Better Apprenticeship Learning with LLM Explanations

As the population ages, care robots will play an increasing role in assisting caregiving by taking on repetitive or physically cumbersome activities. To effectively provide care, robotic agents must be able to meet the needs and preferences of care receivers. However, these needs and preferences may change over time, making it intractable to pre-define the way the care robot should act before deployment. Instead, the care robot should be able to learn directly from non-expert end-user demonstrations. However, prior work investigating the feasibility of learning a policy from older adult demonstrations finds that older adult demonstrators desire a better understanding of what the robot needs them to do, and how. To help demonstrators understand how to improve on suboptimal or heterogeneous demonstrations, we propose to utilize a Large Language Model to provide human-interpretable explanations of Shapley values of a policy. These explanations enable the demonstrator to understand how the policy is performing, and what changes are needed, informing their corrective demonstrations. We showcase our framework's performance in deterministic and stochastic versions of Wumpus World.