Dual-arm robotic manipulation often requires lifting and transporting objects whose mass and center of mass are not known in advance. These uncertainties affect the force-moment balance required for stable lifting, and improper regulation of contact wrenches can lead to slip, object drop, orientation deviation, or excessive squeezing. This paper presents a friction-aware dual-arm box-manipulation framework for objects with unknown inertial properties. The framework combines online inertial estimation with convex contact-wrench optimization. The inertial estimation module estimates the object mass and center of mass from measured contact wrenches, while the optimization module computes friction-feasible contact forces and torsional moments through a second-order cone program (SOCP) under ellipsoidal friction-limit-surface constraints. An offline trajectory refinement stage is also included to reduce undesired object-environment contact when geometric constraints are present. By enforcing friction feasibility as a hard constraint and minimizing contact effort within the feasible region, the framework achieves stable lifting without treating slip avoidance and excessive squeezing as separately tuned objectives. Experiments on a real dual-arm robotic system under different center-of-mass configurations demonstrate that the method lifts objects with unknown inertial properties while maintaining stable frictional contact.

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