Toward Self-supervised Learning of Robotic Manipulation Tasks
Talk by Professor Abdeslam Boularias
Complex manipulation tasks combine low-level sensorimotor primitives, such as grasping, pushing, and simple arm movements, with high-level reasoning skills, such as deciding which object to grasp next and where to place it. While low-level sensorimotor primitives have been extensively studied in robotics, learning how to perform high-level task planning is relatively less explored. In this talk, I will present a unified framework for learning both low and high-level skills in an end-to-end manner from visual demonstrations of tasks performed by humans. The focus is on tasks that require manipulating several objects in sequence. The presented new techniques not only enhance current robotic capabilities but also set the stage for future advancements where robots can autonomously perform complex tasks in dynamic environments, further closing the gap between human and robotic task execution.
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- Date: 23 Apr 2024
- Time: 03:20 PM to 04:40 PM
- All times are (UTC-04:00) Eastern Time (US & Canada)
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- Starts 20 April 2024 12:00 AM
- Ends 23 April 2024 03:00 PM
- All times are (UTC-04:00) Eastern Time (US & Canada)
- No Admission Charge
Speakers
Abdeslam Boularias of Rutgers University
Toward Self-supervised Learning of Robotic Manipulation Tasks
Complex manipulation tasks combine low-level sensorimotor primitives, such as grasping, pushing, and simple arm movements, with high-level reasoning skills, such as deciding which object to grasp next and where to place it. While low-level sensorimotor primitives have been extensively studied in robotics, learning how to perform high-level task planning is relatively less explored. In this talk, I will present a unified framework for learning both low and high-level skills in an end-to-end manner from visual demonstrations of tasks performed by humans. The focus is on tasks that require manipulating several objects in sequence. The presented new techniques not only enhance current robotic capabilities but also set the stage for future advancements where robots can autonomously perform complex tasks in dynamic environments, further closing the gap between human and robotic task execution.
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Address:United States