Enhancing Capabilities of Assistive Robotic Arms: Learning, Control, and Object Manipulation
| dc.contributor.author | Mehta, Shaunak A. | en |
| dc.contributor.committeechair | Losey, Dylan P. | en |
| dc.contributor.committeemember | Bartlett, Michael D. | en |
| dc.contributor.committeemember | Hamed, Kaveh Akbari | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2024-12-05T15:49:23Z | en |
| dc.date.available | 2024-12-05T15:49:23Z | en |
| dc.date.issued | 2024-11-11 | en |
| dc.description.abstract | In this thesis, we explore methods to enable assistive robotic arms mounted on wheelchairs to assist disabled users with their daily activities. To effectively aid users, these robots must recognize a variety of tasks and provide intuitive control mechanisms. We focus on developing techniques that allow these assistive robots to learn diverse tasks, manipulate different types of objects, and simplify user control of these complex, high-dimensional systems. This thesis is structured around three key contributions. First, we introduce a method for assistive robots to autonomously learn complex, high-dimensional behaviors in a given environment and map them to a low-dimensional joystick interface without human demonstrations. Through controlled experiments and a user study, we show that this approach outperforms systems based on human-demonstrated actions, leading to faster task completion compared to industry-standard baselines. Second, we improve the efficiency of reinforcement learning for robotic manipulation tasks by introducing a waypoint-based algorithm. This approach frames task learning as a sequence of multi-armed bandit problems, where each bandit problem corresponds to a waypoint in the robot's trajectory. We introduce an approximate posterior sampling solution that builds the robot's motion one waypoint at a time. Our simulations and real-world experiments show that this approach achieves faster learning than state-of-the-art baselines. Finally, to address the challenge of manipulating a variety of objects, we introduce RIgid-SOft (RISO) grippers that combine soft-switchable adhesives with standard rigid grippers and propose a shared control framework that automates part of the grasping process. The RISO grippers allow users to manipulate objects using either rigid or soft grasps, depending on the task. Our user study reveals that, with the shared control framework and RISO grippers, users were able to grasp and manipulate a wide range of household objects effectively. The findings from this research emphasize the importance of integrating advanced learning algorithms and control strategies to improve the capabilities of assistive robots in helping users with their daily activities. By exploring different directions within the domain of assistive robotics, this thesis contributes to the development of methods that enhance the overall functionality of assistive robotic arms. | en |
| dc.description.abstractgeneral | In this thesis, we explore ways to make robotic arms attached to wheelchairs more helpful for people with disabilities in their everyday lives. To be truly useful, these robots need to understand a variety of tasks and be easy for users to control. Our focus is on developing techniques that help these robots learn different tasks, handle different types of objects, and make controlling them simpler. The thesis is built around three main contributions. First, we introduce a way for robots to learn how to perform complex tasks on their own and then simplify controlling robots for those tasks so users can control the robot to perform different tasks using just a joystick. We show through experiments that this approach helps people complete tasks faster than systems that rely on human-taught actions. Second, we improve how robots learn to perform tasks using a more efficient learning method. This method breaks down tasks into smaller steps, and the robot learns how to move toward each step more quickly. Our tests show that this approach speeds up the learning process compared to other methods. Finally, we address the challenge of handling different types of objects by developing a new type of robotic gripper that combines soft and rigid gripping options. This gripper allows users to pick up and manipulate a wide variety of household objects more easily, thanks to a control system that helps automate part of the process. In our user study, people found it easier to use the new gripper to handle different items. Overall, this research highlights the importance of combining learning algorithms and userfriendly controls to make assistive robots better at helping people with their daily tasks. These contributions advance the development of robotic arms that can more effectively assist users. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | https://hdl.handle.net/10919/123738 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Human-Robot Interaction | en |
| dc.subject | Reinforcement Learning | en |
| dc.subject | Assistive Manipulation | en |
| dc.title | Enhancing Capabilities of Assistive Robotic Arms: Learning, Control, and Object Manipulation | en |
| dc.type | Thesis | en |
| dc.type.dcmitype | Text | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |