Trajectory-Calibrated Merging of Embodied Policies

Robot foundation models are increasingly adapted into task specialists to expand their embodied capabilities. Yet these capabilities remain fragmented across separate policies, and existing model-merging methods do not reliably preserve them during closed-loop execution: parameter-space alignment provides only an indirect proxy for behavior, while regression may fail to preserve behavior at states not covered by its calibration inputs. We present Trajectory-Calibrated Regression (TCR), an execution-aware, gradient-free framework that consolidates robot specialists into a single policy. Across two complementary timescales, TCR collects calibration inputs from expert rollouts spanning different stages of task execution and from intermediate states within iterative action generation. To account for representation shifts introduced by merging, TCR recomputes each block's input through the already merged blocks. It then uses each expert's computation within the block to form module inputs and fits merged weights to match the experts' linear outputs. A two-pass calibration procedure further refines the merged policy using separate rollout caches while retaining the frozen specialists as teachers. The resulting model preserves the original inference graph and requires neither expert routing nor additional gradient training. On LIBERO, TCR merges four ${\pi }_{0.5}$ specialists and achieves 77.83% success across 1,200 evaluation episodes, outperforming FeatCal, RegMean, and direct weight averaging by 9.50, 24.17, and 33.00 percentage points, respectively. We also find that continued training from TCR reaches a higher observed peak success of 97.75%, compared with 94.00% when training from the common base. These results show that calibrating model merging around execution dynamics and evolving internal representations provides an effective path toward preserving diverse embodied skills in a unified policy. Code, experimental results, and visualizations are available at https://anonymous.4open.science/r/vla-merge-6333/README.md.