Recent successes aside, reinforcement learning (RL) still faces significant challenges in its
application to the real-world robotics domain. Guiding the learning process with additional …

Movement Primitives (MPs) are a well-known concept to represent and generate modular
trajectories. MPs can be broadly categorized into two types:(a) dynamics-based approaches …

Symbolic planning and reasoning are powerful tools for robots tackling complex tasks.
However, the need to manually design the symbols restrict their applicability, especially for …

Given the popular presupposition of human reasoning as the standard for learning and
decision making, there have been significant efforts and a growing trend in research to …

Learning to interact with the environment not only empowers the agent with manipulation
capability but also generates information to facilitate building of action understanding and …

Due to changes in the environment and errors that occurred during skill initialization, the
robot's operational skills should be modified to adapt to new tasks. As such, skills learned by …

Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to
different situations. Task-parameterized learning improves the generalization of motion …

With the increasing complexity of industrial production and manufacturing tasks, industrial
robots are expected to learn intricate operations from simple actions easily and quickly with …

Current advancements in reinforcement learning (RL) have predominantly focused on
learning step-based policies that generate actions for each perceived state. While these …

Imitation learning approaches achieve good generalization within the range of the training
data, but tend to generate unpredictable motions when querying outside this range. We …