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.
- We released robomimic, an open-source framework for robot learning from demonstration.
- Talk recordings of our CVPR'21 workshop on 3D Vision and Robotics are released on our YouTube channel.
- We are organizing the Workshop on Visual Learning and Reasoning for Robotics at RSS 2021.
Our lab received Amazon Research Award for research on 3D perception for robot manipulation.
Our work on multi-arm manipulation is nominated as finalist for Best Multi-robot Systems Paper.