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Teaching robot policies without new demonstrations: interview with Jiahui Zhang and Jesse Zhang
Original from Robohub.org: Teaching robot policies without new demonstrations: interview with Jiahui Zhang and Jesse Zhang

Teaching robot policies without new demonstrations: interview with Jiahui Zhang and Jesse Zhang

At the Conference on Robot Learning (CoRL) 2025, researchers Jiahui Zhang, Yusen Luo, Abrar Anwar, and Sumedh A. Sontakke introduced the ReWiND method, a novel approach designed to enhance robotic learning through language-guided rewards. This method unfolds in three distinct phases: first, it involves learning a reward function; next, it incorporates pre-training; and finally, it applies the learned reward function alongside the pre-trained policy to tackle new language-specific tasks in real-time. The motivation behind this research is to enable robots to adapt to new tasks without requiring additional demonstrations, thereby streamlining the learning process. By leveraging language as a guiding tool, the ReWiND method aims to improve the efficiency and effectiveness of robotic task execution.

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