Enhancing Sample Efficiency and Uncertainty Compensation in Learning-based Model Predictive Control for Aerial Robots

Understand

The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems.

• Despite attaining substantial performance improvements over their non-learning counterparts, many of these frameworks rely on an offline learning procedure to synthesize a dynamics model.
• This implies that uncertainties encountered by the robot during deployment are not accounted for in the learning process.
• On the other hand, learning-based MPC methods that learn dynamics models online are computationally expensive and often require a significant amount of data.