RoboPocket: Improve Robot Policies Instantly with Your Phone
Scaling imitation learning is fundamentally constrained by the efficiency of data collection. While handheld interfaces have emerged as a scalable solution for in-the-wild data acquisition, they predominantly operate in an open-loop manner: operators blindly collect demonstrations without knowing the underlying policy's weaknesses, leading to inefficient coverage of critical state distributions. Conversely, interactive methods like DAgger effectively address covariate shift but rely on physical robot execution, which is costly and difficult to scale. To reconcile this trade-off, we introduce RoboPocket, a portable system that enables Robot-Free Instant Policy Iteration using single consum...
| Authors | Junjie Fang, Wendi Chen, Han Xue et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 March 2026 |
| Last updated at source | 6 March 2026 |
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