RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabRoboArchitect automates the design of an agent harness, the control code that decides when to query a frozen robot policy, consult vision tools, or command the arm directly. A coding model observes rollouts, diagnoses failures, and iteratively revises the harness without updating model weights. Average success rises from 9.7% to 43.3% on unseen scenes and from 12.7% to 40.2% on unseen tasks, across multiple benchmarks and policies. The evolved harness also builds persistent memory carrying learned experience into later decisions.
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- Primary sourceHugging Face Daily Papers
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RoboArchitect automates the design of an agent harness, the control code that decides when to query a frozen robot policy, consult vision tools, or command the arm directly. A coding model observes rollouts, diagnoses failures, and iteratively revises the harness without updating model weights. Average success rises from 9.7% to 43.3% on unseen scenes and from 12.7% to 40.2% on unseen tasks, across multiple benchmarks and policies. The evolved harness also builds persistent memory carrying learned experience into later decisions.
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Key findings from the stored brief
- Harness design around a frozen policy is automated by a coding model that revises control code from observed failures.
- Average success on unseen scenes rises from 9.7% to 43.3%, and on unseen tasks from 12.7% to 40.2%.
- Gains are largest where the benchmark is far from the policy's training data.
- The evolved harness develops persistent memory, such as effective grasp depths, used in later decisions.
Source claims not independently checked; results are reported across the benchmarks and policies the authors selected.
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Evidence & sources
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Why this may matter
The work suggests substantial performance headroom in robotic manipulation may lie in agent harness design rather than model finetuning, and that this design can be automated without weight updates. This could reduce the manual engineering needed for each new robot or benchmark.
Limits and open questions
Source claims not independently checked; results are reported across the benchmarks and policies the authors selected.
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