Towards Generalizable Associative Skill Memories

Associative Skill Memories (ASMs) were formulated to encode stereotypical movements along with their stereotypical sensory events to increase robustness of underlying dynamic movement primitives (DMPs) against noisy perception and perturbations. In ASMs, the stored sensory trajectories, such as the haptic and tactile measurements, are used to compute how much a perturbed movement deviates from the desired one, and to correct the movement if possible. In our work, we extend ASMs: rather than using stored single sensory trajectory instances, our system generates sensory event models, and exploits those models to correct the perturbed movements during executions with the aim of generalizing to novel configurations. In particular, measured force and the torque trajectories are modeled using Hidden Markov Models, and then reproduced by Gaussian Mixture Regression. With Baxter robot, we demonstrate that our proposed force feedback model can be used to correct a non-linear trajectory while pushing an object, which otherwise slips away from the gripper because of noise. At the end, we discuss how far this skill can be generalized using the force model and possible future improvements.