Large language models (LLMs) pre-trained on vast internet-scale data hav...
In recent years, Reinforcement Learning (RL) is becoming a popular techn...
The application of Large Language Models (LLMs) to the medical domain ha...
Large language models encode a vast amount of semantic knowledge and pos...
Reinforcement learning (RL) has achieved remarkable success in complex
r...
With strong capabilities of reasoning and a generic understanding of the...
The successful transfer of a learned controller from simulation to the r...
One of the key challenges in deploying RL to real-world applications is ...
Reinforcement learning often suffer from the sparse reward issue in
real...
Safe reinforcement learning (RL) that solves constraint-satisfactory pol...
Adapting to the changes in transition dynamics is essential in robotic
a...
Humans are capable of abstracting various tasks as different combination...
Choosing an appropriate parameter set for the designed controller is cri...
Reinforcement learning shows great potential to solve complex contact-ri...
Transformer has achieved great successes in learning vision and language...
Partially Observable Markov Decision Process (POMDP) provides a principl...
Constrained reinforcement learning (CRL) has gained significant interest...
Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based...
Dynamic game arises as a powerful paradigm for multi-robot planning, for...
Zeroth-order optimization methods and policy gradient based first-order
...
In the trial-and-error mechanism of reinforcement learning (RL), a notor...
Safety is the major consideration in controlling complex dynamical syste...
Global climate changes are related to the ocean's store of carbon. We st...
Safety is essential for reinforcement learning (RL) applied in the real
...
Motion planning under uncertainty is of significant importance for
safet...
The safety constraints commonly used by existing safe reinforcement lear...
Model information can be used to predict future trajectories, so it has ...
Reinforcement learning (RL) has great potential in sequential
decision-m...
Safety is essential for reinforcement learning (RL) applied in real-worl...
Reinforcement learning has shown great potential in developing high-leve...
Safety constraints are essential for reinforcement learning (RL) applied...
Current autonomous driving systems are composed of a perception system a...
Unlike popular modularized framework, end-to-end autonomous driving seek...
Probabilistic vehicle trajectory prediction is essential for robust safe...
We present the design and implementation of a visual search system for r...
As autonomous vehicles (AVs) need to interact with other road users, it ...
Urban autonomous driving decision making is challenging due to complex r...
The decision and planning system for autonomous driving in urban environ...