Relmogen: Integrating motion generation in reinforcement learning for mobile manipulation
Many Reinforcement Learning (RL) approaches use joint control signals (positions,
velocities, torques) as action space for continuous control tasks. We propose to lift the action …
velocities, torques) as action space for continuous control tasks. We propose to lift the action …
MPC-MPNet: Model-predictive motion planning networks for fast, near-optimal planning under kinodynamic constraints
Kinodynamic Motion Planning (KMP) is to find a robot motion subject to concurrent
kinematics and dynamics constraints. To date, quite a few methods solve KMP problems and …
kinematics and dynamics constraints. To date, quite a few methods solve KMP problems and …
Double critic deep reinforcement learning for mapless 3d navigation of unmanned aerial vehicles
This paper presents a novel deep reinforcement learning-based system for 3D mapless
navigation for Unmanned Aerial Vehicles (UAVs). Instead of using an image-based sensing …
navigation for Unmanned Aerial Vehicles (UAVs). Instead of using an image-based sensing …
Deep reinforcement learning for mapless navigation of a hybrid aerial underwater vehicle with medium transition
Since the application of Deep Q-Learning to the continuous action domain in Atari-like
games, Deep Reinforcement Learning (Deep-RL) techniques for motion control have been …
games, Deep Reinforcement Learning (Deep-RL) techniques for motion control have been …
Relmogen: Leveraging motion generation in reinforcement learning for mobile manipulation
Many Reinforcement Learning (RL) approaches use joint control signals (positions,
velocities, torques) as action space for continuous control tasks. We propose to lift the action …
velocities, torques) as action space for continuous control tasks. We propose to lift the action …
A shadowcasting-based next-best-view planner for autonomous 3D exploration
In this letter, we address the problem of autonomous exploration of unknown environments
with an aerial robot equipped with a sensory set that produces large point clouds, such as …
with an aerial robot equipped with a sensory set that produces large point clouds, such as …
Mapless navigation of a hybrid aerial underwater vehicle with deep reinforcement learning through environmental generalization
Previous works showed that Deep-RL can be applied to perform mapless navigation,
including the medium transition of Hybrid Unmanned Aerial Underwater Vehicles …
including the medium transition of Hybrid Unmanned Aerial Underwater Vehicles …
Deep reinforcement learning for mapless navigation of unmanned aerial vehicles
RB Grando, JC de Jesus… - 2020 Latin American …, 2020 - ieeexplore.ieee.org
This paper presents a deep reinforcement learning-based system for goal-oriented mapless
navigation for Unmanned Aerial Vehicles (UAVs). In this context, image-based sensing …
navigation for Unmanned Aerial Vehicles (UAVs). In this context, image-based sensing …
DoCRL: Double Critic Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition
Deep Reinforcement Learning (Deep-RL) techniques for motion control have been
continuously used to deal with decision-making problems for a wide variety of robots …
continuously used to deal with decision-making problems for a wide variety of robots …
When to replan? an adaptive replanning strategy for autonomous navigation using deep reinforcement learning
The hierarchy of global and local planners is one of the most commonly utilized system
designs in autonomous robot navigation. While the global planner generates a reference …
designs in autonomous robot navigation. While the global planner generates a reference …