Dexterous manipulation of the robot is an important part of realizing intelligence, but
manipulators can only perform simple tasks such as sorting and packing in a structured …

The idea of reusing or transferring information from previously learned tasks (source tasks)
for the learning of new tasks (target tasks) has the potential to significantly improve the …

The answer to the question posed in the title is yes if the context (the list of variables defining
the motion control problem) is dimensionally similar. This article explores the use of the …

Featured Application The proposed RL via data-informed Domain Randomization (DDR) is
designed to stabilize autonomous underwater vehicles and the platforms of underwater …

Designing controllers for compliant, underactuated robots is challenging and usually
requires a learning procedure. Learning robotic control in simulated environments can …

In this work, deep reinforcement learning methodology takes advantage of transfer learning
methodology to achieve a reasonable trade-off between environmental impact and …

Transfer Learning (TL) has received a great deal of attention because of its ability to speed
up Reinforcement Learning (RL) by reusing learned knowledge from other tasks. This paper …

Transfer learning is an effective approach for adapting an autonomous agent to a new target
task by transferring knowledge learned from the previously learned source task. The major …

Designing controllers for compliant, underactuated robots is challenging and usually
requires a learning procedure. Learning robotic control in simulated environments can …

Designing controllers for compliant, underactuated robots is challenging and usually
requires a learning procedure. Learning robotic control in simulated environments can …