Industrial Dual-Arm Box Handling via Online Inertial Estimation and Convex Wrench Optimization

Industrial robotic object handling often involves boxes and packages 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-handling framework for objects with unknown inertial properties. The proposed approach estimates the object mass and center of mass online from measured contact wrenches, and 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.

Learning and generalization of robotic dual-arm manipulation of boxes from demonstrations via Gaussian Mixture Models (GMMs)

Learning from demonstration (LfD) is an effective method to teach robots to move and manipulate objects in a human-like manner. This is especially true when dealing with complex robotic systems, such as those with dual arms employed for their improved payload capacity and manipulability. However, a key challenge is in expanding the robotic movements beyond the learned scenarios to adapt to minor and major variations from the specific demonstrations. In this work, we propose a learning and novel generalization approach that adapts the learned Gaussian Mixture Model (GMM)-parameterized policy derived from human demonstrations. Our method requires only a small number of human demonstrations and eliminates the need for a robotic system during the demonstration phase, which can significantly reduce both cost and time. The generalization process takes place directly in the parameter space, leveraging the lower-dimensional representation of GMM parameters. With only three parameters per Gaussian component, this process is computationally efficient and yields immediate results upon request. We validate our approach through real-world experiments involving a dual-arm robotic manipulation of boxes. Starting with just five demonstrations for a single task, our approach successfully generalizes to new unseen scenarios, including new target locations, orientations, and box sizes. These results highlight the practical applicability and scalability of our method for complex manipulations.