Q&A: How will policy shape the future of AI in Pennsylvania? - Penn State University
<a href="https://news.google.com/rss/articles/CBMikAFBVV95cUxPN25LVldydWFrQWxJMHQxbk9VVFE1QXZXZk13Vm1WUVYwdU12NlhkM3VLUzNCd2NwUHRUOWlsV2J5cEo0VXpzZjYxNmt1Tl93RXl6bVdNTUtwZlZ2V0tkek1FTlJ1dEZxYVY1MHVyUEhGNXZUaldEUHMtMW80djBMMy1DenpRMFRsVTRFbXhLdUQ?oc=5" target="_blank">Q&A: How will policy shape the future of AI in Pennsylvania?</a> <font color="#6f6f6f">Penn State University</font>
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Real-Time Operator Takeover for Visuomotor Diffusion Policy Training
arXiv:2502.02308v3 Announce Type: replace Abstract: We present a Real-Time Operator Takeover (RTOT) paradigm that enables operators to seamlessly take control of a live visuomotor diffusion policy, guiding the system back to desirable states or providing targeted corrective demonstrations. Within this framework, the operator can intervene to correct the robot's motion, after which control is smoothly returned to the policy until further intervention is needed. We evaluate the takeover framework on three tasks spanning rigid, deformable, and granular objects, and show that incorporating targeted takeover demonstrations significantly improves policy performance compared with training on an equivalent number of initial demonstrations alone. Additionally, we provide an in-depth analysis of the
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