Learning Partially Structured Environmental Dynamics for Marine Robotic Navigation

03/11/2018
by   Chen Huang, et al.
0

We investigate the scenario that a robot needs to reach a designated goal after taking a sequence of appropriate actions in a non-static environment that is partially structured. One application example is to control a marine vehicle to move in the ocean. The ocean environment is dynamic and oftentimes the ocean waves result in strong disturbances that can disturb the vehicle's motion. Modeling such dynamic environment is non-trivial, and integrating such model in the robotic motion control is particularly difficult. Fortunately, the ocean currents usually form some local patterns (e.g. vortex) and thus the environment is partially structured. The historically observed data can be used to train the robot to learn to interact with the ocean tidal disturbances. In this paper we propose a method that applies the deep reinforcement learning framework to learn such partially structured complex disturbances. Our results show that, by training the robot under artificial and real ocean disturbances, the robot is able to successfully act in complex and spatiotemporal environments.

READ FULL TEXT

page 1

page 4

page 5

research
05/28/2020

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

Deep Reinforcement Learning has been successfully applied in various com...
research
12/17/2020

ViNG: Learning Open-World Navigation with Visual Goals

We propose a learning-based navigation system for reaching visually indi...
research
11/11/2022

Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling

Learning long-horizon tasks such as navigation has presented difficult c...
research
09/13/2018

Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics

Learning robot tasks or controllers using deep reinforcement learning ha...
research
02/05/2019

Learning to Learn in Simulation

Deep learning often requires the manual collection and annotation of a t...
research
04/07/2020

Self-propulsion on spandex: toward a robotic analog gravity system

Numerous laboratory systems have been proposed as analogs to study pheno...

Please sign up or login with your details

Forgot password? Click here to reset