iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception

[2410.09072] iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2410.09072 (cs) [Submitted on 1 Oct 2024 ( v1 ), last revised 14 Apr 2026 (this version, v4)] Title: iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception Authors: Jishnu Jaykumar P , Cole Salvato , Vinaya Bomnale , Jikai Wang , Yu Xiang View a PDF of the paper titled iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception, by Jishnu Jaykumar P and 4 other authors View PDF HTML (experimental) Abstract: Robotic perception models often fail when deployed in real-world environments due to out-of-distribution conditions such as clutter, occlusion, and novel object instances. Existing approaches address this gap through offline data collection and retraining, which are slow and do not resolve deployment-time failures. We propose iTeach, a failure-driven interactive teaching framework for adapting robot perception in the wild. A co-located human observes model predictions during deployment, identifies failure cases, and performs short human-object interaction (HumanPlay) to expose informative object configurations while recording RGB-D sequences. To minimize annotation effort, iTeach employs a Few-Shot Semi- Supervised (FS3) labeling strategy, where only the final frame of a short interaction sequence is annotated using hands-free eye-gaze and voice commands, and labels are propagated across the video to produce dense supervision. The collected failure-driven samples are used for iterative fine-tuning, enabling progressive deployment-time adaptation of the perception model. We evaluate iTeach on unseen object instance segmentation (UOIS) starting from a pretrained MSMFormer model. Using a small number of failure-driven samples, our method significantly improves segmentation performance across diverse real-world scenes. These improvements directly translate to higher grasping and pick-and-place success on the SceneReplica benchmark and real robotic experiments. Our results demonstrate that failure-driven, co-located interactive teaching enables efficient in-the-wild adaptation of robot perception and improves downstream manipulation performance. Project page at this https URL Subjects: Robotics (cs.RO) Cite as: arXiv:2410.09072 [cs.RO] (or arXiv:2410.09072v4 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2410.09072 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jishnu Jaykumar P [ view email ] [v1] Tue, 1 Oct 2024 18:30:59 UTC (31,290 KB) [v2] Sun, 11 May 2025 17:58:00 UTC (32,744 KB) [v3] Fri, 9 Jan 2026 11:16:38 UTC (18,014 KB) [v4] Tue, 14 Apr 2026 04:16:16 UTC (18,396 KB) Full-text links: Access Paper: View a PDF of the paper titled iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception, by Jishnu Jaykumar P and 4 other authors View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files ( details ) : iTeach_supp.pdf Current browse context: cs.RO < prev | next > new | recent | 2024-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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