Building a Sandbox Towards Investigating the Behavior of Control Algorithms and Training of Real-World Robots

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Abstract

The control of legged robots and teaching robotic hands to grasp are still challenging tasks. Machine learning approaches already work well in simulation. However, the discrepancy between simulation and reality sometimes causes diÿculties when applying simulation results to the real robot. Learning algorithms also require a huge amount of training data. The goal of this work is to build a sandbox that provides a detailed comparison between simulated and real-world robots and o˙ers a way of controlled and continuous data collection and exploration. The sandbox consists of a motion capture and a simulation component. The motion capture component is responsible for the continuous data collection and is realized with a system from OptiTrack with six high-precision infrared cameras. The simulation com-ponent is realized with Simulink and the Simscape Multibody Library. This component is responsible for the exploration and comparison of simulated data with real-world data. The robot that is selected for this work is a small four-legged puppy robot from ROBOTIS that is actuated with 15 Dynamixel servomotors. To integrate the robot into the sandbox, the robot’s controller is reprogrammed to make a transfer from motion data to the robot easier and to control the robot remotely. The robot is programmed with a straight walking gait and equipped with reflective markers to track its movements. With the computer-aided design (CAD) software SolidWorks, a 3D model of the puppy robot is constructed that is used for simulation in Simulink. The result is a system that accurately gathers 6 degrees of freedom (DOF) data of a small robot. This data is transferred to the simulation and can be compared to simulated data. Data from the simulation can also be tested easily on the real robot and tracked again. This way, a closed-loop system is provided for iterative robot exploration. Two datasets are compared with the help of the resulting sandbox: A dataset from ROBOTIS containing ideal joint angles for the robot, and a dataset that is obtained with the motion capture system, containing tracked joint angles. The datasets are simulated and the position and orientation of the robot are compared to the data from the motion capture. Despite strong variations in the simulated results, the simulated robot kept a similar direction and was only a few centimetres o˙ from the real robot.

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BibTeX

@mastersthesis{Brandstaetter2020,
  title =      "Building a Sandbox Towards Investigating the Behavior of
               Control Algorithms and Training of Real-World Robots",
  author =     "Klara Brandst\"{a}tter",
  year =       "2020",
  abstract =   "The control of legged robots and teaching robotic hands to
               grasp are still challenging tasks. Machine learning
               approaches already work well in simulation. However, the
               discrepancy between simulation and reality sometimes causes
               diÿculties when applying simulation results to the real
               robot. Learning algorithms also require a huge amount of
               training data. The goal of this work is to build a sandbox
               that provides a detailed comparison between simulated and
               real-world robots and o˙ers a way of controlled and
               continuous data collection and exploration. The sandbox
               consists of a motion capture and a simulation component. The
               motion capture component is responsible for the continuous
               data collection and is realized with a system from OptiTrack
               with six high-precision infrared cameras. The simulation
               com-ponent is realized with Simulink and the Simscape
               Multibody Library. This component is responsible for the
               exploration and comparison of simulated data with real-world
               data. The robot that is selected for this work is a small
               four-legged puppy robot from ROBOTIS that is actuated with
               15 Dynamixel servomotors. To integrate the robot into the
               sandbox, the robot’s controller is reprogrammed to make a
               transfer from motion data to the robot easier and to control
               the robot remotely. The robot is programmed with a straight
               walking gait and equipped with reflective markers to track
               its movements. With the computer-aided design (CAD) software
               SolidWorks, a 3D model of the puppy robot is constructed
               that is used for simulation in Simulink. The result is a
               system that accurately gathers 6 degrees of freedom (DOF)
               data of a small robot. This data is transferred to the
               simulation and can be compared to simulated data. Data from
               the simulation can also be tested easily on the real robot
               and tracked again. This way, a closed-loop system is
               provided for iterative robot exploration. Two datasets are
               compared with the help of the resulting sandbox: A dataset
               from ROBOTIS containing ideal joint angles for the robot,
               and a dataset that is obtained with the motion capture
               system, containing tracked joint angles. The datasets are
               simulated and the position and orientation of the robot are
               compared to the data from the motion capture. Despite strong
               variations in the simulated results, the simulated robot
               kept a similar direction and was only a few centimetres o˙
               from the real robot.",
  month =      nov,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/Brandstaetter2020/",
}