Recent work shows that a frozen robot policy performs far better inside an agent harness, where control code decides when to query the policy, consult vision tools, or command the arm directly. Yet to
RoboArchitect automates robot harness design by iteratively revising control code around a frozen policy, substantially improving success on unseen scenes and tasks without model updates.
Recent work shows that a frozen robot policy performs far better inside an agent harness, where control code decides when to query the policy, consult vision tools, or command the arm directly. Yet today these harnesses are built by hand and redesigned for each new robot or benchmark. We show that harness design can be automated, offering a strong alternative to finetuning on a novel setup. In RoboArchitect, a coding model observes a robot's rollouts, diagnoses failures, and iteratively revises the harness around the frozen policy. The harness evolves on a set of episodes for its optimization and is evaluated on held-out scenes and tasks. Without updating any model weights, RoboArchitect…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 2 checks on 1 different day. Its best position was #30 of 30, reached on Nov 3, 03:28 UTC. It dropped off the list after Nov 3, 05:28 UTC.
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Not at the moment. It was last on the list on Nov 3, 05:28 UTC, after 1 day of trending.
#30 on Hugging Face · Daily papers, on Nov 3, 03:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 1 day (2 checks) since.