Reinforcement learning for robotic safe control with force sensing
For the task with complicated manipulation in unstructured environments, traditional hand-
coded methods are ineffective, while reinforcement learning can provide more general and …
coded methods are ineffective, while reinforcement learning can provide more general and …
Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition
Z Liu, X Liu, Y Zhang, Z Liu, P Huang - arXiv preprint arXiv:2311.11287, 2023 - arxiv.org
Robotic manipulation holds the potential to replace humans in the execution of tedious or
dangerous tasks. However, control-based approaches are not suitable due to the difficulty of …
dangerous tasks. However, control-based approaches are not suitable due to the difficulty of …
Reinforcement learning on variable impedance controller for high-precision robotic assembly
J Luo, E Solowjow, C Wen, JA Ojea… - … on Robotics and …, 2019 - ieeexplore.ieee.org
Precise robotic manipulation skills are desirable in many industrial settings, reinforcement
learning (RL) methods hold the promise of acquiring these skills autonomously. In this …
learning (RL) methods hold the promise of acquiring these skills autonomously. In this …
Learning compliant grasping and manipulation by teleoperation with adaptive force control
In this work, we focus on improving the robot's dexterous capability by exploiting visual
sensing and adaptive force control. TeachNet, a vision-based teleoperation learning …
sensing and adaptive force control. TeachNet, a vision-based teleoperation learning …
Review of deep reinforcement learning for robot manipulation
Reinforcement learning combined with neural networks has recently led to a wide range of
successes in learning policies in different domains. For robot manipulation, reinforcement …
successes in learning policies in different domains. For robot manipulation, reinforcement …
Deep reinforcement learning for tactile robotics: Learning to type on a braille keyboard
Artificial touch would seem well-suited for Reinforcement Learning (RL), since both
paradigms rely on interaction with an environment. Here we propose a new environment …
paradigms rely on interaction with an environment. Here we propose a new environment …
Learning object manipulation with dexterous hand-arm systems from human demonstration
P Ruppel, J Zhang - … on Intelligent Robots and Systems (IROS), 2020 - ieeexplore.ieee.org
We present a novel learning and control framework that combines artificial neural networks
with online trajectory optimization to learn dexterous manipulation skills from human …
with online trajectory optimization to learn dexterous manipulation skills from human …
Sim-to-real transfer for robotic manipulation with tactile sensory
Reinforcement Learning (RL) methods have been widely applied for robotic manipulations
via sim-to-real transfer, typically with proprioceptive and visual information. However, the …
via sim-to-real transfer, typically with proprioceptive and visual information. However, the …
Towards transferring tactile-based continuous force control policies from simulation to robot
The advent of tactile sensors in robotics has sparked many ideas on how robots can
leverage direct contact measurements of their environment interactions to improve …
leverage direct contact measurements of their environment interactions to improve …
MimicTouch: Learning Human's Control Strategy with Multi-Modal Tactile Feedback
In robotics and artificial intelligence, the integration of tactile processing is becoming
increasingly pivotal, especially in learning to execute intricate tasks like alignment and …
increasingly pivotal, especially in learning to execute intricate tasks like alignment and …