As a popular concept proposed in the field of psychology, affordance has been regarded as
one of the important abilities that enable humans to understand and interact with the …

We present an end-to-end, model-based deep reinforcement learning agent which
dynamically attends to relevant parts of its state during planning. The agent uses a …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

While large language models (LLMs) are successful in completing various language
processing tasks, they easily fail to interact with the physical world by generating control …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

Decision-making AI agents are often faced with two important challenges: the depth of the
planning horizon, and the branching factor due to having many choices. Hierarchical …

Learning models of the environment from pure interaction is often considered an essential
component of building lifelong reinforcement learning agents. However, the common …

Picking up an entangled object is a difficult manipulation task due to its rich contact
dynamics. Most existing solutions fail to produce grasp poses to enable reliable …

While large language models (LLMs) are successful in completing various language
processing tasks, they easily fail to interact with the physical world properly such as …

Social affordances, although key in human-robot interaction processes, have received little
attention in robotics. Hence, it remains unclear whether the prevailing mechanisms to exploit …