Robotic laser profiling is widely used for dimensional verification and surface inspection, yet measurement fidelity is often dominated by sensor configuration rather than robot motion. Industrial profilers expose multiple coupled parameters, including sampling frequency, measurement range, exposure time, receiver dynamic range, and illumination, that are still tuned by trial-and-error; mismatches can cause saturation, clipping, or missing returns that cannot be recovered downstream. We formulate instruction-conditioned sensing parameter recommendation; given a pre-scan RGB observation and a natural-language inspection instruction, infer a discrete configuration over key parameters of a robot-mounted profiler. To benchmark this problem, we develop Instruct-Obs2Param, a real-world multimodal dataset linking inspection intents and multi-view pose and illumination variation across 16 objects to canonical parameter regimes. We then propose ScanHD, a hyperdimensional computing framework that binds instruction and observation into a task-aware code and performs parameter-wise associative reasoning with compact memories, matching discrete scanner regimes while yielding stable, interpretable, low-latency decisions. On Instruct-Obs2Param, ScanHD achieves 92.7% average exact accuracy and 98.1% average Win@1 accuracy across the five parameters, with strong cross-split generalization and low-latency inference suitable for deployment, outperforming rule-based heuristics, conventional multimodal models, and multimodal large language models. This work enables autonomous, instruction-conditioned sensing configuration from task intent and scene context, eliminating manual tuning and elevating sensor configuration from a static setting to an adaptive decision variable.

翻译：机器人激光轮廓测量广泛应用于尺寸验证和表面检测，但测量精度往往由传感器配置而非机器人运动主导。工业轮廓仪暴露多个耦合参数（包括采样频率、测量范围、曝光时间、接收器动态范围和光照），目前仍依赖试错法调节；参数失配会导致饱和度、截断或无法在下游恢复的缺失回波信号。我们提出指令条件感知参数推荐方法：基于预扫描RGB观测和自然语言检测指令，推断机器人轮廓仪关键参数的离散配置。为建立该问题的基准，我们开发了Instruct-Obs2Param——一个真实世界多模态数据集，将16个物体的检测意图、多视角位姿和光照变化与标准参数区间关联。进而提出ScanHD超维计算框架，将指令和观测绑定为任务感知编码，通过紧凑记忆执行参数关联推理，匹配离散扫描仪模式同时提供稳定、可解释、低延迟的决策。在Instruct-Obs2Param上，ScanHD在五个参数上达到92.7%平均精确匹配准确率和98.1%平均Win@1准确率，具备强跨分裂泛化能力和适合部署的低延迟推理，性能优于基于规则的启发式方法、传统多模态模型和多模态大语言模型。该工作实现了基于任务意图和场景上下文的自主指令条件感知配置，消除了手动调节，并将传感器配置从静态设置提升为自适应决策变量。