← Search

Jiangtao Hu

5 accepted papers

2024

Spined Torso Renders Advanced Mobility for Quadrupedal Locomotion

ICRA 2024poster

Animals possessing spinal columns often exhibit exceptional agility for highly dynamic locomotion. The spine grants the trunk with increased degrees of freedom, thereby endowing diverse postures. This paper presents the development of a robot STRAY for quadrupedal locomotion, featuring a four-degree…

Cited by 2SourceScholar
2024

Torque Ripple Reduction in Quasi-Direct Drive Motors Through Angle-Based Repetitive Learning Observer and Model Predictive Torque Controller

IROS 2024poster

Torque ripple reduction in quasi-direct drive (QDD) motors is crucial in their robotic applications for dynamic locomotion and dexterous manipulation. In this paper, we present a novel approach for reducing torque ripples of QDD motors, which integrates an angle-based repetitive learning observer (A…

Cited by 0SourceScholar
2021

Autonomous Driving Trajectory Optimization With Dual-Loop Iterative Anchoring Path Smoothing and Piecewise-Jerk Speed Optimization

RA-L 2021

This letter presents a free space trajectory optimization algorithm for autonomous driving, which decouples the collision-free trajectory generation problem into a Dual-Loop Iterative Anchoring Path Smoothing (DL-IAPS) problem and a Piecewise-Jerk Speed Optimization (PJSO) problem. The work leads to

Cited by 66SourceScholar
2020

Lane-Attention: Predicting Vehicles’ Moving Trajectories by Learning Their Attention Over Lanes

IROS 2020poster

Accurately forecasting the future movements of surrounding vehicles is essential for safe and efficient operations of autonomous driving cars. This task is difficult because a vehicle's moving trajectory is greatly determined by its driver's intention, which is often hard to estimate. By leveraging…

Cited by 50SourceScholar
2019

An Automated Learning-Based Procedure for Large-scale Vehicle Dynamics Modeling on Baidu Apollo Platform

IROS 2019poster

In the autonomous driving industry, vehicle dynamic models are important to control-in-the-loop simulations. For current commercial self-driving simulators, vehicle dynamic models are expressed explicitly by sophisticated analytical equations, which are accurate but difficult to build and expensive…

Cited by 60SourceScholar