also reports that in 2024 Skild AI raised $300 million in a round that valued the company at $1.5 billion.
Skild is not alone in chasing more general robot AI. Other groups named in the source include the Toyota Research Institute and Physical Intelligence. What stands out in Skild’s approach is its emphasis on generalizing across many kinds of hardware.
Why this matters for the future of robotics
The deeper issue is whether robot AI can become less brittle. A robot that only works under expected conditions has limited usefulness. A robot that can adjust when its body, task or environment changes could be useful in a wider set of real-world settings.
The source frames Skild’s work as part of a broader belief among researchers: if enough training data can be gathered, robotic AI models could see a major advance similar to the one that reshaped language models and chatbots.
The demonstrations may look unsettling, and Pathak acknowledges that reaction. But he reads them differently. To him, the ability to keep adapting hints at what he calls a kind of physical superintelligence for robots. As he puts it, "It is so exciting to me personally, dude."
For now, the key takeaway is practical. Skild AI is trying to move robot control away from narrow, machine-specific training and toward a generalist model that can handle unfamiliar bodies, damaged parts and changing surroundings. If that approach continues to work, the meaning of a "robot brain" may become much less tied to the shape of any one robot.