Robust Task Generalization for Dual-Arm Learning from Demonstration
Dual-arm manipulation or physical human-robot coordination requires robots to adapt rapidly to changing environments and constraints. Traditional Learning from Demonstration approaches struggle to generalize when faced with out-of-distribution scenarios, requiring costly retraining. We propose a Movement Primitive learning algorithm based on Gaussian Processes, combined with real-time zero-shot adaptation through Pathwise Conditioning. The method encapsulates the predictive uncertainty of the demonstrated movement using heteroscedastic GPs and utilizes an update via Matheron's rule to instantaneously adjust the trajectory to new via-points, without the need to retrain the underlying model. This formulation is extended to dual-arm coordination by dynamically calculating 6D relative constraints to maintain a closed kinematic chain. Experimental results, both in 2D comparisons against task-parameterized models and in tasks with the ADAM robot, demonstrate robust adaptation with near-zero error in real time, making it applicable for highly changing environments.