Interactive Perception and Robot Learning Lab
The Interactive Perception and Robot Learning Lab at Stanford advances robust sensorimotor coordination at the intersection of robotics, machine learning, and computer vision. Research focuses on autonomous robots that can plan and execute complex manipulation tasks in dynamic, uncertain, and unstructured environments.
Notable achievements
TidyBot, TidyBot++, Mobi-π, HoMeR, Masquerade
Notable work
Recent publications
All papers →Matched by this lab's specialties (keyword overlap + direct affiliation)
A fusion prediction model of tool wear based on physical information and machine learning in five-axis milling TC4 titanium alloy
Shaoqing Qin, Lida Zhu, Yanpeng Hao +7 more
Robotics and Computer-Integrated Manufacturing · 2026
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
Generalized machine learning model for deformation prediction and compensation in robotic machining
Taehwa Hong, Gyuho Kim, Seong Hyeon Kim +1 more
Robotics and Computer-Integrated Manufacturing · 2026
Integrating computer vision and Kalman filter in an assistive system for visually impaired individuals to predict hand-object interaction
Amirmohammad Barsalani, Arman Mardani, Hamidreza Daniali
Robotics and Autonomous Systems · 2026
A machine learning–based tool for enhancing position accuracy in industrial robots with a reduced dataset
Giuseppe Romano, Pietro Bilancia, Alberto Locatelli +3 more
Robotics and Computer-Integrated Manufacturing · 2026
How VLAs Fail Differently: Black-Box Action Monitoring Reveals Architecture-Specific Failure Signatures
Krishnam Gupta
2026