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Found 4 papers, 3 papers with code
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control
Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments.
Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking
In this paper, we present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience.
AutoTune: Controller Tuning for High-Speed Flight
To answer the first question, we study the relationship between parameters and performance and find out that the faster the maneuver, the more sensitive a controller becomes to its parameters.
Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks
The proposed approach achieves cutting-edge results without the need of training the models with real annotated data of human body parts.
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