GaussianWAM: Distilling Geometry and Semantics from 3D Gaussian Fields into World-Action Models
- Published
- Source
- arXiv
- Paper number
- 1014
- Field
- Robotics
- arXiv ID
- 2608.24714
Key points
- Using a 3D Gaussian field as an intermediary, it spatially aligned depth, semantic, and coverage supervision with WAM representations that had previously focused only on predicting future video.
- Because the teacher is used only during training, the inference pipeline and computational cost remain unchanged, allowing use without additional production-service overhead.
- On LIBERO-Plus, it improved FastWAM from 52.05%→71.29% and Cosmos Policy from 71.52%→77.30%.
- Integrating teacher signals through the Gaussian field was effective: it scored 1.9 points higher than plain direct distillation (69.37%).
- In real dual-arm robot experiments (two UR7e robots), it also raised average success from 30.00% to 40.00%.
Paper links
External research summaries. These are not HDATF publications or measured product results.