iTeach: Interactive Teaching for Robot Perception using Mixed Reality

Robots deployed in the wild often encounter objects and scenes that break pre-trained perception models, yet adapting these models typically requires slow offline data collection, labeling, and retraining. We introduce iTeach, a human-in-the-loop system that enables robots to improve perception continuously as they explore new environments. A human sees the robot's predictions from its own viewpoint, corrects failures in real time, and the informed data drives iterative fine-tuning until performance is satisfactory. A mixed reality headset provides the interface, overlaying predictions in the user's view and enabling lightweight annotation via eye gaze and voice. Instead of tedious frame-by-frame labeling, a human guides the robot to scenes of choice and records short videos while interacting with objects. The human labels only the final frame, and a video segmentation model propagates labels across the sequence, converting seconds of input into dense supervision. The refined model is deployed immediately, closing the loop between human feedback and robot learning. We demonstrate iTeach on Unseen Object Instance Segmentation (UOIS), achieving consistent improvements over a pre-trained MSMFormer baseline on both our collected dataset and the SceneReplica benchmark, where it leads to higher grasping success, followed by a real-world demonstration of grasping unseen objects with a Fetch robot. By combining human judgment, efficient annotation, and on-the-fly refinement, iTeach provides a practical path toward perception systems that generalize robustly in diverse real-world conditions. Project page atthis https URL