Training People to Reward Robots
Year: 2025
Type: conference-paper
Abstract: Learning from demonstration (LfD) is a technique that allows expert teachers to teach task-oriented skills to robotic systems. However, the most effective way of guiding novice teachers to approach expert-level demonstrations quantitatively for specific teaching tasks remains an open question. To this end, this paper investigates the use of machine teaching (MT) to guide novice teachers to improve their teaching skills based on reinforcement learning from demonstration (RLfD). The paper reports an experiment in which novices receive MT-derived guidance to train their ability to teach a given motor skill with only 8 demonstrations and generalise this to previously unseen ones. Results indicate that the MT-guidance not only enhances robot learning performance by 89% on the training skill but also causes a 70% improvement in robot learning performance on skills not seen by subjects during training. These findings highlight the effectiveness of MT-guidance in upskilling human teaching behaviours, ultimately improving demonstration quality in RLfD.
Authors: Endong Sun, Yuqing Zhu, Matthew Howard
Institution: King's College London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W4416749286