The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

Language-conditioned robotic manipulation represents a cutting-edge area of research,
enabling seamless communication and cooperation between humans and robotic agents …

Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems,
trains an agent to achieve different goals under particular scenarios. Compared to the …

Learning provides a powerful tool for vision-based navigation, but the capabilities of
learning-based policies are constrained by limited training data. If we could combine data …

Natural language is perhaps the most flexible and intuitive way for humans to communicate
tasks to a robot. Prior work in imitation learning typically requires each task be specified with …

Building autonomous machines that can explore open-ended environments, discover
possible interactions and build repertoires of skills is a general objective of artificial …

We address the task of generating temporally consistent and physically plausible images of
actions and object state transformations. Given an input image and a text prompt describing …

Multistep tasks, such as block stacking or parts (dis) assembly, are complex for autonomous
robotic manipulation. A robotic system for such tasks would need to hierarchically combine …

Offline goal-conditioned RL (GCRL) offers a way to train general-purpose agents from fully
offline datasets. In addition to being conservative within the dataset, the generalization …

Developmental machine learning studies how artificial agents can model the way children
learn open-ended repertoires of skills. Such agents need to create and represent goals …