[HTML][HTML] A review on reinforcement learning for contact-rich robotic manipulation tasks
Í Elguea-Aguinaco, A Serrano-Muñoz… - Robotics and Computer …, 2023 - Elsevier
Research and application of reinforcement learning in robotics for contact-rich manipulation
tasks have exploded in recent years. Its ability to cope with unstructured environments and …
tasks have exploded in recent years. Its ability to cope with unstructured environments and …
Learning force control for contact-rich manipulation tasks with rigid position-controlled robots
CC Beltran-Hernandez, D Petit… - IEEE Robotics and …, 2020 - ieeexplore.ieee.org
Reinforcement Learning (RL) methods have been proven successful in solving manipulation
tasks autonomously. However, RL is still not widely adopted on real robotic systems …
tasks autonomously. However, RL is still not widely adopted on real robotic systems …
Reinforcement learning based on movement primitives for contact tasks
Recently, robot learning through deep reinforcement learning has incorporated various
robot tasks through deep neural networks, without using specific control or recognition …
robot tasks through deep neural networks, without using specific control or recognition …
Meta-reinforcement learning for robotic industrial insertion tasks
Robotic insertion tasks are characterized by contact and friction mechanics, making them
challenging for conventional feedback control methods due to unmodeled physical effects …
challenging for conventional feedback control methods due to unmodeled physical effects …
Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks
Realistic manipulation tasks require a robot to interact with an environment with a prolonged
sequence of motor actions. While deep reinforcement learning methods have recently …
sequence of motor actions. While deep reinforcement learning methods have recently …
[HTML][HTML] Reinforcement learning in robotics: Applications and real-world challenges
In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to
learn, improve, adapt and reproduce tasks with dynamically changing constraints based on …
learn, improve, adapt and reproduce tasks with dynamically changing constraints based on …
Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks
Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive
results in recent years. While many studies in RL focus on varying the observation space or …
results in recent years. While many studies in RL focus on varying the observation space or …
Making sense of vision and touch: Learning multimodal representations for contact-rich tasks
Contact-rich manipulation tasks in unstructured environments often require both haptic and
visual feedback. It is nontrivial to manually design a robot controller that combines these …
visual feedback. It is nontrivial to manually design a robot controller that combines these …
Residual reinforcement learning for robot control
Conventional feedback control methods can solve various types of robot control problems
very efficiently by capturing the structure with explicit models, such as rigid body equations …
very efficiently by capturing the structure with explicit models, such as rigid body equations …
Learning variable impedance control for contact sensitive tasks
M Bogdanovic, M Khadiv… - IEEE Robotics and …, 2020 - ieeexplore.ieee.org
Reinforcement learning algorithms have shown great success in solving different problems
ranging from playing video games to robotics. However, they struggle to solve delicate …
ranging from playing video games to robotics. However, they struggle to solve delicate …