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   General LLMs are rewriting robotics R&D, from code to benchtop experiments

   A TMTPost analysis describes two ways general-purpose LLMs are entering robotics. On Oct 16, MicroFactory founder Igor Kulakov said a single customer deployment used Claude Opus and GPT-6 Astra to generate control plans and code while dedicated programs executed the work, running 1.5 hours of autonomous electronics assembly with zero errors (a company claim). Two days earlier, Poke & Wiggle let Claude Opus 5.5 handle the full loop of reading papers, writing algorithms, training models and running real-robot evaluations to reproduce 10 robotics papers; an agent integrated and trained Microsoft's ~5B-parameter open-weight dual-arm Rho model the next day, reaching 87% success on a screw-driving task, and once diagnosed and rolled back its own inference-code bug in under 35 minutes. The piece also recounts a DSD reproduction where the agent widened the action-normalization range, flattening DSD's edge (45.3% vs 46.0%); restoring the paper's parameters gave DSD 45.3% against a 32.0% baseline, a 13.3-point lead. The author concludes R&D and integration workflows are shifting, but the evidence does not show general LLMs broadly replacing VLA models.

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