Variable duration movement encoding with minimal intervention control

Programming by Demonstration (PbD) offers a user-friendly way to transfer skills from human to robot. Typically, demonstration data do not contain the control inputs required to reproduce the demonstrated skill. These can be obtained from a low-level controller that tracks the modeled movement. We present a PbD approach for minimal intervention control — a control strategy that only corrects perturbations that interfere with task performance. The novelty of our approach is the probabilistic encoding of the movement duration, providing a performance measure that enables minimal intervention control in a temporal sense. This is achieved by combining a probabilistic movement encoding based on Hidden Semi-Markov Model (HSMM) with Model Predictive Control (MPC). The probabilistic model is used to construct an objective function, hereby assuming that variance is a measure for task performance. The proposed method is demonstrated in a robot experiment and compared with our earlier work.

Authors

• Martijn J. A. Zeestraten
• Sylvain Calinon
• Darwin G. Caldwell

Keywords

• Hidden Markov models
• Robots
• Encoding
• Probabilistic logic
• Linear programming
• Predictive models
• Optimal control
• Minimal Intervention
• Minimal Intervention Control
• Control Strategy
• Objective Function
• Task Performance
• Probabilistic Model
• Model Predictive Control
• Movement Duration
• Robot Experiments
• Temporal Sense
• Time Step
• System State
• Mean Duration
• Reference Frame
• Hidden Markov Model
• Additional Constraints
• Interaction Forces
• Start Position
• Gaussian Mixture Model
• Joint Space
• Sequence Of States
• Demonstration Of Skills
• Linear Quadratic Regulator
• Task Parameters
• Temporal Signal
• State Prediction
• Task Space
• Regularized Least Squares
• Prediction Horizon
• Local Frame