Location
Tampa
Start Date
19-5-2023 11:00 AM
End Date
19-5-2023 11:20 AM
Document Type
Full Paper
Keywords
ADL, teleoperation, PbD, Object recognition, trajectory planning, obstacle avoidance, manipulation, machine-learning techniques
Description
Wheelchair-mounted robotic arms are used in rehabilitation robotics to help physically impaired people perform ADL (Activity of daily living) tasks. However, the dexterity of manipulation tasks makes the teleoperation of the robotic arm challenging for the user, as it is difficult to control all degrees of freedom with a handheld joystick or a screen touch device. PbD (Programming by demonstration) allows the user to demonstrate the desired behavior and enables the system to learn from the demonstrations and adapt to a new environment. This learned model can perform a new set of actions in a new environment. Learning from a demonstration includes Object identification and recognition, trajectory planning, Obstacle avoidance, and adapting to a new environment, wherever necessary. PbD using a learning-based approach learns the task through a model that captures the underlying structures of the task. The model can be a probabilistic graphical model, a neural network, or a combination of both. PbD with learning can be generalized and applied to new situations as this method enables the robot to learn the model rather than just memorizing and imitating the demonstration. In addition to this, it also helps in efficient learning with a reduced number of demonstrations. This survey focuses on an overview of the recent machine learning techniques used with PbD to perform dexterous manipulation tasks that enable the robot to learn from and apply it to a new set of tasks and a new environment.
DOI
https://doi.org/10.5038/IMRT5980
Included in
Programming by demonstration using learning based approach: A Mini review
Tampa
Wheelchair-mounted robotic arms are used in rehabilitation robotics to help physically impaired people perform ADL (Activity of daily living) tasks. However, the dexterity of manipulation tasks makes the teleoperation of the robotic arm challenging for the user, as it is difficult to control all degrees of freedom with a handheld joystick or a screen touch device. PbD (Programming by demonstration) allows the user to demonstrate the desired behavior and enables the system to learn from the demonstrations and adapt to a new environment. This learned model can perform a new set of actions in a new environment. Learning from a demonstration includes Object identification and recognition, trajectory planning, Obstacle avoidance, and adapting to a new environment, wherever necessary. PbD using a learning-based approach learns the task through a model that captures the underlying structures of the task. The model can be a probabilistic graphical model, a neural network, or a combination of both. PbD with learning can be generalized and applied to new situations as this method enables the robot to learn the model rather than just memorizing and imitating the demonstration. In addition to this, it also helps in efficient learning with a reduced number of demonstrations. This survey focuses on an overview of the recent machine learning techniques used with PbD to perform dexterous manipulation tasks that enable the robot to learn from and apply it to a new set of tasks and a new environment.