RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory
Overview
This research introduces RoboArchitect, a system that automates the design of robotic agent harnesses by observing robot rollouts, diagnosing failures, and iteratively revising control strategies. Without updating any model weights, RoboArchitect increases average success rates from 9.7% to 43.3% on unseen scenes and from 12.7% to 40.2% on unseen tasks across multiple benchmarks. The system automatically designs intuitive control strategies, such as lifting a jammed arm or navigating around obstacles, and evolves its own persistent memory to carry learned experience into later decisions. These results suggest that substantial improvements in robotic manipulation can be achieved by optimizing the agent harness rather than fine-tuning model weights.
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Reported quantity · improvement factor: 4.5 other · Basis not reported
“ing 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 raises average success from 9.7% to 43.3% on unseen scenes (4.5x) and from 12.7% to 40.2% on un”
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- arXiv Robotics — research abstractsSignalRoboArchitect: Automating the Design of Robotic Agents with Persistent Memory
This research introduces RoboArchitect, a system that automates the design of robotic agent harnesses by observing robot rollouts, diagnosing failures, and iteratively revising control strategies. Without updating any model weights, RoboArchitect increases average success rates from 9.7% to 43.3% on unseen scenes and from 12.7% to 40.2% on unseen tasks across multiple benchmarks. The system automatically designs intuitive control strategies, such as lifting a jammed arm or navigating around obstacles, and evolves its own persistent memory to carry learned experience into later decisions. These results suggest that substantial improvements in robotic manipulation can be achieved by optimizing the agent harness rather than fine-tuning model weights.
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