IPAB Workshop: Online Dynamic Trajectory Optimization and Control for a Quadruped Robot; Joint semantic segmentation and disparity learning

The next IPAB Workshop will take place 05/11/2020 at 1pm via Blackboard Collaborate - Oguzhan Cebe, Hanz Cuevas Velasquez will be speaking.

You will find the link in your emails.


Speaker: Oguzhan Cebe

Title: Online Dynamic Trajectory Optimization and Control for a Quadruped Robot

Abstract: Legged robot locomotion requires the planning of stable reference trajectories, especially while traversing uneven terrain. The   proposed trajectory optimization framework is capable of generating  dynamically stable base and footstep trajectories for    multiple steps.  The locomotion task can be defined with contact locations, base motion or both, making the algorithm   suitable for multiple scenarios (e.g., presence  of moving   obstacles). The planner uses a simplified momentum-based task space model for the robot dynamics, allowing computation times that are fast  enough for online  replanning. This fast planning capability also enables the quadruped to accommodate for drift and environmental  changes.


Speaker: Hanz Cuevas Velasquez

Title: Joint semantic segmentation and disparity learning

Abstract: Disparity estimation and semantic segmentation are two fundamental problems in computer vision. The goal of disparity estimation is to obtain the corresponding pixels from a rectified pair of images. On the other hand, semantic segmentation is used to assign class labels to each pixel in the image. These two methods are highly used in scene understanding, autonomous driving and robotics. However, executing the state-of-the-art methods of both areas together involves a long computation run-time, which is prohibitively expensive in these systems. Therefore, a method that combines both tasks into a single model reduces computation and allows it to be embedded in portable devices or executed in real-time. We approach this problem through multi-task learning, our method focuses on sharing the specific feature knowledge between tasks in a progressive manner, which improves learning efficiency and prediction accuracy for each task.

Thursday, 5 November, 2020 - 13:00 to Friday, 6 November, 2020 - 13:45
Oguzhan Cebe, Hanz Cuevas Velasquez
University of Edinburgh