外观
论文arXiv 2610.1774815 天前
I3L：用于全身移动操作的迭代交互式模仿学习
原文链接：I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation（arXiv 2610.17748 · 发布日期：2026-10-13）
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation
I3L通过全身纠正与迭代策略优化，解决离线示教策略在长程全身移动操作中的可靠部署问题，并在三种机器人、八个仿真与真实任务上验证。
核心要点
- 离线遥操作示教训练的策略易在分布外退化，长程全身移动操作会放大累积误差。
- I3L结合JoyLo+全身纠正、触摸接管、迭代部署与重训练，针对更新策略的失败采集修正。
- 四轮HG-DAgger在受控仿真中最终成功率最高；I3L较基线平均提升68.3个百分点。
- JoyLo+为低成本开源接口；视频、可视化和附录已在项目网页提供。
验证范围与局限：验证覆盖三种机器人形态和八个仿真及真实任务，其中包括四个长程全身移动操作任务。
英文摘要 (Abstract)
Policies trained offline from teleoperated demonstrations can degrade outside their training distribution. This gap is particularly acute in long-horizon whole-body mobile manipulation, where long horizons amplify compounding errors and coordinated arm, torso, and base motion makes exhaustive demonstration coverage impractical. We introduce I3L, an integrated hardware and learning system that combines whole-body correction with iterative policy refinement to address this last-mile reliability gap. Its low-cost, open-source JoyLo+ interface uses policy-tracking leader arms and touch-triggered takeover to enable coordinated corrections across the body parts, while iterative deployment and retraining allow operators to target failures of the updated policy. We evaluate I3L across three robot embodiments and eight simulated and real-world tasks, including four long-horizon whole-body mobile manipulation tasks. Controlled simulation experiments show that four-round HG-DAgger achieves the highest final success on both tasks. Under this protocol, I3L improves autonomous success over the base policies by an average of 68.3 percentage points and achieves at least 90% success on every task, outperforming demonstration-only training by 35.6 percentage points on average with the same number of training trajectories. Averaged across tasks, takeover duration per episode decreases by 72% over four rounds. Together, these results show that I3L helps close the last-mile gap between offline training and reliable deployment and provides practical insights into collecting and using corrections for long-horizon whole-body mobile manipulation. Videos, visualizations, and appendix are at https://i-3l.github.io/.
作者：Wensi Ai, Christopher Luey, Karthik Dharmarajan, Evans Han, Yunfan Jiang, Stef Ren, Jianwen Xie, Jiajun Wu, Li Fei-Fei, Ruohan Zhang
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