Abstract

Interactive perception is a significant and unique characteristic of embodied agents. An agent can discover plenty of knowledge through active interaction with its surrounding environment. Recently, deep learning structures introduced new possibilities to interactive perception in robotics. The advantage of deep learning is in acquiring self-organizing features from gathered data; however, it is computationally impractical to implement in real-time interaction applications. Moreover, it can be difficult to attach a physical interpretation. An alternative suggested framework in such cases is integrated perception-action. In this dissertation, we propose two integrated interactive perception-action algorithms for real-time automated grasping of novel objects using pure tactile sensing. While visual sensing and processing is necessary for gross reaching movements, it can slow down the grasping process if it is the only sensing modality utilized. To overcome this issue, humans primarily utilize tactile perception once the hand is in contact with the object. Inspired by this, we first propose an algorithm to define similar ability for a robot by formulating the required grasping steps. Next, we develop the algorithm to achieve force closure constraint via suggesting a human-like behavior for the robot to interactively identify the object. During this process, the robot adjusts the hand through an interactive exploration of the object's local surface normal vector. After the robot finds the surface normal vector, it then tries to find the object edges to have a graspable final rendezvous with the object. Such achievement is very important in order to find the objects edges for rectangular objects before fully grasping the object. We implement the proposed approaches on an assistive robot to demonstrate the performance of interactive perception-action strategies to accomplish grasping task in an automatic manner.

Notes

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Graduation Date

2019

Semester

Fall

Advisor

Behal, Aman

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering

Format

application/pdf

Identifier

CFE0007780

URL

http://purl.fcla.edu/fcla/etd/CFE0007780

Language

English

Release Date

December 2019

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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