Continuous Latent Action Models in Long-Horizon Robotic Tasks with Limited Labeled Data

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do continuous latent action models perform in long-horizon robot tasks (e.g., Lift or X-Maze) when fine-tuned with a small amount of labeled data, measured by success rates and compared to. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.9/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How do continuous latent action models perform in long-horizon robot tasks (e.g., Lift or X-Maze) when fine-tuned with a small amount of labeled data, measured by success rates and compared to discrete token baselines?

Autonomous literature synthesis. Automated review score: 7.9/10. Full text and citation available at Assignee Research.