iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception
arXiv:2410.09072v5 Announce Type: replace Abstract: We present iTeach, a deployable system that lets any co-located human fix a robot's perception failures on the spot without expertise, a workstation, or offline retraining. The operator wears a mixed reality (MR) headset, sees the robot's segmentation predictions overlaid on the real scene, and corrects failures hands-free: rearranging objects (HumanPlay), annotating via gaze and voice, and triggering SAM2 backward mask propagation. Each ~20 s
Overview
arXiv:2410.09072v5 Announce Type: replace Abstract: We present iTeach, a deployable system that lets any co-located human fix a robot's perception failures on the spot without expertise, a workstation, or offline retraining. The operator wears a mixed reality (MR) headset, sees the robot's segmentation predictions overlaid on the real scene, and corrects failures hands-free: rearranging objects (HumanPlay), annotating via gaze and voice, and triggering SAM2 backward mask propagation. Each ~20 s interaction yields 150-300 densely labeled training frames; the system fine-tunes the perception model onboard, keeps the better model, and redeploys, all without leaving the deployment site. The full loop requires only an RGB-D camera, onboard GPU, and an MR headset: any mobile robot, any environment. Starting from 26.1 on cluttered real-world scenes, 45 teaching interactions (13K frames) raise segmentation to 80.7 with no catastrophic forgetting; on three standard benchmarks the model never trained on, performance improves as well. Downstream pick-and-place on SceneReplica reaches 72/100, surpassing a model-based pipeline requiring CAD models. A 12-participant user study confirms non-experts match experts on annotation accuracy (~95% box IoU), speed, and task load (NASA-TLX 21/100). The framework is architecture-agnostic: any fine-tunable perception model can serve as backbone.
Source
Originally published at arxiv.org.
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Source: https://arxiv.org/abs/2410.09072
