GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
- Published
- Source
- arXiv
- Paper number
- 630
- Field
- Robotics
- arXiv ID
- 2607.13960
Key points
- It uses an action-centric WAM that jointly learns future visual changes and actions during training, but decodes only actions at inference without generating video.
- It combines AC-WM and WAM pretraining and assigns visual changes and action generation to separate Transformer experts.
- In a local RTX 4090 environment, it reduced action-inference latency to 85 milliseconds while retaining the policy-learning benefits of future visual supervision.
- Agent-based AutoResearch automates the search over training configurations, potentially reducing the development burden of real-time closed-loop robot control.
- The 85-millisecond figure was measured in a specific RTX 4090 deployment environment, so it does not guarantee latency or performance on other hardware or a wider variety of robot tasks.
Paper links
External research summaries. These are not HDATF publications or measured product results.