AD-WM: Action-Discriminative World Models for Counterfactual Model Predictive Control
- Published
- Source
- arXiv
- Paper number
- 1130
- Field
- Robotics
- arXiv ID
- 2609.30264
Key points
- It precisely identifies the structural problem of world models: low factual prediction error does not mean good action selection.
- It attaches an action-recovery (inverse dynamics) auxiliary task during training only, so predictions retain action information, and removes it at test time to keep planning unchanged.
- It improved hard-start success from 3.7% to 52.0% on OGBench-Cube, and real Franka robot pick-and-place from 42.2% to 71.1%.
- It also showed that diagnostic metrics such as elite regret align with actual success better than plain prediction error.
Paper links
External research summaries. These are not HDATF publications or measured product results.