Results 11 to 20 of about 11,358 (231)

Data-Efficient Reinforcement Learning for Variable Impedance Control

IEEE Access
One of the most crucial steps toward achieving human-like manipulation skills in robots is to incorporate compliance into the robot controller. Compliance not only makes the robot’s behaviour safe but also makes it more energy efficient.
Akhil S. Anand, Rituraj Kaushik, Jan Tommy Gravdahl, Fares J. Abu-Dakka   +3 more
doaj   +6 more sources

Learning Variable Impedance Control for Contact Sensitive Tasks [PDF]

IEEE Robotics and Automation Letters, 2020
Reinforcement learning algorithms have shown great success in solving different problems ranging from playing video games to robotics. However, they struggle to solve delicate robotic problems, especially those involving contact interactions. Though in principle a policy directly outputting joint torques should be able to learn to perform these tasks ...
Miroslav Bogdanovic, Majid Khadiv, Ludovic Righetti   +2 more
openaire   +3 more sources

Model-based variable impedance learning control for robotic manipulation

Robotics and Autonomous Systems, 2023
The capability to adapt compliance by varying muscle stiffness is crucial for dexterous manipulation skills in humans. Incorporating compliance in robot motor control is crucial for enabling real-world force interaction tasks with human-like dexterity.
Akhil S. Anand, Jan Tommy Gravdahl, Fares J. Abu-Dakka   +2 more
openaire   +2 more sources

Exploiting sensorimotor stochasticity for learning control of variable impedance actuators [PDF]

2010 10th IEEE-RAS International Conference on Humanoid Robots, 2010
Novel anthropomorphic robotic systems increasingly employ variable impedance actuation in order to achieve robustness to uncertainty, superior agility and efficiency that are hallmarks of biological systems. Controlling and modulating impedance profiles such that it is optimally tuned to the controlled plant is crucial to realise these benefits.
Djordje Mitrovic, Stefan Klanke, Matthew Howard 0001, Sethu Vijayakumar   +3 more
openaire   +2 more sources

Learning From Demonstration and Interactive Control of Variable-Impedance to Cut Soft Tissues [PDF]

IEEE/ASME Transactions on Mechatronics, 2022
In this article, we propose an approach to extract variable-impedance during cutting tasks from human demonstrations, so as to ease soft-tissue cutting by robots. We model the dynamic adjustment of the human arm during interactions with the tissue and transfer these adaptive capabilities to the robot, by learning both the motion and change of impedance.
Wu, Rui, Billard, Aude, Wu, Rui
openaire   +1 more source

Variable Impedance Control - A Reinforcement Learning Approach

Robotics: Science and Systems VI, 2010
One of the hallmarks of the performance, versatility, and robustness of biological motor control is the ability to adapt the impedance of the overall biomechanical system to different task requirements and stochastic disturbances. A transfer of this principle to robotics is desirable, for instance to enable robots to work robustly and safely in ...
Jonas Buchli, Evangelos A. Theodorou, Freek Stulp, Stefan Schaal   +3 more
openaire   +1 more source

Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly [PDF]

2019 International Conference on Robotics and Automation (ICRA), 2019
ICRA 2019.
Jianlan Luo, Eugen Solowjow, Chengtao Wen, Juan Aparicio Ojea, Alice M. Agogino, Aviv Tamar, Pieter Abbeel   +6 more
openaire   +2 more sources

Q-Learning-based model predictive variable impedance control for physical human-robot collaboration

Artificial Intelligence, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Loris Roveda, Andrea Testa, Asad Ali Shahid, Francesco Braghin, Dario Piga   +4 more
openaire   +1 more source

Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

Journal of Intelligent & Robotic Systems, 2020
Industry 4.0 is taking human-robot collaboration at the center of the production environment. Collaborative robots enhance productivity and flexibility while reducing human's fatigue and the risk of injuries, exploiting advanced control methodologies. However, there is a lack of real-time model-based controllers accounting for the complex human-robot ...
Loris Roveda, Jeyhoon Maskani, Paolo Franceschi, Arash Abdi, Francesco Braghin, Lorenzo Molinari Tosatti, Nicola Pedrocchi   +6 more
openaire   +4 more sources

A PSO-Optimized Fuzzy Reinforcement Learning Method for Making the Minimally Invasive Surgical Arm Cleverer

IEEE Access, 2019
For robotic-assisted surgery, the preoperative preparation procedure within which the surgical arms need to be adjusted manually to their expected configuration can pose interlocking problems in terms of accuracy, system robustness, and human-robot ...
Weidong Wang, Chengjin Du, Wei Wang, Zhijiang Du   +3 more
doaj   +1 more source