Ep#110: ENPIRE: Agentic Robot PolicySelf-Improvement in the Real World

Podcast: RoboPapers · Chris Paxton and Michael Cho

Opublikowano: 2026-10-07

In many fields, automated research has become increasingly plausible with the improving capabilities of frontier language models, and the field robotics may be no exception. In ENPIRE, Wenli Xiao, Jia Xie, and Tonghe Zhang build a harness framework that captures physical feedback and allows for automated robotics research, minimizing human effort while training policies that achieve a 99% success rate on challenging, dexterous tasks like organizing a pin box or fastening a zip tie. Learn more in Episode 110 of RoboPapers, with Michael Cho, Chris Paxton, and Ruijie He! Abstract Achieving dexterous robotic manipulation in...