Bayesian Gaussian mixture model for robotic policy imitation

Abstract

A common approach to learn robotic skills is to imitate a demonstrated policy. Due to the compounding of small errors and perturbations, this approach may let the robot leave the states in which the demonstrations were provided. This requires the consideration of additional strategies to guarantee that the robot will behave appropriately when facing unknown states. We propose to use a Bayesian method to quantify the action uncertainty at each state. The proposed Bayesian method is simple to set up, computationally efficient, and can adapt to a wide range of problems. Our approach exploits the estimated uncertainty to fuse the imitation policy with additional policies. It is validated on a Panda robot with the imitation of three manipulation tasks in the continuous domain using different control input/state pairs.

Cited by in corpus (7)

• Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects
• Memory of Motion for Warm-starting Trajectory Optimization
• Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series
• Gaussian-Process-based Robot Learning from Demonstration
• A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing
• Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration
• Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach