Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …

Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous
driving systems in simulation. This work introduces a data-driven method called TrafficGen …

We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …

Robotic applications often involve working in environments that are uncertain, dynamic, and
partially observable. Recently, diffusion models have been proposed for learning trajectory …

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-
world settings such as negotiation, medical diagnosis, and criminal investigation. It serves …

In this work, we study the problem of how to leverage instructional videos to facilitate the
understanding of human decision-making processes, focusing on training a model with the …

Human intervention is an effective way to inject human knowledge into the training loop of
reinforcement learning, which can bring fast learning and ensured training safety. Given the …

When learning common skills like driving, beginners usually have domain experts standing
by to ensure the safety of the learning process. We formulate such learning scheme under …

As a promising topic in cognitive robotics, neuro-symbolic modeling integrates symbolic
reasoning and neural representation altogether. However, previous neuro-symbolic models …

The successful integration of autonomous robots in real-world environments strongly
depends on their ability to reason from context and take socially acceptable actions. Current …