WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
arXiv:2604.08958v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to a target task. Offline-to-online RL leverages prior data but typically assumes a given fixed dataset and does not address how to generate reliable data for transfer. We propose World Model-Based Experience Transfer (WOMBET), a framework that jointly generates and utilizes prior data. WOMBET learns a world model in the source task and generates offline data via uncertainty-penalized planni
The increasing demand for robust and efficient robotic systems in real-world applications drives continuous innovation in reinforcement learning techniques.
This development offers a method to significantly reduce the cost and risk associated with training robots, accelerating the deployment of autonomous systems.
The ability to generate and leverage reliable offline data through world models fundamentally alters the data acquisition and transfer learning paradigm in robotics.
- · Robotics companies
- · AI research institutions
- · Logistics and manufacturing sectors
- · Companies reliant on extensive manual data collection
Faster development cycles for robotic applications in hazardous or expensive environments.
Increased adoption of autonomous robots across new industries due to lower entry barriers and improved reliability.
A potential shortage of skilled technicians capable of maintaining and integrating increasingly complex autonomous systems.
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Read at arXiv cs.AI