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Dexterous Robot Manipulation using Physics Engine

Project

Project Details

Program
Electrical Engineering
Field of Study
Electrical and Computer Enginnering
Division
Computer, Electrical and Mathematical Sciences and Engineering

Project Description

With the advance in sensing, actuation, and mechanical design, the robots are becoming more capable of performing complex and high-precision tasks ranging from autonomous driving in urban cities to handling packages in fulfillment centers. Control and planning of robot motion in real-world environments require high-fidelity models to simulate how the robot's motion impacts physical objects before they take action. Many well-established approaches in robotics rely on mathematical models due to their analytic tractability; however as robotic systems become complex and are required to interact in physical environments, we face limitations in finding analytically tractable models and begin to resort to new models that leverage the powerful computational resources embodied in robotic systems. To address the challenges in robot operation in physical environments, in this project, we explore the idea of building computational models based on physics engines and design feedback algorithms to autotune parameters of the model whenever the robot detects the discrepancy between the simulation and its experience in the physical world -- to reduce the sim-to-real gap. As key applications, we imagine to apply outcomes of the project to enable robotic manipulators to carry out the tasks in assembly lines and research laboratories that are labor-intensive and require dexterous skills.

About the Researcher

Shinkyu Park
Assistant Professor, Electrical and Computer Engineering
Computer, Electrical and Mathematical Science and Engineering Division

Affiliations

Education Profile

  • Postdoctoral Fellow, Massachusetts Institute of Technology, 2019
  • PhD, University of Maryland College Park, 2015
  • MS, Seoul National University, 2008BS, Kyungpook National University, 2006

Research Interests

Professor Park's research interests are in the general areas of robotics, multi-agent decision making, and feedback control. His most recent research has been in design and control of multi-robot systems and related topics of game theory and feedback control systems, with applications to multi-robot learning and coordination.

Selected Publications

  • S. Park, M. Cap, J. Alonso-Mora, C. Ratti, and D. Rus, ""Social Trajectory Planning for Urban Autonomous Surface Vessels,"" IEEE Transactions on Robotics, 2020.
  • S. Park, K. H. Aschenbach, M. Ahmed, W. Scott, N. E. Leonard, K. Abernathy, G. Marshall, M. Shepard, and N. C. Martins, ""Animal-Borne Wireless Network: Remote Imaging of Community Ecology,"" Journal of Field Robotics, vol. 36, no. 6, pp. 1141-1165, 2019.
  • S. Park, J. S. Shamma, and N. C. Martins, ""From Population Games to Payoff Dynamics Models: A Passivity-Based Approach,"" Tutorial Session at IEEE Conference on Decision and Control (CDC), pp. 6584-6601, 2019.
  • B. Gheneti, S. Park, R. Kelly, D. Meyers, P. Leoni, C. Ratti, and D. Rus, ""Trajectory Planning for the Shapeshifting of Autonomous Surface Vessels,"" 2nd IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS' 19), pp. 76-82, 2019.
  • S. Park and N. C. Martins, ""Design of Distributed LTI Observers for State Omniscience,"" IEEE Transactions on Automatic Control, vol. 62, no. 2, pp. 561-576, 2017.

Desired Project Deliverables

This project aims to adopt a physics engine to build computational model and examine the fidelity of the model through experiments with manipulators in the robotics lab at KAUST. The students are expected to collaborate with the lab members to explore creative ideas and implement them on physical robotic systems. To fulfill the requirements of the project, the students are expected to have experience working with robotic systems/software and confidence in Python programming. The model design and experiment reports are expected to be delivered at the end of the internship program.

Recommended Student Background

Robotics
Feedback Control Systems
Computer Science
Electrical Engineering

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