# Soma dataset

Soft robotic grasping lacks multimodal data that coordinates vision, depth, touch, inertial measurement and text, and existing alignment methods apply one strength to every modality pair. SomaAlign first estimates each modality's relative reliability from masked reconstruction, then uses it to set a pair-specific contrastive temperature, and in a third stage tightens intra-sample cross-modal consistency with conflict-aware constraints. Across 30 directed cross-modal retrieval tasks on the Soma dataset the mean Recall@10 is 90.66%, and on the independently collected Soma-Success set, excluded from representation learning, frozen encoders with lightweight probes reach 94.59% post-hoc grasp-outcome classification accuracy.

Soft robotic grasping lacks multimodal data that coordinates vision, depth, touch, inertial measurement and text, and existing alignment methods apply one strength to every modality pair. SomaAlign first estimates each modality's relative reliability from masked reconstruction, then uses it to set a pair-specific contrastive temperature, and in a third stage tightens intra-sample cross-modal consistency with conflict-aware constraints. Across 30 directed cross-modal retrieval tasks on the Soma dataset the mean Recall@10 is 90.66%, and on the independently collected Soma-Success set, excluded from representation learning, frozen encoders with lightweight probes reach 94.59% post-hoc grasp-outcome classification accuracy.