GeoMerge-OPD: Balanced Skill Integration in Vision-Language-Action Models

Vision-language-action (VLA) models excel at individual robotic skills, yet integrating heterogeneous skills into a single policy remains a key challenge for general-purpose robotic control, especially in whole-body tasks with differing manipulation and locomotion requirements. Common multi-skill integration strategies, such as joint fine-tuning and parameter merging, can induce unintended actions during closed-loop execution, severely degrading individual skills. Task-conditioned analysis of expert updates reveals response energy concentrated in a few dominant directions and opposing expert responses along high-energy directions within a shared, task-relevant response subspace. This structure co-occurs with unintended action shifts after merging. We propose GeoMerge-OPD, combining response-space regularization, parameter merging, and on-policy distillation. During independent expert fine-tuning, G-isometry constrains update-response gains within task-activation subspaces. The expert parameters are then statically merged into a student with preliminary multitask capabilities. Fine-tuned task experts serve as teachers, providing velocity-field supervision at the environment observations and intermediate action-flow states visited by the student to calibrate residual behavioral deviations. Experiments with two VLA backbones on LIBERO, LIBERO-PRO, and SIMPLE show that GeoMerge-OPD achieves more balanced performance across skills, significantly outperforms joint fine-tuning and multitask reinforcement-learning baselines, and remains robust under perturbations.