Jeannette Bohg
Jeannette Bohg is an Associate Professor of Computer Science at Stanford University, where she leads the Interactive Perception and Robot Learning lab. Her research focuses on perception and learning for autonomous robotic manipulation and grasping, with emphasis on methods that are goal-directed, real-time, and multi-modal. She is a Faculty Affiliate of Stanford's Institute for Human-Centered Artificial Intelligence and has received several awards including the 2019 IEEE Robotics and Automation Society Early Career Award and the 2019 ICRA Best Paper Award.
“The paper's core contribution is a principled decomposition of why VLA models fail at contact-rich tasks into two distinct root causes: precision failures and force failures”
Source→“On plug insertion alone, this single change improved success from 30% to 50% (+20pp), and on button push from 12.5% to 57.5% (+45pp, p<.001) (Table 1)”
Source→“CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy — Stanford University | arXiv:2606.12352 | June 2026”
Source→“Listed as co-author; also co-author on TidyBot++ hardware platform used throughout experiments”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.