Robot Learning and Perception Lab (RoboPIL)
RoboPIL at Stanford focuses on robot learning at the intersection of robotics, computer vision, and machine learning. They specialize in structured world models, embodied intelligence, and multi-modal perception for robotic manipulation of deformable objects.
Notable achievements
Research on physics-inspired predictive models, robotic foundation models, multi-modal perception integration
Notable work
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
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
VIA: Visual Interface Agent for Robot Control
Hengyuan Hu, Priya Sundaresan, Jensen Gao +1 more
2026
A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation
Liuyi Wang, Kai Sheng, Zongtao He +8 more
2026
An Attention-based Model for Robust Forecasting with Missing Modality
Zhitian Zhang, Wenjie Zi, Yunduz Rakhmangulova +3 more
2026
An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
Zhe Liu, Huanbo Jin, Zhaohui Du +8 more
2026