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AIgorithms in Robotics for Task and Motion Planning
Description
Computational Robotics: Multiple positions for undergraduate researchers on computational research in robotics are available at the School of Computing, ANU.
Principal Investigator:
Rahul Shome,
Tenure Track Lecturer, School of Computing, The Australian National University.
Contact: For more details, please email me at
rahul.shome@anu.edu.au .
My office is CSIT 108 N315 if you want to drop by for a chat.
You will find my past work and several motivating videos and publications at my website
https://rahulsho.me
Goals
Majority of the project will focus on developing algorithmic frameworks for task and motion planning. Task and motion planning refers to category of algorithms that robots use to compute actions and motions to solve tasks. For more information about the intuition behind task and motion planning, try out this online demo at https://rahulsho.me/tamp
Requirements
Programming experience in
C++ and Python is recommended.
(Optional) Any exposure to the following
• AI techniques,
• heuristic search,
• algorithms
• and data structures
will be a plus.
(Optional) Mathematical background in the following
• discrete math,
• optimization, or
• differential geometry
can also be relevant.
If you tick any of these boxes and find robots interesting, this project might be for you.
Background Literature
Robotic Planning Algorithms: Visit my website for a detailed list of research relevant publications https://rahulsho.me/pubs
There you will find an overview of the different elements of robotics that introduce interesting variants of the planning problem. Before that, it will be important to understand what planning is---deliberative processes that find a sequence of valid goal directed steps. In robotics, I am specifically interested in task and motion planning algorithms that can apply to collaborative scenarios involving teams of robots and even humans.
The various aspects of robotics that will be relevant (depending upon the interest of the student only a subset of these will be covered)
• motion planning
• multi-robot planning
• object rearrangement
• manipulation planning
• task and motion planning,
• sampling-based planning
Gain
Exposure to State of the Art
The algorithms within the project will initially involve development in C++ and expose the student to bleeding edge software and tools in Robotics, AI, and Computer Vision like
• Robot Operating System,
• Gazebo,
• pyBullet,
• Open Motion Planning LIbrary,
• Point Cloud Library,
• OpenCV,
• PDDL,
and others.
Robotics is inherently very interdisciplinary and initial stages of the projects will involve simulators where algorithms can be evaluated. These tools are of interest to employers who often use these or similar tools within their robotic automation pipelines.
The student will also gain research expertise in
• sampling-based planning algorithms,
• high-dimensional search strategies,
and depending upon the interests of the student also explorations of
• stochastic processes,
• topology,
• optimization,
• AI planning,
• and other areas.
Keywords
robotics, artificial intelligence, planning, task and motion planning, algorithms
