Recent advancements in Artificial Intelligence (AI) have largely been propelled by scaling. In
Robotics, scaling is hindered by the lack of access to massive robot datasets. We advocate …

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

Recent years in robotics and imitation learning have shown remarkable progress in training
large-scale foundation models by leveraging data across a multitude of embodiments. The …

To realize effective large-scale, real-world robotic applications, we must evaluate how well
our robot policies adapt to changes in environmental conditions. Unfortunately, a majority of …

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 …

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 …

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

The advancement of large Vision-Language-Action (VLA) models has significantly improved
robotic manipulation in terms of language-guided task execution and generalization to …

With large language models, robots can understand language more flexibly and more
capable than ever before. This survey reviews and situates recent literature into a spectrum …

Recent works in the robot learning community have successfully introduced generalist
models capable of controlling various robot embodiments across a wide range of tasks, such …