Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection

2026-07-17 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

An embodied active learning approach addresses the challenge of adapting computer vision object detectors to unknown environments under both robot navigation time and annotation budget constraints. This method selects informative robot trajectories and image samples to retrain the detector, explicitly targeting its failure cases. It utilizes spatial consistency to identify images with inconsistent labels, which are likely to provide the greatest improvement to the vision model. The approach was evaluated on large scenes from the AI2-THOR simulator and a real-world setup using a Boston Dynamics Spot robot with the YOLOv5 real-time object detector. Experimental results show that spatial inconsistency effectively guides the agent and selects relevant images without external supervision, achieving the highest detection accuracy under the same budget compared to several baselines. The project is open-source.

Key takeaway

For Robotics Engineers deploying object detectors in novel environments with tight annotation and navigation budgets, this embodied active learning approach offers a robust strategy. You should consider integrating spatial inconsistency checks into your robot's data collection and retraining pipeline to efficiently identify and address detector failure cases, maximizing accuracy with minimal resource expenditure.

Key insights

Embodied active learning uses spatial inconsistency to efficiently adapt object detectors in new environments with limited budgets.

Principles

• Spatial consistency identifies inconsistent labels.
• Targeting failure cases improves vision models.
• Budget constraints necessitate efficient sample selection.

Method

An embodied active learning agent selects robot trajectories and image samples, utilizing spatial consistency to identify images with inconsistent labels for detector retraining under navigation and annotation budgets.

In practice

• Adapt YOLOv5 to new robot environments.
• Guide robot navigation for data collection.
• Improve object detection with minimal annotation.

Topics

• Embodied Active Learning
• Object Detection
• Robot Navigation
• AI2-THOR Simulator
• Boston Dynamics Spot
• YOLOv5

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.