This paper presents a comprehensive survey of data-driven robotic visual grasping
detection (DRVGD) for unknown objects. We review both object-oriented and scene …

General-purpose robots coexisting with humans in their environment must learn to relate
human language to their perceptions and actions to be useful in a range of daily tasks …

A long-standing goal in robotics is to build robots that can perform a wide range of daily
tasks from perceptions obtained with their onboard sensors and specified only via natural …

Recent works have shown that Large Language Models (LLMs) can be applied to ground
natural language to a wide variety of robot skills. However, in practice, learning multi-task …

Benefiting from language flexibility and compositionality, humans naturally intend to use
language to command an embodied agent for complex tasks such as navigation and object …

Robotic Grasping has always been an active topic in robotics since grasping is one of the
fundamental but most challenging skills of robots. It demands the coordination of robotic …

Object rearrangement has recently emerged as a key competency in robot manipulation,
with practical solutions generally involving object detection, recognition, grasping and high …

In this paper, we examine the problem of rearranging many objects on a tabletop in a
cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a …

Interactive robotic grasping using natural language is one of the most fundamental tasks in
human-robot interaction. However, language can be a source of ambiguity, particularly …

Language-based communications are essential in human-robot interaction, especially for
the majority of non-expert users. In this paper, we present SeeAsk, an open-world interactive …