EmpathyAgent: Can Embodied Agents Conduct Empathetic Actions?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 52 references that are in the map. First posted on arXiv 19 Mar 2025.
Builds on
26 of 52 references.
References · 26
- 2023GPT-4 Technical Reportcited by 18,132
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
- 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,611
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
- 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
- 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,007
14 more
- 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
- 2016SPICE: Semantic Propositional Image Caption Evaluationcited by 820
- 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
- 2023VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Modelscited by 733
- 2018VirtualHome: Simulating Household Activities via Programscited by 411
- 2023SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agentscited by 350
- 2021Symbolic Knowledge Distillation: from General Language Models to Commonsense Modelscited by 270
- 2023Building Cooperative Embodied Agents Modularly with Large Language Modelscited by 231
- 2023ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulationcited by 157
- 2020Watch-And-Help: A Challenge for Social Perception and Human-AI Collaborationcited by 131
- 2022Evaluating and Inducing Personality in Pre-trained Language Modelscited by 122
- 2023Could a Large Language Model be Conscious?cited by 70
- 2023Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigationcited by 18
- 2019Evaluating Empathy in Artificial Agentscited by 7
Built on by
Citing papers per year: 2026: 2.
2 papers.