Application of a model-based nonlinear attitude control for quadrotor UAVs

A quadrotor, a UAV equipped with four rotors, has an advantage of its maneuver, and has been used for various purposes. For this advantage, many researchers have studied a quadrotor's operation. An attitude control system for a quadrotor is one of the most important parts in order to improve the quadrotor's performance. This thesis developed a quadrotor testbed and applied a model-based nonlinear attitude control, originally designed for a space craft, to the quadrotor. In order to implement a nonlinear attitude control system, the dynamic model of the quadrotor is studied and the quadrotor's physical properties are characterized based on the model. Then, stability and agility of the nonlinear attitude control are validated by both simulations and experiments and its performance is compared with one of an conventional PID attitude control. Finally, this thesis proposes a computationally-e cient position estimator for the quadrotor's operation. The position estimator detects visual markers of an image from the camera, and computes the quadrotor's position. The position estimator was also evaluated by experiments

Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation

We tackle the problem of developing humanoid loco-manipulation skills with deep imitation learning. The difficulty of collecting task demonstrations and training policies for humanoids with a high degree of freedom presents substantial challenges. We introduce TRILL, a data-efficient framework for training humanoid loco-manipulation policies from human demonstrations. In this framework, we collect human demonstration data through an intuitive Virtual Reality (VR) interface. We employ the whole-body control formulation to transform task-space commands by human operators into the robot's joint-torque actuation while stabilizing its dynamics. By employing high-level action abstractions tailored for humanoid loco-manipulation, our method can efficiently learn complex sensorimotor skills. We demonstrate the effectiveness of TRILL in simulation and on a real-world robot for performing various loco-manipulation tasks. Videos and additional materials can be found on the project page: https://ut-austin-rpl.github.io/TRILL.Comment: Submitted to Humanoids 202