外观
论文arXiv 2610.0588223 天前
Mulligan：面向高效在机器人学习的性能引导数据采集
原文链接：Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning（arXiv 2610.05882 · 发布日期：2026-10-05）
Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning
Mulligan将初始状态分布作为决策，并结合价值函数，在真实和仿真高精度操作任务上提升监督部署数据效率。
核心要点
- 人类示教随策略提升边际收益下降，均匀采集会浪费在已能处理的初始状态上。
- Mulligan把初始状态分布作为决策，每轮从已观察失败和未尝试状态启动回合。
- 方法用包含失败在内的全部数据训练价值函数，增强交互式模仿学习的数据效率。
- 在匹配采集预算下优于均匀采样，结合价值动作选择使真实任务成功率提升10-34个百分点。
验证范围与局限：验证范围为三个真实世界任务的2,550个留出盲测回合和两个仿真任务。
英文摘要 (Abstract)
Learning from human demonstrations is a reliable way to teach robots new tasks, but the gains from each additional demonstration shrink as the policy improves. Continued improvement can instead come from supervised deployment, where an operator places the objects and intervenes when the policy fails. We ask how to maximize improvement from a fixed budget of supervised episodes on high-precision manipulation tasks with wide ranges of object placements. We observe that failures can concentrate in a small subset of initial states, so uniform collection spends much of the operator's time on states the policy already handles. Mulligan makes the initial-state distribution a decision, starting each round's episodes at observed failures and untried states. To further improve data efficiency, we augment interactive imitation learning with a value function trained on all data, including failures that imitation discards. Across three real-world tasks evaluated on 2,550 held-out, blinded episodes and two simulated tasks, Mulligan outperforms uniform initial-state sampling at matched collection budgets, and combined with value-based action selection, HiL-IDQL+Mulligan, improves final real-task success by 10-34 percentage points. With operator interventions, the human-robot team completes 98% of collection episodes, remaining productive while the policy learns. Videos, code, and data are available at https://mulligan.page/.
作者：Lars Ankile, Perry Dong, Rohan Tan Bhowmik, Aneesh Muppidi, David D. Yuan, Shuran Song, Chelsea Finn
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