Welcome to the Robot Perception and Learning (RPL) Lab at the University of Texas at Austin! Our research focuses on two intimately connected research threads: Robotics and Embodied AI. We investigate the synergistic relations of perception and action in embodied agents and build algorithms and systems that give rise to general-purpose robot autonomy.
In Robotics, we develop methods and principles that enable autonomous robots to reason about the real world through their senses, perform a wide range of tasks flexibly, and learn new tasks adaptively. To deploy generalist robots in the wild, we must deal with the variability and uncertainty of unstructured environments. We address this challenge by closing the perception-action loop using robot perception and learning techniques. In Embodied AI, we build computational frameworks of embodied agents. In these frameworks, perception arises from an embodied agent's active, situated, and skillful interactions in the open world, and its ability to make sense of the world through the lenses of perception, in turn, facilitates intelligent behaviors.
Our work draws theories and methods from robotics, machine learning, and computer vision, along with inspirations from human cognition, neuroscience, and philosophy, to solve open problems at the forefront of Robotics and AI. We are always looking for talented members to join our group.
We are co-organizing a CoRL'23 workshop on Towards Reliable and Deployable Learning-Based Robotic Systems.