Model-free Probabilistic Movement Primitives for physical interaction

Physical interaction in robotics is a complex problem that requires not only accurate reproduction of the kinematic trajectories but also of the forces and torques exhibited during the movement. We base our approach on Movement Primitives (MP), as MPs provide a framework for modelling complex movements and introduce useful operations on the movements, such as generalization to novel situations, time scaling, and others. Usually, MPs are trained with imitation learning, where an expert demonstrates the trajectories. However, MPs used in physical interaction either require additional learning approaches, e. g. , reinforcement learning, or are based on handcrafted solutions. Our goal is to learn and generate movements for physical interaction that are learned with imitation learning, from a small set of demonstrated trajectories. The Probabilistic Movement Primitives (ProMPs) framework is a recent MP approach that introduces beneficial properties, such as combination and blending of MPs, and represents the correlations present in the movement. The ProMPs provides a variable stiffness controller that reproduces the movement but it requires a dynamics model of the system. Learning such a model is not a trivial task, and, therefore, we introduce the model-free ProMPs, that are learning jointly the movement and the necessary actions from a few demonstrations. We derive a variable stiffness controller analytically. We further extent the ProMPs to include force and torque signals, necessary for physical interaction. We evaluate our approach in simulated and real robot tasks.

Authors

• Alexandros Paraschos
• Elmar Rueckert
• Jan Peters 0001
• Gerhard Neumann

Keywords

• Robot sensing systems
• Trajectory
• Probabilistic logic
• Dynamics
• Robot kinematics
• Force
• Physical Interaction
• Movement Primitives
• Probabilistic Movement Primitives
• Dynamic Model
• Kinematic
• Real Task
• Imitation Learning
• Degrees Of Freedom
• System State
• Nonlinear Systems
• Feedback Control
• Weight Vector
• Joint Distribution
• Sensory Signals
• Gaussian Mixture Model
• Contact Force
• Joint Position
• Proportional-integral-derivative
• Tactile Sensor
• Linear Control
• Model-free Approach
• Distribution Of Trajectories
• Probable Trajectory
• Number Of Basis Functions
• Stochastic Control
• Model-free Control
• Force Feedback
• System Dynamics
• Physical Tasks
• Corrective Actions