RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory
Abstract
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 raises average success from 9.7% to 43.3% on unseen scenes (4.5x) and from 12.7% to 40.2% on unseen tasks (3.2x), across multiple benchmarks and policies. The gains are largest where the benchmark is far from the policy's training data: wrapping a general-purpose policy, the evolved harness yields a specialist that often outperforms even the finetuned version. Intuitive control strategies are automatically designed, such as lifting a jammed arm clear or reaching in horizontally when an obstacle blocks the way from above. The harness also evolves its own persistent memory, carrying learned experience, such as effective grasp depths, into later decisions. These results suggest that substantial headroom in robotic manipulation lies in the agent harness, and its design can be effectively automated and optimized without weight updates.
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Research summary
The paper introduces RoboArchitect, a method that automates the design of agent harnesses around frozen robot policies using iterative coding LLM optimization, demonstrating that substantial performance gains in robotic manipulation can be achieved without weight updates by evolving control logic and persistent memory, effectively turning general policies into specialists for specific setups.
Methodology
The paper proposes "RoboArchitect," a framework that automates the design of an "agent harness" surrounding a frozen Vision-Language-Action (VLA) policy. The core methodology involves an iterative optimization loop where a coding LLM (Claude Opus 5) observes robot rollouts, diagnoses failure modes, and rewrites the control code (harness) to mediate between the environment and the frozen policy. The harness is not a fixed architecture but evolves to include specific control routines (e.g., jam recovery, force rescaling) and persistent memory structures. The approach is distinct from standard fine-tuning as it updates no model weights, relying instead on programmatic logic and external tool calls (VLMs, detectors). The novelty lies in treating the agent's control logic as an evolvable software artifact rather than a static wrapper, allowing the system to dynamically decide when to trust the policy vs. direct control.
Results & evaluation
The experiments are extensive, covering multiple benchmarks (LIBERO-Pro, RoboCerebra, VLABench, RoboCasa, SimplerEnv) and multiple policies (OpenVLA, Pi-0, MolmoAct-2, etc.). The evaluation protocols are rigorous, distinguishing between "unseen scenes" (same tasks, new layouts) and "unseen tasks" (new tasks, shared skills). The results show significant improvements (e.g., 9.7% to 43.3% on unseen scenes), and notably, the evolved harness often outperforms the fine-tuned version of the same policy in out-of-domain settings. The ablation on iterative refinement vs. one-shot design confirms the value of the self-improvement loop. The emergence of persistent memory (e.g., storing grasp depths) is a strong qualitative and quantitative finding.
Reproducibility
The paper provides a GitHub link and detailed implementation sections. It specifies the exact coding model (Claude Opus 5), auxiliary tools (Qwen3, Molmo2, Grounding-DINO, SAM 3), and iteration limits. The cost analysis (<$150, <6 hours) is a strong point for practical reproducibility. However, the heavy reliance on a specific, proprietary coding model (Claude) may limit reproducibility for labs without access to that specific model version or API.
Limitations
The primary limitation is that all evaluations are in simulation. The paper acknowledges this and suggests the interface should transfer to real robots, but no real-world validation is provided. Additionally, the method is capped at 5 iterations, and the saturation point is not fully explored. The complexity of the harness code could become a maintenance burden, though the paper argues it is interpretable. The reliance on a powerful coding LLM makes the method expensive in terms of API costs, though the paper argues this is cheaper than collecting new demonstrations.
Broader impact
This work has high potential impact by offering an alternative to the data-hungry fine-tuning paradigm in robotics. If the "harness optimization" approach scales to real-world robots, it could drastically reduce the cost of adapting robot policies to new environments. It bridges the gap between code-as-policy and learned VLA policies, suggesting a hybrid future where LLMs write the control logic around frozen neural networks. The concept of "evolving" the agent's software interface is a significant shift in how robotic agents are deployed. The paper introduces RoboArchitect, a method that automates the design of agent harnesses around frozen robot policies using iterative coding LLM optimization, demonstrating that substantial performance gains in robotic manipulation can be achieved without weight updates by evolving control logic and persistent memory, effectively turning general policies into specialists for specific setups.
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