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Found 6 papers, 1 papers with code
Towards Unsupervised Object Detection From LiDAR Point Clouds
In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes.
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Learning world models can teach an agent how the world works in an unsupervised manner.
Learning Realistic Traffic Agents in Closed-loop
Our experiments show that RTR learns more realistic and generalizable traffic simulation policies, achieving significantly better tradeoffs between human-like driving and traffic compliance in both nominal and long-tail scenarios.
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective
Intuitively, learning from those arbitrary demonstrations can be seen as a form of imitation learning (IL).
Learning Latent Landmarks for Generalizable Planning
Planning - the ability to analyze the structure of a problem in the large and decompose it into interrelated subproblems - is a hallmark of human intelligence.
World Model as a Graph: Learning Latent Landmarks for Planning
Planning - the ability to analyze the structure of a problem in the large and decompose it into interrelated subproblems - is a hallmark of human intelligence.
