Adaptation of Generalist Robot Policies with Minimal Data
A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet fully autonomous learning remains difficult with current policies: sparse rewards and weak zero-shot exploration make it unlikely that a robot will discover successful behavior from scratch. We study minimal-data adaptation, a regime in which a pre-trained robot policy must learn a new task from as little as one demonstration followed by autonomous online interaction. This setting serves as the closest tractable proxy for fully autonomous improvement, allowing us to study whether minimal human guidance can bootstrap autonomous learning and w...
| Authors | Shreyas Kowshik, Sreyas Venkataraman, Leo Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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