Foundation models have changed how AI handles language and images. The next step may bring a similar shift into the physical world, where robots need to interpret their surroundings and choose actions as conditions change.
From one model per task to broader skills
Traditional robotics often relies on systems built for a specific job. A robot designed to pick and pack groceries may need a different model from one that sorts electrical parts or unloads pallets. Each new task can mean gathering more data and training another specialized system.
A foundation model offers another approach: train one model across a diverse set of tasks so it can carry skills from one situation into another. The idea is that broad experience can help a robot respond to unfamiliar or irregular conditions that a narrow system may not handle well.
This is the physical-world counterpart to a change already seen in language AI. Large language models learn from varied material and can apply what they have learned to tasks beyond any single example. For robotics, the hoped-for result is a system that can see, reason about its surroundings, and act with greater flexibility.
Robots need data from real interactions
Building that flexibility requires examples of how actions succeed or fail in the real world. Robotics does not have an existing, broad dataset that already captures how machines should interact with physical objects and environments.
Video alone cannot reliably show the details of physical interaction, and academic datasets may cover only a limited range of situations. A robot has to learn from contact, movement, and changing circumstances, so the source article argues that operating fleets of robots in production is the route to collecting diverse data at scale.
The quality of that data matters alongside its volume. A model trained on varied but unhelpful examples may not learn the actions people actually need. Real-world experience can provide a closer link between a robot’s decisions and their practical outcomes.
Why reinforcement learning matters
Many physical tasks have no single correct sequence of actions. Picking up a red onion, for instance, can be successful in more than one way. That makes simple learning from fixed examples insufficient for the full challenge.
Deep reinforcement learning combines reinforcement learning with deep neural networks. A robot can try actions, receive signals about whether they made progress, and adjust its behavior as it encounters new scenarios. In language AI, feedback from people can help shape responses toward human preferences; robotic learning likewise needs a way to distinguish helpful actions from unsuccessful ones.
This approach is intended to help machines keep improving rather than depend only on a prewritten set of instructions. But physical control is a distinct scientific problem: a robot must act in an environment where objects and circumstances can vary.
A promising shift with hard problems ahead
The source describes robotic foundation models as advancing quickly, with applications involving precise object manipulation already being used in production environments. It also points to a larger wave of commercially viable applications expected to be deployed at scale in 2024.
That outlook depends on solving challenges that differ from those in language AI. Robots need broad, high-quality interaction data, and they must learn to make reliable choices in unstructured settings. The promise is that one adaptable model could support work across logistics, transportation, manufacturing, retail, agriculture, and healthcare.
If these systems become capable enough, they could bring some of the efficiencies associated with digital AI into repetitive physical work. The path runs through real-world learning: gathering useful experience, improving through feedback, and applying learned skills across tasks.