SPECTRA-VLA: Base-Fine Hyperspectral Token Transfer at Frozen Vision-Language-Action Interfaces
RGB vision-language-action models struggle to distinguish waste objects that appear identical in color but differ spectrally. SPECTRA-VLA learns a compact, base-orthogonal spectral residual that enriches frozen visual tokens at their native width without retraining the visual encoder or action decoder. On the SpectralWaste dataset, the method improves category balanced accuracy across 86 held-out acquisition sessions while retaining within-category spectral shape better than category-level codes.
The paper proposes a carefully constrained adaptation pipeline that delivers robust classification gains on a hyperspectral waste-sorting dataset while keeping a pretrained VLA backbone frozen. Its strongest contribution is the disentangled experimental design, which shows that both spectral content and token-level composition matter for downstream decoder compatibility. However, the evaluation remains essentially a classification task on frozen logits with ground-truth masks; it does not demonstrate actual action generation or robot control, and some ablations suggest the accuracy gains do not strictly require the proposed projection step.
The session-held-out evaluation is extensive and the gains are consistent. The mechanism studies convincingly separate content from composition, the preservation of exact zero-injection identity is principled, and the spectral-recovery metric supports the claim that continuous residuals retain fine-grained variation beyond category compression.
Despite the VLA framing, the experiments do not assess action generation or manipulation; they measure object-category and proxy-state classification through frozen action-symbol logits using ground-truth target masks, which limits relevance to robotic deployment. Mask sensitivity is severe: with poor predicted masks (mean IoU 0.218), base-fine drops below the false-color base (0.362 versus 0.468), showing the method depends on accurate segmentation that is not solved here. Moreover, removing base-orthogonalization can improve recognition in some settings; Table I shows the unprojected ablation reaches 0.893 category bAcc versus 0.883 for SPECTRA-VLA, and under a common movement allowance in Table IV both methods reach 0.880, indicating that projection mainly regulates disturbance tails rather than driving accuracy.
The paper contrasts its frozen-insertion approach against direct replacement, high-anchor retargeting, pooled-token replacement, and category-posterior codes, showing that base-fine outperforms direct replacement and that own-token composition approximately doubles the target-specific recovery increment after decoding. However, comparisons to OmniVLA and MuseVLA remain qualitative, and there is no quantitative benchmark against other HSI adapters on the same VLA backbone.
Hyperparameters, layer definitions, loss terms, projection formulas, and optimization settings are documented in detail in the main text and supplement, and code is released at a public GitHub repository. Reproduction should be feasible because the authors provide precomputed frozen visual tokens, specific software versions, and grouped prediction files. The remaining barrier is the reliance on the SpectralWaste corpus and its ground-truth target masks; independent reproduction requires obtaining this dataset and its acquisition-session splits.
Hyperspectral imaging distinguishes RGB-similar objects, but a pretrained vision-language-action model needs a state its frozen decoder can use. SPECTRA-VLA provides a native-width spectral upgrade without retraining the visual encoder or action decoder. A learned continuous residual enriches each target token through its own normalized false-color reference; projection, norm control and validation-calibrated injection regulate the change. Across 86 held-out acquisitions, category balanced accuracy rises from 0.812 to 0.883. With identical residuals and scale, own-token addition improves reuse of an external category-derived probe on frozen decoder logits: 0.701 versus 0.590 with pooled replacement, at similar image-disjoint recognition. Five grouped tests covering the same 86 acquisitions recover within-category spectral shape with $R^2=0.802/0.337$ from continuous and posterior-code states. Category-matched spectral swaps further show that own-token composition approximately doubles the target-specific recovery increment after frozen decoding. Spectral enrichment and receiving-token preservation thus work together at the pretrained interface.
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