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 intelligent algorithms that give rise to general-purpose robot autonomy.
In Robotics, we develop methods and mechanisms that enable autonomous robots to reason about the real world through their senses, to flexibly perform a wide range of tasks, and to adaptively learn new tasks. To deploy general-purpose robot autonomy in the wild, we have to deal with the variability and uncertainty of the 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 out for talented members to join our group.
Our lab has four papers accepted at ICRA 2023, RSS 2023 and ICML 2023.
Our MineDojo work received the Outstanding Paper Award at NeurIPS 2022.
We have two papers accepted at CoRL 2022 and two at NeurIPS 2022.
We received a second Amazon Research Award on our 3D perception research.
Our ACID paper is nominated as a finalist for the Best Student Paper award at RSS 2022.
Our MAPLE paper won the Outstanding Learning Paper award at ICRA 2022.