Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
- Published
- Source
- arXiv
- Paper number
- 559
- Field
- Robotics
- arXiv ID
- 2607.02466
Key points
- The Decomposition Hypothesis separates how to move, physical control, from what to do, semantics, and only the latter requires language.
- An inverse-dynamics objective enables learning of a physical prior from unrelated trajectories and autonomous-driving data.
- SIMPLER matches models trained on more than 1 million expert examples and beats behavior cloning by an absolute 10%.
- On a real WidowX robot, it retains 25% success under camera perturbation, compared with 0% for an internet-scale baseline.
- Partial success rises from 31.8% to 45.8%, confirming the transferability of the physical pretraining.
- Stage 1 scale determines the performance ceiling, while extending Stage 2 has diminishing returns.
Paper links
External research summaries. These are not HDATF publications or measured product results.