mlsanityData to 7 Oct 2026

How To Train Your HERON

arXiv 2102.10357 ↗First posted 20 Feb 2021cs.RO cs.LGcited by 0

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Abstract (arXiv)

In this paper we apply Deep Reinforcement Learning (Deep RL) and Domain Randomization to solve a navigation task in a natural environment relying solely on a 2D laser scanner. We train a model-based RL agent in simulation to follow lake and river shores and apply it on a real Unmanned Surface Vehicle in a zero-shot setup. We demonstrate that even though the agent has not been trained in the real world, it can fulfill its task successfully and adapt to changes in the robot's environment and dynamics. Finally, we show that the RL agent is more robust, faster, and more accurate than a state-aware Model-Predictive-Controller.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 20 Feb 2021.

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16 of 26 references.

References · 16

  1. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  2. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,598
  3. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  4. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,221
  5. 2018Learning Dexterous In-Hand Manipulationcited by 643
  6. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 616
  7. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  8. 2020Reinforcement Learning with Augmented Datacited by 452
  9. 2019Learning agile and dynamic motor skills for legged robotscited by 402
  10. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 277
  11. 2019Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Modelcited by 228
  12. 2016CAD2RL: Real Single-Image Flight without a Single Real Imagecited by 227
4 more
  1. 2018Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experiencecited by 203
  2. 2018Distributed Distributional Deterministic Policy Gradientscited by 195
  3. 2020High-speed Autonomous Drifting with Deep Reinforcement Learningcited by 20
  4. 2018Virtual-to-Real: Learning to Control in Visual Semantic Segmentationcited by 12

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