arXiv AI
Task-Specified Active Metrological Inspection with Measurement-Steered VLA Manipulation and Deterministic Evidence Gating
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arXiv Computer Vision
Sep 18
INSPECT: Learning Robot View Selection from Assistant Use
INSPECT is a system that learns how a robot should choose its camera view during assembly inspection by observing a smart‑glasses assistant that answers part queries and guides the user. It uses techniques such as Presence‑Invariant TwinSwap for object evidence calibration, claim‑indexed supervision to separate evidence needs from camera changes, and object‑centered calibration to adapt view preferences to robot poses. In experiments on gearbox assemblies and angle‑grinder recordings, INSPECT outperforms other non‑oracle policies, improving view utility and decision accuracy.
Hugging Face Trending Papers
Aug 24
RACO: Reliability-Aware Coarse-Goal Optimization for Inspection-Oriented UAV Vision-Language Navigation
UAV vision-language navigation (UAV-VLN) is commonly evaluated as goal reaching, but inspection-oriented deployment requires the agent to stop within a valid inspection region and avoid falsely confir...
arXiv AI
Aug 25
RACO: Reliability-Aware Coarse-Goal Optimization for Inspection-Oriented UAV Vision-Language Navigation
The paper introduces RACO, a reliability‑aware adaptive coarse‑to‑fine navigation framework for inspection‑oriented UAV vision‑language navigation. It treats the coarse goal as a runtime hypothesis, using object‑level anchors to correct localization before and at the transition to the fine stage, and applies scale‑adaptive terminal refinement for near‑miss cases. RACO is evaluated on the new LG‑UVI inspection setting and outperforms the HETT baseline by 9.53 and 7.98 percentage points on validation‑unseen and test‑unseen, respectively, while improving inspection‑region arrival and reducing false verification risk.
arXiv AI
Aug 18
DeepInsight II: One Trace from Benchmark to Robot
arXiv:2608. 16556v1 Announce Type: new Abstract: Across a Physical AI stack, evaluation maturity is inversely aligned with deployment risk: foundation models enjoy mature, standardized harnesses, while the embodied layers on which deployment actually turns remain fragmented across benchmark-specific simulators, embodiments, and interfaces.
arXiv AI
Sep 3
Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework
The paper introduces TRACE, a Transparent Reasoning Architecture for Credible Execution, which provides an explainable AI-based decision framework for autonomous robots. TRACE structures decision-making into four auditable layers—Semantic Perception, Belief Reasoning, Action Synthesis, and Execution Verification—to ensure every action can be traced back to sensor evidence through documented causal chains. Experimental results on warehouse robot navigation show high evidence traceability (98.6%), temporal continuity (99.0%), and decision reconstructability (98.1%) across 500 simulated decision cycles.
arXiv Machine Learning
Sep 22
When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads
arXiv:2609.22299v1 Announce Type: cross Abstract: When a robot faces unfamiliar physical conditions, a common approach is to collect evidence about what changed and adapt. For such diagnosis to impro...