ICONS: Imitation CONStraints for robot collaboration

Skill imitation has been an important ability in human-robot collaboration since it allows expeditious robot teaching of new tasks never seen before. To mimic the human pose, inverse kinematic solvers have been used to endow kinematic structures with human-like motion. Nevertheless, these solutions tend to be formulated for a specific robot or task. To address this generalization issue, this work presents the ICONS framework for imitation constraints, which proposes a general formulation for pose imitation, paired with a computationally efficient solver, inspired by the FABRIK algorithm. Three versions of the solver were developed to optimize the presented constraints. To assess the performance of ICONS, two tasks were evaluated, an incision task, and an assembly task. Fifty demonstrations were collected for each task. We compared the performance of our method, using pose accuracy and occlusion, against the numerical solver baseline (FABRIK). Notably, the ICONS framework improved the pose accuracy by 58% and reduced the environment occlusion by 38%. Moreover, the computational efficiency of the ICONS framework was assessed. Results show that the proposed algorithm maintains the efficiency of the baseline, finding the target solution under 10 iterations.