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Weixuan Zhang

6 accepted papers

2023

Learning to Open Doors with an Aerial Manipulator

IROS 2023poster

The field of aerial manipulation has seen rapid advances, transitioning from push-and-slide tasks to interaction with articulated objects. The motion trajectory of these complex actions is usually hand-crafted or a result of online optimization methods like Model Predictive Control (MPC) or Model Pr…

Cited by 4SourceScholar
2022

Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing

RA-L 2022

The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection. These tasks require the robotic system to exchange forces with partially-known environments, which may contain uncertainties including un

Cited by 28SourceScholar
2021

Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots

ICRA 2021poster

Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the eff…

Cited by 11SourceScholar
2020

Learning Dynamics for Improving Control of Overactuated Flying Systems

RA-L 2020

Overactuated omnidirectional flying vehicles are capable of generating force and torque in any direction, which is important for applications such as contact-based industrial inspection. This comes at the price of an increase in model complexity. These vehicles usually have non-negligible, repetitiv

Cited by 14SourceScholar
2020

Trajectory Tracking Nonlinear Model Predictive Control for an Overactuated MAV

ICRA 2020poster

This work presents a method to control omnidirectional micro aerial vehicles (OMAVs) for the tracking of 6-DoF trajectories in free space. A rigid body model based approach is applied in a receding horizon fashion to generate optimal wrench commands that can be constrained to meet limits given by th…

Cited by 41SourceScholar