How better grippers can unlock physical AI
Models can generate actions, but hardware must execute those actions. | Credit: OnRoot Physical AI promises robots that can perceive, act and adapt in the real world with far less task-specific engineering than traditional automation. It’s a compelling proposition: robots powered by AI models trained on vast amounts of data, capable of improving over time and operating effectively in far less structured environments. But how do we get there? As AI systems move from the digital to the physical wo
Physical AI refers to robots that can perceive, act, and adapt in the real world with less task-specific engineering than traditional automation, but these intelligent systems depend on more than just software models. The grippers and end-of-arm tools that robots use to interact with physical objects are critical to whether AI models can actually execute their decisions reliably in unstructured environments. For physical AI to work effectively, these tools must handle real-world variation in parts and conditions, provide feedback through sensing during manipulation, capture contact data that complements vision and simulation, and offer flexibility to switch between different types of grippers for different tasks. This makes the hardware layer of manipulation as important to physical AI's success as the AI models themselves.
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