Several earlier studies have shown impressive control performance in complex robotic
systems by designing the controller using a neural network and training it with model-free …

Safe reinforcement learning (RL) focuses on training reward-maximizing agents subject to
pre-defined safety constraints. Yet, learning versatile safe policies that can adapt to varying …

In this paper, we consider the problem of learning safe policies for probabilistic-constrained
reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high …

Ensuring safety is critical when applying reinforcement learning (RL) to real-world problems.
Consequently, safe RL emerges as a fundamental and powerful paradigm for safely …

Generalizing policies across different domains with dynamics mismatch poses a significant
challenge in reinforcement learning. For example, a robot learns the policy in a simulator …

In safety-critical robotic tasks, potential failures must be reduced, and multiple constraints
must be met, such as avoiding collisions, limiting energy consumption, and maintaining …

Many real-world domains require safe decision making in uncertain environments. In this
work, we introduce a deep reinforcement learning framework for approaching this important …

Current constrained reinforcement learning (RL) methods guarantee constraint satisfaction
only in expectation, which is inadequate for safety-critical decision problems. Since a …

Learning from Demonstration (LfD) is a powerful method for non-roboticists end-users to
teach robots new tasks, enabling them to customize the robot behavior. However, modern …

Safe reinforcement learning (RL) has shown great potential for building safe general-
purpose robotic systems. While many existing works have focused on post-training policy …