Quality over Quantity: Demonstration Curation via Influence Functions for Data-Centric Robot Learning
Learning from demonstrations has emerged as a promising paradigm for end-to-end robot control, particularly when scaled to diverse and large datasets. However, the quality of demonstration data, often collected through human teleoperation, remains a critical bottleneck for effective data-driven robot learning. Human errors, operational constraints, and teleoperator variability introduce noise and suboptimal behaviors, making data curation essential yet largely manual and heuristic-driven. In this work, we propose Quality over Quantity (QoQ), a grounded and systematic approach to identifying high-quality data by defining data quality as the contribution of each training sample to reducing ...
| Authors | Haeone Lee, Taywon Min, Junsu Kim et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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