Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …

Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual
reinforcement learning (RL) algorithms. Notably, employing simple observation …

Can we endow visuomotor robots with generalization capabilities to operate in diverse open-
world scenarios? In this paper, we propose\textbf {Maniwhere}, a generalizable framework …

As the challenges in autonomous driving become more complex and changing, traditional
methods are struggling to cope. As a result, artificial intelligence (AI) techniques have …

In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks. We
revisit a simple Learning-from-Scratch (LfS) baseline that incorporates data augmentation …

Plasticity, the ability of a neural network to evolve with new data, is crucial for high-
performance and sample-efficient visual reinforcement learning (VRL). Although methods …

Recent advances in visual reinforcement learning (RL) have led to impressive success in
handling complex tasks. However, these methods have demonstrated limited generalization …

Multi-agent reinforcement learning (MARL) has achieved promising results in recent years.
However, most existing reinforcement learning methods require a large amount of data for …

The uncertain delay characteristic of actuators is a critical factor that affects the control
effectiveness of the active suspension system. Therefore, it is crucial to develop a control …

Recent approaches have utilized self-supervised auxiliary tasks as representation learning
to improve the performance and sample efficiency of vision-based reinforcement learning …