Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante

Autonomous robots require online trajectory planning capability to operate in the real world. Efficient offline trajectory planning methods already exist, but are computationally demanding, preventing their use online. In this paper, we present a novel algorithm called Guided Trajectory Learning that learns a function approximation of solutions computed through trajectory optimization while ensuring accurate and reliable predictions. This function approximation is then used online to generate trajectories. This algorithm is designed to be easy to implement, and practical since it does not require massive computing power. It is readily applicable to any robotics systems and effortless to set up on real hardware since robust control strategies are usually already available. We demonstrate the computational performance of our algorithm on flat-foot walking with the self-balanced exoskeleton Atalante.

Authors

• Alexis Duburcq
• Yann Chevaleyre
• Nicolas Bredèche
• Guilhem Boéris

Keywords

• Trajectory optimization
• Function approximation
• Task analysis
• Legged locomotion
• Exoskeleton
• Trajectory Planning
• Online Trajectory Planning
• Walking
• Robotic System
• Learning Trajectories
• Robust Control Strategy
• Complex Systems
• Optimization Problem
• Residual Error
• Standard Regression
• Inequality Constraints
• Equality Constraints
• Non-convex Problem
• Compact Set
• Sequence Of States
• Policy Learning
• Humanoid
• Trajectory Optimization Problem
• Trajectory Duration
• Penalty Factor
• Task Space
• Real Robot
• Expert Demonstrations
• Trajectory Generation
• Number Of Fitting Parameters
• Need For Accuracy
• Terminal Constraint