Task-Aware Scanning Parameter Configuration for Robotic Inspection Using Vision Language Embeddings and Hyperdimensional Computing

Problem
This preprint addresses the gap in automated sensor configuration for robotic laser profiling, which is critical for dimensional verification and surface inspection. Current practices rely heavily on trial-and-error methods for tuning multiple coupled parameters (e.g., sampling frequency, measurement range, exposure time), leading to issues such as saturation, clipping, or missing returns. The authors propose a novel approach to automate this tuning process by leveraging instruction-conditioned sensing parameter recommendations based on RGB observations and natural language instructions.

Method
The core technical contribution is the development of ScanHD, a hyperdimensional computing framework that integrates vision-language embeddings to facilitate task-aware parameter configuration. The authors introduce the Instruct-Obs2Param dataset, which consists of multimodal data linking inspection intents with multi-view pose and illumination variations across 16 objects. ScanHD encodes both the instruction and observation into a hyperdimensional representation, enabling parameter-wise associative reasoning. The model is trained to predict discrete configurations for five key parameters, utilizing compact memory structures for efficient inference. The training compute details are not disclosed, but the framework is designed for low-latency decision-making suitable for real-time deployment.

Results
On the Instruct-Obs2Param dataset, ScanHD achieves an average exact accuracy of 92.7% and an average Win@1 accuracy of 98.1% across the five parameters. These results demonstrate strong cross-split generalization capabilities, significantly outperforming rule-based heuristics, conventional multimodal models, and multimodal large language models. The effect sizes indicate a substantial improvement in accuracy, suggesting that the proposed method effectively captures the complex relationships between inspection tasks and sensor configurations.

Limitations
The authors acknowledge that the dataset may not encompass all possible inspection scenarios, which could limit the generalizability of the model in diverse real-world applications. Additionally, the reliance on hyperdimensional computing may introduce challenges in interpretability and scalability when applied to more complex tasks or larger parameter spaces. The paper does not address potential biases in the dataset or the implications of deploying the model in untested environments.

Why it matters
This work has significant implications for the field of robotic inspection, as it transitions sensor configuration from a static, manual process to an adaptive, instruction-driven decision-making framework. By automating the tuning of sensing parameters based on task intent and scene context, the proposed method enhances the fidelity of robotic inspections and reduces the need for human intervention. This advancement could lead to more efficient and reliable inspection processes in various industrial applications, paving the way for further research into autonomous robotic systems that can adapt to dynamic environments.

Authors: Zhiling Chen, David Gorsich, Matthew P. Castanier, Yang Zhang, Jiong Tang, Farhad Imani
Source: arXiv:2605.03909
URL: https://arxiv.org/abs/2605.03909v1