How To Train Your HERON

• URL: http://arxiv.org/abs/2102.10357v1
• Date: Sat, 20 Feb 2021 14:22:21 GMT
• Title: How To Train Your HERON
• Authors: Antoine Richard, Stephanie Aravecchia, Thomas Schillaci, Matthieu Geist, Cedric Pradalier
• Abstract summary: 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 show that the RL agent is more robust, faster, and more accurate than a state-aware Model-Predictive-Controller.
• Score: 15.49567616697007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a Grid [0.0]
  We bridge the gap between a trained agent in a simulated environment and its real-world implementation in navigating a robot in a similar setting. Specifically, we focus on navigating a quadruped robot in a real-world grid-like environment inspired by the Gymnasium Frozen Lake.
  arXiv  Detail & Related papers  (2024-11-05T20:18:29Z)
• Automatic Environment Shaping is the Next Frontier in RL [20.894840942319323]
  Many roboticists dream of presenting a robot with a task in the evening and returning the next morning to find the robot capable of solving the task. Sim-to-real reinforcement learning has achieved impressive performance on challenging robotics tasks, but requires substantial human effort to set up the task in a way that is amenable to RL. It's our position that algorithmic improvements in policy optimization and other ideas should be guided towards resolving the primary bottleneck of shaping the training environment.
  arXiv  Detail & Related papers  (2024-07-23T05:22:29Z)
• Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks [93.38375271826202]
  We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks. We first build a simulator by integrating Gaussian splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks. In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, programming of expert demonstration training data, and the task understanding capabilities of Liquid networks.
  arXiv  Detail & Related papers  (2024-06-21T13:48:37Z)
• SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering [49.78647219715034]
  We propose a sensing-aware model-based reinforcement learning system called SAM-RL. With the sensing-aware learning pipeline, SAM-RL allows a robot to select an informative viewpoint to monitor the task process. We apply our framework to real world experiments for accomplishing three manipulation tasks: robotic assembly, tool manipulation, and deformable object manipulation.
  arXiv  Detail & Related papers  (2022-10-27T05:30:43Z)
• Verifying Learning-Based Robotic Navigation Systems [61.01217374879221]
  We show how modern verification engines can be used for effective model selection. Specifically, we use verification to detect and rule out policies that may demonstrate suboptimal behavior. Our work is the first to demonstrate the use of verification backends for recognizing suboptimal DRL policies in real-world robots.
  arXiv  Detail & Related papers  (2022-05-26T17:56:43Z)
• Multitask Adaptation by Retrospective Exploration with Learned World Models [77.34726150561087]
  We propose a meta-learned addressing model called RAMa that provides training samples for the MBRL agent taken from task-agnostic storage. The model is trained to maximize the expected agent's performance by selecting promising trajectories solving prior tasks from the storage.
  arXiv  Detail & Related papers  (2021-10-25T20:02:57Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World [0.0]
  We present a DRL-based algorithm that is capable of performing autonomous robot control using Deep Q-Networks (DQN) In our approach, the agent is trained in a simulated environment and it is able to navigate both in a simulated and real-world environment. The trained agent is able to run on limited hardware resources and its performance is comparable to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-09-23T15:23:54Z)
• AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning [38.429105809093116]
  We introduce a deep reinforcement learning (RL) based multi-robot formation controller for the task of autonomous aerial human motion capture (MoCap) We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose and shape a single moving person using multiple aerial vehicles.
  arXiv  Detail & Related papers  (2020-07-13T12:30:31Z)
• RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real [74.45688231140689]
  We introduce the RL-scene consistency loss for image translation, which ensures that the translation operation is invariant with respect to the Q-values associated with the image. We obtain RL-CycleGAN, a new approach for simulation-to-real-world transfer for reinforcement learning.
  arXiv  Detail & Related papers  (2020-06-16T08:58:07Z)
• Smooth Exploration for Robotic Reinforcement Learning [11.215352918313577]
  Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL leads to jerky motion patterns on real robots. We address these issues by adapting state-dependent exploration (SDE) to current Deep RL algorithms.
  arXiv  Detail & Related papers  (2020-05-12T12:28:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.