Abstract-- Incorporating empathetic behavior into robots can improve their social effectiveness and interaction quality. In this paper, we present whEE (when and how to express empathy), a framework that enables social robots to detect when empathy is needed and generate appropriate responses. Using large language models, whEE identifies key behavioral empathy cues in human interactions. We evaluate it in human-robot interaction scenarios with our social robot, Haru. Results show that whEE effectively identifies and responds to empathy cues, providing valuable insights for designing social robots capable of adaptively modulating their empathy levels across various interaction contexts. In most scenarios, Large Language Models (LLMs) represent the state-of-the-art approach for classifying empathy [1], [2] and generating empathetic responses [3], [4]. However, the development of robots capable of dynamically adjusting their level of empathy based on the context remains an underexplored area [5]. To this end, we introduce whEE (when and how to express empathy), an empathy framework that provides guidelines on when robots should respond empathetically and how to achieve it. Using our framework, we analyze the utterances of speakers and listeners in dyadic and group conversations with varying levels of empathy. Our analysis identifies key empathy cues that indicate when a speaker seeks an empathetic response and the cues exhibited by listeners displaying high levels of empathy. We approach empathy by focusing on observable behaviors that individuals exhibit when demonstrating an understanding of others' emotions and engaging deeply with their experiences--referred to as behavioral empathy [6].

Social robots have had it tough recently. There are lots of reasons for this, but a big part of it is that it's a challenge to develop a social robot that's able to spark long-term user interest without driving initial expectations impractically high. This isn't just the case for commercial robots--social robots designed for long-term user interaction studies have the same sorts of issues. The Honda Research Institute is well aware of how tricky this is, and researchers there have been working on the design of a prototype social robot that achieves a "balance between human expectation, surface appearance, physical affordance, and robot functionality." It's called Haru, and Honda Research has provided a fascinating and detailed look into how they came up with its design.