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Researchers from UC Berkeley and NVIDIA have introduced an approach that uses AI coding agents to write readable code for industrial robots, rather than relying only on large imitation-learning datasets. IEEE Spectrum reports success rates of up to 99 per cent in testing, alongside faster robot retasking.
Why it mattersThe report also mentions two robotics benchmarks, GaP and VABAR. The practical idea is to have agents help design, test and refine robot skills, though the reported results do not establish how the approach performs in real-world deployments.
Discuss: For industrial robots, should teams favour AI agents that generate inspectable code, or methods trained on large demonstrations?
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