Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914543130640384 |
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| author | Chen, Yanzhe Ma, Kevin Yuchen Lv, Qi Lin, Yiqi Bai, Zechen Gao, Chen Shou, Mike Zheng |
| author_facet | Chen, Yanzhe Ma, Kevin Yuchen Lv, Qi Lin, Yiqi Bai, Zechen Gao, Chen Shou, Mike Zheng |
| contents | While Vision-Language-Action (VLA) models offer broad general capabilities, deploying them on specific hardware requires real-world adaptation to bridge the embodiment gap. Since robot demonstrations are costly, this adaptation must often occur under a strict data budget. In this work, we identify a critical diversity trap: the standard heuristic of "maximizing coverage" by collecting diverse, single-shot demonstrations can be self-defeating due to non-vanishing estimation noise. We formalize this phenomenon as a Coverage--Density Trade-off. By decomposing the policy error into estimation (density) and extrapolation (coverage) terms, we characterize an interior optimal allocation of unique conditions for a fixed budget. Guided by this analysis, we propose Anchor-Centric Adaptation (ACA), a two-stage framework that first stabilizes a policy skeleton through repeated demonstrations at core anchors, then selectively expands coverage to high-risk boundaries via teacher-forced error mining and constrained residual updates. Real-robot experiments validate our trade-off framework and demonstrate that ACA significantly improves task reliability and success rates over standard diverse sampling strategies under the same budget. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07381 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation Chen, Yanzhe Ma, Kevin Yuchen Lv, Qi Lin, Yiqi Bai, Zechen Gao, Chen Shou, Mike Zheng Robotics Artificial Intelligence While Vision-Language-Action (VLA) models offer broad general capabilities, deploying them on specific hardware requires real-world adaptation to bridge the embodiment gap. Since robot demonstrations are costly, this adaptation must often occur under a strict data budget. In this work, we identify a critical diversity trap: the standard heuristic of "maximizing coverage" by collecting diverse, single-shot demonstrations can be self-defeating due to non-vanishing estimation noise. We formalize this phenomenon as a Coverage--Density Trade-off. By decomposing the policy error into estimation (density) and extrapolation (coverage) terms, we characterize an interior optimal allocation of unique conditions for a fixed budget. Guided by this analysis, we propose Anchor-Centric Adaptation (ACA), a two-stage framework that first stabilizes a policy skeleton through repeated demonstrations at core anchors, then selectively expands coverage to high-risk boundaries via teacher-forced error mining and constrained residual updates. Real-robot experiments validate our trade-off framework and demonstrate that ACA significantly improves task reliability and success rates over standard diverse sampling strategies under the same budget. |
| title | Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2605.07381 |