Improving robotic manipulation: techniques for object pose estimation, accommodating positional uncertainty, and disassembly tasks from examples

Galaiya, Viral Rasik (2024) Improving robotic manipulation: techniques for object pose estimation, accommodating positional uncertainty, and disassembly tasks from examples. Masters thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (6MB)

Abstract

To use robots in more unstructured environments, we have to accommodate for more complexities. Robotic systems need more awareness of the environment to adapt to uncertainty and variability. Although cameras have been predominantly used in robotic tasks, the limitations that come with them, such as occlusion, visibility and breadth of information, have diverted some focus to tactile sensing. In this thesis, we explore the use of tactile sensing to determine the pose of the object using the temporal features. We then use reinforcement learning with tactile collisions to reduce the number of attempts required to grasp an object resulting from positional uncertainty from camera estimates. Finally, we use information provided by these tactile sensors to a reinforcement learning agent to determine the trajectory to take to remove an object from a restricted passage while reducing training time by pertaining from human examples.

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