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Enterprises battle rising labor and downtime costs as AI robots fall short of adjusting themselves in the moment to varied, unexpected tasks—such as switching from material handling to precise assembly on the fly.
This rigidity undermines the promise of flexibility and responsiveness that AI-powered robots are meant to deliver, leading to bottlenecks and lost competitive edge in environments centered on rapid fulfillment or customizable production.
The lack of seamless integration between AI algorithms, modular hardware, and cross-domain task libraries means current robots require manual intervention (often by highly-skilled engineering staff) for reconfiguration, which is time-consuming and expensive.
Existing modular robots require pre-programmed routines or complex manual setups for reconfiguration, and lack generalized AI frameworks to autonomously adapt to unknown tasks.
These approaches are slow, inflexible, and don’t meet the rising demand for last-minute versatility.
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