HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone
- Published
- Source
- arXiv
- Paper number
- 748
- Field
- Robotics
- arXiv ID
- 2607.25895
Key points
- It overcomes the fidelity limits of prior UMI data with 3 mm trajectory accuracy, microsecond synchronization, and a field of view of about 200 degrees.
- Zero-robot post-training means it is trained only on HiFi-UMI data and can then be deployed directly on real robots, which is validated on all three backbones.
- The strongest policy reaches an 85 percent success rate on precision insertion tasks, even though HiFi-UMI contains no trajectories for those tasks.
- With 4,000 hours of pretraining, action error decreases by 41 percent on 10 unsupported tasks, and one backbone gains an additional 18.1 percentage points in success rate.
- HiFi-UMI-2K, a high-quality dataset containing 2,000 hours of microsecond-synchronized data, was released as open source.
Paper links
External research summaries. These are not HDATF publications or measured product results.