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This paper introduces a method for automatically configuring robotic laser profilers based on natural language inspection instructions and pre-scan RGB observations. They formulate instruction-conditioned sensing parameter recommendation and create a real-world dataset, Instruct-Obs2Param, to benchmark this problem. The proposed ScanHD framework, based on hyperdimensional computing, binds instruction and observation into a task-aware code, enabling parameter-wise associative reasoning and achieving high accuracy and low-latency inference compared to other methods.
Forget tedious manual tuning: ScanHD lets robots autonomously configure laser profilers based on natural language instructions and visual context, achieving >92% accuracy in real-world inspection tasks.
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.
