Probabilistic Movement Primitives


Movement primitives (MPs) are a well-established approach for representing interchangeable and reusable generators of robot movement. They provide a condense parameterization of a robot's control policy. In robot learning, multiple MPs may be combined as building blocks in a modular control architecture to solve complex tasks. They can be blended between motions, adapted to altered task variables, and co-activated in parallel. Many robot learning successes are based on MPs due to their ability to accurately approximate continuous and high-dimensional movements. For example, MPs have been employed in learning from demonstration using imitation learning. Probabilistic movement primitives (ProMPs) are a concept whereby a distribution of trajectories is learned from multiple demonstrations. They provide an effective approach to learning flexible trajectories from demonstration.


  • We designed a ProMP controller that guarantees the system never leaves a neighborhood defined by the training set, and it provides a simple way to define trajectories that enforce safety constraints for manipulation tasks
  • We combined model predictive control and control barrier functions to guide a robot along a predefined trajectory while guaranteeing it always maintains a desired distance from a human motion distribution defined by a ProMP
  • We developed a novel means of automating the design of CLFs and CBFs from the distribution delivered by a ProMP and demonstrated its practical applicability through experimental validation on a Universal Robots UR5e
  • We created a safe control design approach that takes demonstrations provided by a human teacher to enable a robot to accomplish complex manipulation scenarios in dynamic environments


  1. M. Davoodi, A. Iqbal, J.M. Cloud, W.J. Beksi, and N.R. Gans, "Probabilistic Movement Primitive Control via Control Barrier Functions," International Conference on Automation Science and Engineering (CASE), 2021.
    Best Conference Paper Award Finalist  •   Paper  •   Citation
  2. M. Davoodi, J.M. Cloud, A. Iqbal, W.J. Beksi, and N.R. Gans, "Safe Human-Robot Coetaneousness Through Model Predictive Control Barrier Functions and Motion Distributions," Modeling, Estimation, and Control Conference (MECC), 2021.
    Paper  •   Citation
  3. M. Davoodi, A. Iqbal, J.M. Cloud, W.J. Beksi, and N.R. Gans, "Safe Robot Trajectory Control using Probabilistic Movement Primitives and Control Barrier Functions," Frontiers in Robotics and AI, 2022.
    Paper  •   Citation
  4. M. Davoodi, A. Iqbal, J.M. Cloud, W.J. Beksi, and N.R. Gans, "Rule-Based Safe Probabilistic Movement Primitive Control via Control Barrier Functions," IEEE Transactions on Automation Science and Engineering, 2022.
    Paper  •   Citation