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)
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
DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation
Jusuk Lee, Seungjae Lee, Jonghun Shin +6 more
2026
VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models
Dehao Huang, Aoxiang Gu, Chengjie Zhang +5 more
2026
MiraBench: Evaluating Action-Conditioned Reliability in Robotic World Models
Tianzhuo Yang, Zihan Shen, Zirui Mi +7 more
2026
Technical challenges in autonomous robotic ultrasound examinations: perception, planning, and control.
Guo D, Zhang Y, Huang X +1 more
Journal of robotic surgery · 2026
Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models
Jiyao Zhang, Mingxu Zhang, Yitong Peng +8 more
2026