arXiv · cs.AI · Wensi Ai, Christopher Luey, Karthik Dharmarajan, Evans Han, Yunfan Jiang, Stef Ren, Jianwen Xie, Jiajun Wu, Li Fei-Fei, Ruohan Zhang · 8 小时前模型评分46
I3L：面向全身移动操作迭代交互式模仿学习的硬件与学习系统
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation
导读
I3L 是一套结合全身纠正与迭代策略精炼的软硬件系统，用低成本开源的 JoyLo+ 接口让操作员通过策略跟随主臂和触摸触发接管进行全身协调纠正。在三种机器人本体、八项仿真与真实任务上，四轮 HG-DAgger 使自主成功率较基础策略平均提升 68.3 个百分点，每项任务至少达 90%，同等训练轨迹数下比纯示范训练高 35.6 个百分点。
分类置信度
中 · 待人工复核
- 只有单一媒体来源，需要人工复核。
来源级别：媒体与个人
事件类型：研究发布
证据摘录
- “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.”
来源：arXiv · cs.AI · arxiv.org