Domain Adaptation with Adaptive Imagination for Visual Reinforcement Learning under Limited Target Data
arXiv:2606.30192v1 Announce Type: new Abstract: Sim-to-real transfer remains a major obstacle for reinforcement learning (RL), especially for vision-based control where image observations exacerbate the state-distribution shift between simulation and the real world. Domain adaptation (DA) is a promising remedy for this challenge. Prior sim-to-real DA works have demonstrated encouraging results, yet these approaches typically assume substantially more target data, which is not available in practice. Indeed, their performance degrades significantly when the target data budget is reduced. To addr
The proliferation of advanced AI robotics and reinforcement learning necessitates robust sim-to-real transfer methods, making this research timely as practical applications demand solutions for limited real-world data.
This development addresses a critical bottleneck in deploying AI in physical systems, enabling more efficient training and safer deployment of intelligent agents in complex environments with scarce real-world data.
The ability to perform effective domain adaptation with significantly less target data changes the economics and feasibility of developing and deploying vision-based robotic systems.
- · Robotics companies
- · AI research labs
- · Automation industries
- · Logistics and manufacturing
- · Companies reliant on extensive real-world data collection for RL
- · Traditional simulation-only training methodologies
Improved efficiency and reduced cost in training visual reinforcement learning agents for real-world tasks.
Faster development cycles and broader adoption of AI-powered robotic systems in industrial and service sectors.
Enhanced autonomy and generalization capabilities of robotic agents, potentially leading to more flexible and adaptive automation solutions across various industries.
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Read at arXiv cs.AI