This survey explores the transformative role of Generative Artificial Intelligence (GenAI) in
enhancing the trustworthiness, reliability, and security of autonomous systems such as …

In this paper, we propose a real-world benchmark for studying robotic learning in the context
of functional manipulation: a robot needs to accomplish complex long-horizon behaviors by …

Multimodal Vision Language Models (VLMs) have emerged as a transformative technology
at the intersection of computer vision and natural language processing, enabling machines …

Large-scale endeavors like and widespread community efforts such as Open-X-Embodiment
have contributed to growing the scale of robot demonstration data. However, there is still an …

Grounding the reasoning ability of large language models (LLMs) for embodied tasks is
challenging due to the complexity of the physical world. Especially, LLM planning for multi …

We introduce Latent Action Pretraining for general Action models (LAPA), an unsupervised
method for pretraining Vision-Language-Action (VLA) models without ground-truth robot …

Learned language-conditioned robot policies often struggle to effectively adapt to new real-
world tasks even when pre-trained across a diverse set of instructions. We propose a novel …

We explore how to enhance next-token prediction models to perform in-context imitation
learning on a real robot, where the robot executes new tasks by interpreting contextual …

Intelligent instruction-following robots capable of improving from autonomously collected
experience have the potential to transform robot learning: instead of collecting costly …

Humans possess the visual-spatial intelligence to remember spaces from sequential visual
observations. However, can Multimodal Large Language Models (MLLMs) trained on million …