Yingdong Hu

I am a fourth-year Ph.D. student at Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, advised by Prof. Yang Gao. Previously, I obtained my bachelor's degree from Beijing University of Posts and Telecommunications (BUPT).

My research focuses on Embodied AI, a frontier domain at the intersection of machine learning, robotics and computer vision. I investigate the fundamental challenges of developing general-purpose robotic systems that can effectively adapt and generalize their learned behaviors across diverse, unstructured real-world environments.

Email  /  Google Scholar  /  Github  /  WeChat

Here are representative papers. For more works, please refer to my Google Scholar.

We demonstrate that the policy’s generalization ability to new objects, new environments, or both scales approximately as a power law with the number of training objects, training environments, or training environment-object pairs, respectively.

We introduce ViLa, a novel approach for long-horizon robotic planning that leverages GPT-4V to generate a sequence of actionable steps. ViLa empowers robots to execute complex tasks with a profound understanding of the visual world.

We present Semantic-Geometric Representation (SGR), a universal perception module for robotics that leverages the rich semantic information of large-scale pre-trained 2D models and inherits the merits of 3D spatial reasoning.

We conduct the first thorough evaluation of pre-trained vision model performance across different downstream policy learning methods and environments. We discover that the effectiveness of pre-training is highly dependent on the choice of the downstream policy learning algorithm.

We show that fine-grained features learned with pixel-level self-supervised learning (SSL) objectives are complementary to semantic features from image-level SSL methods. Fusing these features can significantly improve the performance for visual correspondence tasks.

Modified from Jon Barron