Add to ORKGAbstract — Learning policies that generalize across multiple tasks is an important and challenging research topic in rein-forcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled ap-proaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in r...
Some imitation learning approaches rely on Inverse Reinforcement Learning (IRL) methods, to decode a...
Reinforcement Learning is an essential ability for robots to learn new motor skills. Nevertheless, f...
© 2014 Elsevier B.V.In robotics, lower-level controllers are typically used to make the robot solve ...
Abstract — Learning policies that generalize across multiple tasks is an important and challenging r...
A summary of the state-of-the-art reinforcement learning in robotics is given, in terms of both algo...
Abstract—Reinforcement learning enables an agent to learn behavior by acquiring experience through t...
In robotics, controllers make the robot solve a task within a specific context. The context can des...
Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in...
Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in...
Robots are on the verge of becoming ubiquitous. In the form of affordable humanoid toy robots, auton...
Behavior learning is a promising alternative to planning and control for behavior generation in robo...
Policy Learning approaches are among the best suited methods for high-dimensional, continuous contro...
Humans utilise a large diversity of control and reasoning methods to solve different robot manipula...
As most action generation problems of autonomous robots can be phrased in terms of sequential decisi...
Policy learning which allows autonomous robots to adapt to novel situations has been a long standing...
Some imitation learning approaches rely on Inverse Reinforcement Learning (IRL) methods, to decode a...
Reinforcement Learning is an essential ability for robots to learn new motor skills. Nevertheless, f...
© 2014 Elsevier B.V.In robotics, lower-level controllers are typically used to make the robot solve ...
Abstract — Learning policies that generalize across multiple tasks is an important and challenging r...
A summary of the state-of-the-art reinforcement learning in robotics is given, in terms of both algo...
Abstract—Reinforcement learning enables an agent to learn behavior by acquiring experience through t...
In robotics, controllers make the robot solve a task within a specific context. The context can des...
Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in...
Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in...
Robots are on the verge of becoming ubiquitous. In the form of affordable humanoid toy robots, auton...
Behavior learning is a promising alternative to planning and control for behavior generation in robo...
Policy Learning approaches are among the best suited methods for high-dimensional, continuous contro...
Humans utilise a large diversity of control and reasoning methods to solve different robot manipula...
As most action generation problems of autonomous robots can be phrased in terms of sequential decisi...
Policy learning which allows autonomous robots to adapt to novel situations has been a long standing...
Some imitation learning approaches rely on Inverse Reinforcement Learning (IRL) methods, to decode a...
Reinforcement Learning is an essential ability for robots to learn new motor skills. Nevertheless, f...
© 2014 Elsevier B.V.In robotics, lower-level controllers are typically used to make the robot solve ...