Dual Quaternion Based Compliant Movement Primitives for Deformable Object Manipulation

Abstract

Learning from demonstration effectively transfers human manipulation skills to robots. It can be especially useful for imitating industrial manipulation tasks which are performed by humans and are difficult to model such as deformable object manipulation. Manipulation of deformable objects often requires not only accurate tracking of the demonstration trajectory using a robot end-effector, but also the accommodation of interaction forces. Precise tracking of such trajectories while ignoring these interaction forces leads to overly stiff, unsafe, or unsuccessful executions. We address this problem by proposing Dual Quaternion based Compliant Movement Primitives (DQ-CMP). DQ-CMP couples a dual-quaternion based Dynamic Movement Primitive for compact 6-DoF pose encoding with learnable wrench primitives. This combination reproduces synchronized motion and force behaviors directly at the end-effector. The method is robot-agnostic and singularity-free at the representation-level, as it operates in operational space using dual quaternions. From a few demonstrations, required wrenches for unseen initial configurations are predicted using Gaussian process regression defined on the pose manifold. This enables generalization of the learned wrenches across different starting poses. We validate the method on real-robot experiments including a shoe-sole detachment for recycling and bending of stiff foam inside a box. Results show compliant, safe task execution and successful generalization to new initial poses.