Add to ORKGDeep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control stability. This is in sharp contrast to the control theory community where the well-established norm is to prove stability whenever a control law is synthesized. What makes traditional stability analysis difficult for DRL are the uninterpretable nature of the neural network policies and unknown system dynamics. In this work, unconditional stability is obtained by deriving an interpretable deep policy structure based on the energy shaping control of Lagrangian systems. Then, stability during physical interaction with an unknown environment is established based on pas...
Deep reinforcement learning (RL) is an optimization-driven framework for producing control strategie...
Model-free reinforcement learning has proved to be successful in many tasks such as robotic manipula...
A methodology for developing robust control systems using Deep Reinforcement Learning (DRL) is propo...
The poster presents a new family of deep neural network-based dynamic systems and how these dynamics...
Reinforcement Learning (RL) of robotic manipu-lation skills, despite its impressive successes, stand...
Deep Reinforcement Learning (DRL) is a promising Machine Learning technique that enables robotic sys...
A fundamental problem of robotics is how can one program a robot to perform a task with its limited ...
In this thesis we deal with the problem of using deep reinforcement learning to generate robust poli...
International audienceDeep learning has provided new ways of manipulating, processing and analyzing ...
The focus of the research community in the soft robotic field has been on developing innovative mate...
In control, the objective is to find a mapping from states to actions that steer a system to a desir...
Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems...
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems ...
This paper proposes an enhanced version of the integral sliding mode (ISM) control, where a deep neu...
Reinforcement Learning (RL) is a framework to deal with decision-making problems with the goal of fi...
Deep reinforcement learning (RL) is an optimization-driven framework for producing control strategie...
Model-free reinforcement learning has proved to be successful in many tasks such as robotic manipula...
A methodology for developing robust control systems using Deep Reinforcement Learning (DRL) is propo...
The poster presents a new family of deep neural network-based dynamic systems and how these dynamics...
Reinforcement Learning (RL) of robotic manipu-lation skills, despite its impressive successes, stand...
Deep Reinforcement Learning (DRL) is a promising Machine Learning technique that enables robotic sys...
A fundamental problem of robotics is how can one program a robot to perform a task with its limited ...
In this thesis we deal with the problem of using deep reinforcement learning to generate robust poli...
International audienceDeep learning has provided new ways of manipulating, processing and analyzing ...
The focus of the research community in the soft robotic field has been on developing innovative mate...
In control, the objective is to find a mapping from states to actions that steer a system to a desir...
Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems...
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems ...
This paper proposes an enhanced version of the integral sliding mode (ISM) control, where a deep neu...
Reinforcement Learning (RL) is a framework to deal with decision-making problems with the goal of fi...
Deep reinforcement learning (RL) is an optimization-driven framework for producing control strategie...
Model-free reinforcement learning has proved to be successful in many tasks such as robotic manipula...
A methodology for developing robust control systems using Deep Reinforcement Learning (DRL) is propo...