EmpathyAgent: Can Embodied Agents Conduct Empathetic Actions?
This work addresses the overlooked need for empathetic behavior in embodied agents, providing a standard benchmark for the research community, though it is incremental as it focuses on evaluation rather than a new method.
The authors tackled the problem of evaluating empathetic actions in embodied agents by introducing EmpathyAgent, a benchmark with 10,000 multimodal samples, and found that current models struggle with empathetic actions while training Llama3-8B showed potential for enhancement.
Empathy is fundamental to human interactions, yet it remains unclear whether embodied agents can provide human-like empathetic support. Existing works have studied agents' tasks solving and social interactions abilities, but whether agents can understand empathetic needs and conduct empathetic behaviors remains overlooked. To address this, we introduce EmpathyAgent, the first benchmark to evaluate and enhance agents' empathetic actions across diverse scenarios. EmpathyAgent contains 10,000 multimodal samples with corresponding empathetic task plans and three different challenges. To systematically evaluate the agents' empathetic actions, we propose an empathy-specific evaluation suite that evaluates the agents' empathy process. We benchmark current models and found that exhibiting empathetic actions remains a significant challenge. Meanwhile, we train Llama3-8B using EmpathyAgent and find it can potentially enhance empathetic behavior. By establishing a standard benchmark for evaluating empathetic actions, we hope to advance research in empathetic embodied agents. Our code and data are publicly available at https://github.com/xinyan-cxy/EmpathyAgent.