Problem
Research questions and friction points this paper is trying to address.
Robot View Selection
Assembly Inspection
Smart-glasses Assistant
INSPECT: Learning Robot View Selection from Assistant Use
🤖 AI Summary
为解决机器人在装配检查中确定部件是否正确安装的问题，提出INSPECT方法，通过学习智能眼镜助手的记录来优化机器人视角选择。
📝 Abstract
Robots inspecting an assembly must determine which parts are present and whether they are correctly installed. During egocentric assembly assistance, head motion and workpiece handling reveal evidence for these checks, while spoken state confirmations link observations to procedural outcomes. We introduce INSPECT, which learns robot view preferences from records of a smart-glasses assistant that answers part queries and provides next-step guidance. Presence-Invariant TwinSwap (PI-TwinSwap) calibrates object evidence through paired identity interventions. Claim-indexed supervision separates evidence requirements from camera-reproducible observation changes. Object-centered calibration adapts relative view preferences to robot poses, while clause-level screening checks predicted evidence. The robot selects views using only its current observation and known poses, without candidate images. Evaluation uses annotated assistant-video replay to simulate state feedback, without target-domain view labels for policy training. On images of physical gearbox assemblies, INSPECT achieves the highest view utility among the compared non-oracle policies and raises human-rated full verifiability from 34.8% to 41.7% compared with keeping the current view. On commercial angle-grinder recordings in IMPACT, the transferred relative-view selector increases the correct decision rate from 50.6% to 54.3% with a frozen perception head. The source code is available at https://github.com/Kratos-Wen/INSPECT.
Innovation
Methods, ideas, or system contributions that make the work stand out.
INSPECT
Presence-Invariant TwinSwap
Claim-indexed supervision
Object-centered calibration
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