SC3-Eval: Evaluating Robot Foundation Models via Self-Consistent Video Generation
- Published
- Source
- arXiv
- Paper number
- 450
- Field
- Robotics
- arXiv ID
- 2606.18610
Key points
- Joint training of forward and backward dynamics corrects autoregressive rollout drift using backward signals.
- Cross-view inpainting training preserves multi-camera consistency without explicit memory.
- The backward dynamics mode is reused as a per-chunk uncertainty signal for early stopping when drift appears.
- Across seven real VLA policies, it achieves a closed-loop Pearson correlation of 0.929 and MMRV of 0.119, outperforming Ctrl-World, IRASim, and Cosmos-Predict 2.5.
- It classifies the real failure modes of policies, including language understanding, grasping, and releasing, into four categories and achieves more than 70 percent recall.
- It is trained on 381 hours of real table-bussing data and shows generalization to out-of-distribution tasks.
Paper links
External research summaries. These are not HDATF publications or measured product results.