HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning

HARBOR is an automated framework that uses specialized AI agents to streamline reinforcement learning workflows for robot training, eliminating manual environment setup, reward shaping, and hyperparameter tuning. Demonstrated across 16 robotic tasks, the system reduces engineering effort while maintaining competitive performance and enabling real-world robot deployment.

HARBOR addresses a critical bottleneck in robotics development: the substantial engineering overhead required to implement reinforcement learning pipelines. Traditional RL workflows demand expertise in simulator configuration, reward function design, and algorithm tuning—tasks that consume significant resources and limit scalability. By automating these components through an agentic architecture, HARBOR democratizes access to RL-based robot training and accelerates development cycles.

The framework's significance lies in its practical approach to AI automation. Rather than focusing solely on algorithmic improvements, HARBOR tackles the engineering pipeline that surrounds RL algorithms. This reflects a broader industry shift toward reducing friction in AI workflows. The use of specialized agents executing bounded stages with persistent artifacts and reusable knowledge mirrors successful patterns in software engineering and DevOps, suggesting that agentic systems work best when decomposing complex tasks into manageable, standardized components.

For robotics developers and organizations, HARBOR reduces both computational and human capital requirements. Evaluation across manipulation, locomotion, and bimanual control tasks demonstrates generalizability. The framework's ability to transfer simulated policies to real robots validates practical applicability—a critical hurdle in robotics research.

Looking ahead, this work indicates a maturing ecosystem where agentic AI handles increasingly complex engineering tasks. Future iterations might extend to hardware-specific optimization or multi-robot coordination. The open-source research community will likely adopt these patterns, accelerating widespread implementation of automated RL pipelines.

• →HARBOR automates end-to-end reinforcement learning workflows for robotics, reducing manual engineering effort in environment setup and reward design.
• →The framework successfully scales across six benchmarks spanning manipulation, locomotion, and dexterous control with competitive or improved performance.
• →Agentic decomposition into bounded stages with persistent artifacts enables decentralized parallel trials and cross-run learning.
• →Simulated policies transfer to real robots, validating practical deployment potential beyond simulation environments.
• →This advancement reduces barriers to RL adoption in robotics by shifting expertise requirements from domain specialists to framework users.