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.