PhD Defense by Tapomayukh Bhattacharjee

Event Details
  • Date/Time:
    • Monday July 10, 2017
      11:00 am - 1:00 pm
  • Location: 3115 (McIntire Conference Room), BME Whitaker Building
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Summaries

Summary Sentence: Rapid Haptic Perception using Force and Thermal Sensing

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Tapomayukh Bhattacharjee

Robotics Ph.D. Candidate

Dept. of Biomedical Engineering

Georgia Institute of Technology

Robotics Ph.D. Defense Presentation

Title: Rapid Haptic Perception using Force and Thermal Sensing

Date: July 10th, 2017 (Monday)

Time: 11:00 AM EDT

Location: 3115 (McIntire Conference Room), BME Whitaker Building

Committee

Dr. Charles C. Kemp, Advisor (Dept. of Biomedical Engineering, Georgia Institute of Technology)

Dr. Henrik I. Christensen (Dept. of Computer Science and Engineering, University of California San Diego)

Dr. C. Karen Liu (School of Interactive Computing, Georgia Institute of Technology)

Dr. James M. Rehg (School of Interactive Computing, Georgia Institute of Technology)

Dr. Lena H. Ting (Dept. of Biomedical Engineering, Emory University)

Abstract

Tactile sensing can enable a robot to infer properties of its surroundings. Recent research has focused on robots that haptically perceive the world through exploratory behaviors that occur over tens of seconds. During manipulation, many opportunities arise for robots to gather information about the environment from brief (<= 2 seconds) contact due to simple motions (e.g., linear). The goal of our work was to enable robots to infer haptic properties under these conditions using force and thermal sensing.

 

We used a data-driven approach with various machine learning methods. Key challenges were obtaining adequate haptic data for training and developing methods that performed well on haptic data that differed from the training data due to common real-world phenomena. For example, haptic sensory signals vary significantly due to the robot, including its velocity, stiffness, and sensor temperature.

 

To collect suitable data, we used a variety of platforms, including simplified robots, handheld human-operated devices, and a mobile robot. We also generated synthetic data with physics-based models. Through careful empirical evaluation, we identified machine learning methods that better handled common signal variations. We also used physics-based models to characterize common perceptual ambiguities and predict the performance of data-driven methods. Overall, our research demonstrates the feasibility of robots inferring properties of their surroundings from brief contact with objects in human environments. By using force and thermal sensing, our methods rapidly recognized materials, detected when objects moved, detected contact with people, and inferred other properties of the robot’s surroundings.

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Phd Defense
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Jun 29, 2017 - 10:43am
  • Last Updated: Jun 29, 2017 - 10:43am