arXiv:2606.08610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pipeline surrounding the algorithms. Building tasks, shaping rewards, and tuning hyperparameters require substantial expert effort, making RL workflows costly and difficult to scale. We introduce HARBOR, an agentic framework that frames robot RL automation as a harness-engineering problem: given a simulator codebase and a task specification, it automates the workflow fro
The increasing complexity and computational demands of advanced robotic systems using reinforcement learning necessitate more efficient automation tools for development and deployment.
HARBOR addresses a critical bottleneck in robot learning by automating significant portions of the engineering pipeline, potentially accelerating the development and adoption of sophisticated robotic applications.
The effort and expertise required to build, test, and scale reinforcement learning tasks for robotics are significantly reduced, moving towards more agentic development processes.
- · Robot manufacturers
- · AI software developers
- · Logistics and industrial sectors
- · Academic robotics researchers
- · Companies reliant on manual robotics engineering
- · Legacy robotics development platforms
Faster and cheaper development of complex robotic agents across various applications.
Broadened accessibility of advanced robotic automation to a wider range of industries and smaller enterprises.
Enhanced overall productivity and efficiency in sectors adopting these automated robotic systems, potentially leading to new economic models.
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Read at arXiv cs.AI