Introducing I3-3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation
Fei-Fei Li and her team present I3L, a new method for improving whole-body mobile manipulation in long-horizon robotics tasks. The approach uses iterative interactive imitation learning to address deployment challenges, with a paper and website provided for further details.
The paper introduces I3L, an iterative interactive imitation learning framework aimed at improving the performance of whole-body mobile manipulation in long-horizon robotics tasks. The method addresses the challenge of closing the last-mile gap during deployment, where existing policies often fail.
Source: Fei-Fei Li — X Robotics · Read original article ↗
Article text · Original source · English
Our latest work in closing the last-mile gap on long horizon robotics tasks.
Long-horizon, whole-body robot policies still fail during deployment. How do we best close this last-mile gap? 🤖 🚨 Introducing I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation Website: https://i-3l.github.io/ Paper: https://arxiv.org/abs/2610.17748View the quoted post on X
Source：Fei-Fei Li · x.com