5 accepted papers
Language-Conditioned Grasping (LCG) is an essential skill for robotic manipulation and has attracted increasing interest. Recent LCG models have made great progress, but need numerous paired image-text-pose annotations for fully supervised learning, which are tedious and expensive. Semi-supervised l
This letter concentrates on the challenging task of language-conditioned grasping detection in clutter, where the grasping postures of objects should be generated for robots according to complicated human instructions. Existing methods typically employ well-trained object detectors and leverage lang
The ability to search for objects is a fundamental prerequisite for mobile robots when addressing a wide range of automation tasks. However, how to effectively estimate the positions of unobserved objects in a continuously changing environment remains an open challenge. Previous works have utilized
Multilingual language models (MLLMs) have achieved remarkable success in various cross-lingual transfer tasks. However, they suffer poor performance in zero-shot low-resource languages, particularly when dealing with longer contexts. Existing research mainly relies on full-model fine-tuning on large…
For high-level human-robot interaction tasks, 3D scene understanding is important and non-trivial for autonomous robots. However, parsing and utilizing effective environment information of the 3D scene is not trivial due to the complexity of the 3D environment and the limited ability for reasoning a…