Large language models (LLMs) have undergone significant expansion and have been
increasingly integrated across various domains. Notably, in the realm of robot task planning …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

Robot learning of manipulation skills is hindered by the scarcity of diverse, unbiased
datasets. While curated datasets can help, challenges remain in generalizability and real …

Imitation learning from human demonstrations is a powerful framework to teach robots new
skills. However, the performance of the learned policies is bottlenecked by the quality, scale …

Zero-shot object navigation is a challenging task for home-assistance robots. This task
emphasizes visual grounding, commonsense inference and locomotion abilities, where the …

Recent vision-language-action (VLA) models rely on 2D inputs, lacking integration with the
broader realm of the 3D physical world. Furthermore, they perform action prediction by …

The pre-train and fine-tune paradigm in machine learning has had dramatic success in a
wide range of domains because the use of existing data or pre-trained models on the …

Learning a generalist embodied agent capable of completing multiple tasks poses
challenges, primarily stemming from the scarcity of action-labeled robotic datasets. In …

Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with learning to achieve
multiple goals in an environment purely from offline datasets using sparse reward functions …

Embodied AI agents require a fine-grained understanding of the physical world mediated
through visual and language inputs. Such capabilities are difficult to learn solely from task …