Building effective imitation learning methods that enable robots to learn from limited data
and still generalize across diverse real-world environments is a long-standing problem in …

Intelligent transportation systems play a crucial role in modern traffic management and
optimization, greatly improving traffic efficiency and safety. With the rapid development of …

Vision-based robot policy learning, which maps visual inputs to actions, necessitates a
holistic understanding of diverse visual tasks beyond single-task needs like classification or …

The ability to predict the future trajectories of traffic participants is crucial for the safe and
efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model …

Recent work has shown diffusion models are an effective approach to learning the
multimodal distributions arising from demonstration data in behavior cloning. However, a …

Diffusion Models (DMs) are probabilistic models that create realistic samples by simulating
the diffusion process, gradually adding and removing noise from data. These models have …

This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model a novel
network architecture for highway trajectory prediction using diffusion models. The proposed …

Traditional methods for synthesizing 6-DoF grasp poses from 3D observations often rely on
geometric heuristics, resulting in poor generalizability, limited grasp options, and higher …

Offline reinforcement learning (RL) aims to learn optimal policies from previously collected
datasets. Recently, due to their powerful representational capabilities, diffusion models have …

In the context of spatial-temporal autonomous driving, the accurate and real-time trajectory
prediction of the surrounding vehicle (SV) is crucial. This paper aims to design an efficient …