In this study, we are interested in imbuing robots with the capability of physically-grounded
task planning. Recent advancements have shown that large language models (LLMs) …

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite
its progress, current algorithms are still unsatisfactory in virtually every aspect of the …

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 …

Visual Reinforcement Learning (RL) is a promising approach to achieve human-like
intelligence. However, it currently faces challenges in learning efficiently within noisy …

Recently, various pre-training methods have been introduced in vision-based
Reinforcement Learning (RL). However, their generalization ability remains unclear due to …

Designing generalizable agents capable of adapting to diverse embodiments has achieved
significant attention in Reinforcement Learning (RL), which is critical for deploying RL …

Abstract In visual Reinforcement Learning (RL), one of the key problems is how to learn
policies, which can be generalized to unseen environments. Recently, saliency guidance …

Acquiring a multi-task imitation policy in 3D manipulation poses challenges in terms of
scene understanding and action prediction. Current methods employ both 3D representation …

Visual imitation learning methods demonstrate strong performance, yet they lack
generalization when faced with visual input perturbations, including variations in lighting …

Effectively generalizing learned policies to unseen environments remains challenging in
Visual Reinforcement Learning (Visual RL). Data Augmentation (DA) is widely used in …