Training People to Reward Robots (2505.10151v1)

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.

Training Novices to Efficiently Teach Robots via Reinforcement Learning from Demonstration

This paper presents a novel framework for utilizing Machine Teaching (MT) to enhance the teaching abilities of novice users in Reinforcement Learning from Demonstration (RLfD) settings. The authors aim to address the challenge of guiding non-expert teachers to deliver expert-level demonstrations efficiently, thereby improving the quality of data used by robots to learn new skills.

The research investigates the application of MT principles within RLfD, a method where robots learn behaviors through observation and imitation of human demonstrations. The paper is motivated by the need to develop methods for non-experts to provide effective demonstrations, particularly in tasks where the reward function is implicitly difficult to specify, or where task dynamics can change.

Key Experimental Findings

The experiments conducted validate several important claims:

1. Improvement in Teaching Quality: Through MT-based guided scaffolding, novice teachers exhibited substantial improvements in the quality of their demonstrations. Specifically, there was an 89% increase in robot learning performance on the skills seen during training. These metrics were quantified using standard error metrics such as the Absolute Demonstration Error (ADE) and normalized evaluations based on reward functions.
2. Transferability of Skills: The skills acquired during the MT-guided training were not just confined to the training task; they showed a significant transferability aspect. The paper reports a 70% improvement in the performance of robots on tasks that were unrelated to those encountered during training sessions, indicating that the learned teaching strategies generalized well across different skills.
3. Practical Implications: The approach could serve as a training protocol for workers in sectors where robot interaction is pivotal, potentially mitigating the challenges of job displacement due to automation. By reducing the dependency on expert demonstrations, the framework facilitates a more democratized approach to robot training, particularly in industrial and service applications.

Theoretical and Practical Implications

Theoretically, the proposed use of MT to quantify and optimize demonstration quality extends the concept of curriculum learning to RL domains, where demonstration efficiency and reward function approximation are critical. The use of Least Squares Policy Iteration (LSPI) as the backbone algorithm demonstrates a solid choice for integration with MT due to its computational efficiency and applicability in robotic control contexts.

Practically, this approach can be leveraged to develop training programs for non-expert employees or end-users, enabling them to effectively teach robots without extensive prior knowledge in machine learning or robotics. The MT-based guidance serves as a scaffolding method, reducing the amount of demonstration data required while enhancing learning effectiveness—a crucial factor in real-world applications where data acquisition can be expensive or labor-intensive.

Future Directions in AI Research

The framework outlined in this paper opens several potential avenues for further research. Future work could focus on extending this approach to more complex multi-agent robotic systems or exploring adaptive MT algorithms that can personalize training protocols based on initial teacher performance. Additionally, the integration of advanced RL algorithms or hybrid models incorporating generative methods might offer improved capabilities for handling a broader range of tasks with varied complexity.

In summary, this research provides a comprehensive examination of how MT can be applied to enhance the ability of novices to teach robots effectively in RL settings. The robust experimental validation demonstrates substantial improvements in teaching quality and skill transfer, suggesting broad applicability in future AI developments in industrial and service automation.