Robot Perception and Learning Lab
The Robot Perception and Learning Lab at UT Austin investigates the synergistic relations of perception and action in embodied agents. The lab develops algorithms and systems for general-purpose robot autonomy, enabling robots to reason about the world through sensing and learn new tasks adaptively.
Recent publications
All papers →Matched by this lab's specialties (keyword overlap + direct affiliation)
Towards embodied AI in manufacturing: Review, Evaluation, and Future directions
Yexing Zheng, Zhengyang Ling, Qinghua Wang +5 more
Robotics and Computer-Integrated Manufacturing · 2027
Learning passive variable impedance skills for contact-rich tasks via conservative extended dynamical systems
Pingyun Nie, Jiexin Zhang, Tianxiang Jiang +4 more
Robotics and Computer-Integrated Manufacturing · 2027
A hierarchical approach to imitation learning for manipulation tasks requiring time varying forces
Rishabh Shukla, Adithya Santhosh, Shaili Gandhi +2 more
Robotics and Computer-Integrated Manufacturing · 2026
An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
Zhe Liu, Huanbo Jin, Zhaohui Du +8 more
2026
What Are We Actually Benchmarking in Robot Manipulation?
Tianchong Jiang, Xiangshan Tan, Samuel Wheeler +3 more
2026
DLO-Lab: Benchmarking Deformable Linear Object Manipulations with Differentiable Physics
Junyi Cao, Yian Wang, Ziyan Xiong +3 more
2026