text: const topic = [
    '2017',
    'Virtual-to-Real:',
    'Learning to Control in Visual Semantic Segmentation',
  ];
  
  const abstract = [
    `Collecting training data from the physical world is usually
  time-consuming and even dangerous for fragile robots, and
  therefore, recent advances in robot learning propose to use
  simulators as the training platform. Unfortunately, the reality
  gap between synthetic and real visual data prohibits directly
  migrating the models trained in virtual worlds to the real
  world. This paper proposes a modular architecture for tackling
  simulation- to-reality problem. The architecture separates the
  learning model into a perception module and a control policy
  module, and uses semantic image segmentation as the meta
  representation for relating these two modules. The perception
  module translates RGB images to semantic image segmentations.
  The control policy module employs deep reinforcement learning
  (RL) methods and takes image segmentations as its inputs.
  Experimental results show that our architecture outperforms all
  baseline methods in the virtual and real environments, and
  learns faster than them.`,
  ];
  
  const proposedMethodology = [
    {
      id: 1,
      title: 'Bridge the Gap between Simulation and Reality',
    },
    {
      id: 2,
      title: 'Training in Simulated Enviorments',
    },
    {
      id: 3,
      title: 'Visual Guidance Module and Target Switching',
    },
  ];
  
  const experimentalResults = [
    {
      id: 4,
      title: 'Model Settings and Robotic Platform',
    },
    {
      id: 5,
      title: 'Learning Curves of the Two Tasks',
    },
    {
      id: 6,
      title: 'Comparison in the Simulated and Real Worlds',
    },
  ];
  
  const conclusion = [
    `We presented a new modular architecture for transferring policies
  learned in simulation to the real world for vision-based
  roboticcontrol. We proposed to separate the model in to a perception
  module and a control policy module, and introduce the concept of
  using semantic image segmentation as the meta state for relating
  these two modules. We trained our model with a standard RL
  algorithm, and did not apply any domain randomization technique. We
  performed experiments in two benchmark tasks: an obstacle avoidance
  task and a target following task, and demonstrated that our proposed
  method outperforms the baseline models in both virtual and real
  environments. We analyzed the use of scene semantics as the meta
  state, and show that this structured form of representations does
  improve the learning speed of our model`,
  ];
  
  const VirtualToReal = {
    topic,
    abstract,
    proposedMethodology,
    experimentalResults,
    conclusion,
  };
  
  export default VirtualToReal;
  