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Combining Learning-based and Sampling-based Approaches for Manipulation Planning in Unstructured and Cluttered Settings

   Cardiff School of Computer Science & Informatics

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  Dr Juan Hernandez Vega, Dr Z Ji  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Grasping and manipulating objects are some of the basic and most common tasks that a robot (such as manipulator arms or mobile manipulators) must do when collaborating with human partners. While it is considered a simple task for humans, sometimes it could be a challenging robot operation. For a robot, grasping an object normally involves perception to detect and determine the grasp affordances of the object [1], as well as planning the motion to approach and grasp the object. With the increasing popularity of machine learning techniques, new frameworks/libraries have been proposed to both automatically detect the best grasping affordances and plan motions to reach the object.

 While such learning-based approaches have considerably improved the response time to determine the grasp affordances, they might still fail to plan feasible motions in situations with characteristics that were not fully represented within the training sets.

To cope with this limitation, we propose to combine and benefit from both model-based planning (e.g., sampling-based) and learning-based approaches. To do so, a learning-based framework can be used to detect the best object grasp affordances, while a sampling-based planner that attempts to reach the object can further refine and learn which affordances are feasible according to the robot motion capabilities. As a more specific example, a computer vision pipeline can be used to detect and learn the best grasping poses of an object that might be surrounded by obstacles in cluttered settings. The output of the pipeline is one or more grasping regions around the object or, alternatively, a set of grasping poses. Such grasping regions or poses will be tested by an off-the-shelf motion planner [2], which will inform the grasping feasibility, i.e., whether the provided grasping regions or poses is doable for a robot.

The project will need to research about existing works in grasping poses detection, and how they can be effectively used in conjunction with the robot motion planner. We aim to contribute a new hybrid manipulation approach that combines novel learning-based techniques and sampling-based motion planners. The results of this project are expected to be validated with KUKA LBR iiwa arms or a mobile manipulator system, both available at Cardiff University. This project will also benefit from existing international collaborations with other well-known research institutions (e.g., Rice University in Houston, TX).

For more information about the project, please contact Dr Juan D. Hernandez Vega

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.

Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.

How to apply:

Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below

Please submit your application before the application deadline 29th April 2022 via Computer Science and Informatics - Study - Cardiff University

In order to be considered candidates must submit the following information: 

  • Supporting statement 
  • CV 
  • In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
  • In the funding field of your application, insert “I am applying for 2022 PhD Scholarship in Computer Science and Informatics”, and specify the project title and supervisors of this project in the text box provided.
  • Qualification certificates and Transcripts
  • References x 2 
  • Proof of English language (if applicable)

Interview - If the application meets the entrance requirements, you will be invited to an interview

If you have any questions or need more information, please contact [Email Address Removed]

Funding Notes

A School-Funded PhD Scholarship is available for entry 2022/23.
In the Funding field of your application, insert "I am applying for 2022 PhD Scholarship" and specify the project title and supervisor of this project in the fields provided.
This project is also open to Self-Funded students worldwide. If you are interested in applying for a Self-Funded PhD, please search FindAPhD for this specific project title, supervisor or School within its Scholarships category.


[1] Ardón, P.; Pairet È.; Petrick, R.; Ramamoorthy, S.; and Lohan, K. S. "Self-Assessment of Grasp Affordance Transfer". IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). October 2020.
[2] Hernández, J. D.; Moll, M.; and Kavraki, L. E. “Lazy evaluation of goal specifications guided by motion planning,” in IEEE International Conference on Robotics and Automation (ICRA), 2019, pp. 944–950.

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