Add to ORKGReinforcement learning (RL) algorithms face significant challenges when dealing with long-horizon robot manipulation tasks in real-world environments due to sample inefficiency and safety issues. To overcome these challenges, we propose a novel framework, SEED, which leverages two approaches: reinforcement learning from human feedback (RLHF) and primitive skill-based reinforcement learning. Both approaches are particularly effective in addressing sparse reward issues and the complexities involved in long-horizon tasks. By combining them, SEED reduces the human effort required in RLHF and increases safety in training robot manipulation with RL in real-world settings. Additionally, parameterized skills provide a clear view of the agent's high...
While reinforcement learning has led to promising results in robotics, defining an informative rewar...
For robots to perform tasks in the unstructured environments of the real world, they must be able to...
We are approaching a future where robots and humans will co-exist and co-adapt. To understand how ca...
Off-the-shelf Reinforcement Learning (RL) algorithms suffer from slow learning performance, partly b...
AbstractWhile Reinforcement Learning (RL) is not traditionally designed for interactive supervisory ...
Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinfor...
Robots are extending their presence in domestic environments every day, it being more common to see ...
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align wi...
Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the des...
Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowle...
As robots become a mass consumer product, they will need to learn new skills by interacting with typ...
Over the course of the last decade, the framework of reinforcement learning has developed into a pro...
Social interacting is a complex task for which machine learning holds particular promise. However, a...
The ability to learn new tasks by sequencing already known skills is an important requirement for fu...
University of Technology Sydney. Faculty of Engineering and Information Technology.A promising metho...
While reinforcement learning has led to promising results in robotics, defining an informative rewar...
For robots to perform tasks in the unstructured environments of the real world, they must be able to...
We are approaching a future where robots and humans will co-exist and co-adapt. To understand how ca...
Off-the-shelf Reinforcement Learning (RL) algorithms suffer from slow learning performance, partly b...
AbstractWhile Reinforcement Learning (RL) is not traditionally designed for interactive supervisory ...
Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinfor...
Robots are extending their presence in domestic environments every day, it being more common to see ...
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align wi...
Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the des...
Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowle...
As robots become a mass consumer product, they will need to learn new skills by interacting with typ...
Over the course of the last decade, the framework of reinforcement learning has developed into a pro...
Social interacting is a complex task for which machine learning holds particular promise. However, a...
The ability to learn new tasks by sequencing already known skills is an important requirement for fu...
University of Technology Sydney. Faculty of Engineering and Information Technology.A promising metho...
While reinforcement learning has led to promising results in robotics, defining an informative rewar...
For robots to perform tasks in the unstructured environments of the real world, they must be able to...
We are approaching a future where robots and humans will co-exist and co-adapt. To understand how ca...