X4Val: Learning Neural Surrogates for Variance-Reduced Policy Evaluation
- Published
- Source
- arXiv
- Paper number
- 309
- Field
- Robotics
- arXiv ID
- 2606.05159
Key points
- It motivates an evaluation method that uses heterogeneous data sources, including simulation, historical policy logs, and data collected from related platforms or environments.
- The paper provides both theoretical analysis and empirical evaluation for autonomous driving and real-world robot manipulation tasks, where X4Val achieves up to a 38.4% reduction in variance and shows consistent gains over strong baselines in these domains.
- These results show that using unpaired heterogeneous data can substantially improve the sample efficiency of rigorous robot system verification.
Paper links
External research summaries. These are not HDATF publications or measured product results.