Reinforcement learning (RL), particularly its combination with deep neural networks referred
to as deep RL (DRL), has shown tremendous promise across a wide range of applications …

Sampling-based motion planning methods for manipulators in crowded environments often
suffer from expensive collision checking and high sampling complexity, which make them …

Integrating learning-based techniques, especially reinforcement learning, into robotics is
promising for solving complex problems in unstructured environments. However, most …

We study the problem of Distributionally Robust Constrained RL (DRC-RL), where the goal
is to maximize the expected reward subject to environmental distribution shifts and …

We present hierarchical policy blending as optimal transport (HiPBOT). HiPBOT
hierarchically adjusts the weights of low-level reactive expert policies of different agents by …

Reinforcement Learning (RL) allows learning non-trivial robot control laws purely from data.
However, many successful applications of RL have relied on ad-hoc regularizations, such as …

This study presents a novel methodology incorporating safety constraints into a robotic
simulation during the training of deep reinforcement learning (DRL). The framework …

Trajectory planning under kinodynamic constraints is fundamental for advanced robotics
applications that require dexterous, reactive, and rapid skills in complex environments …

Safety is one of the key issues preventing the deployment of reinforcement learning
techniques in real-world robots. While most approaches in the Safe Reinforcement Learning …

Unmanned underwater vehicles (UUVs) face challenges such as high hardware costs,
security concerns, a lack of training data in the actual development and debugging. Creating …