GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations
GRACE combines LLMs and human explanations, boosting socially appropriate robot actions.
Key Findings
Methodology
GRACE employs large language models (e.g., GPT-4) to extract common sense knowledge, classifies scene certainty via unsupervised clustering, and uses a bidirectional autoencoder to fuse LLM predictions with human explanations. The system includes scene uncertainty detection, LLM-based behavior scoring, and explanation-enhanced modules, addressing the challenge of balancing norm adherence with user preferences.
Key Results
- On MannersDB+ dataset, GRACE reduces RMSE of behavior appropriateness prediction to 0.45, outperforming traditional ML models (e.g., Random Forest 0.58) and pure LLM predictions (0.52), with over 20% accuracy improvement. Incorporating human explanations further improves performance in uncertain scenes by 15%, enabling coherent explanation generation, thus increasing transparency. Results across multiple robots demonstrate robustness and generalization.
- Ablation studies confirm the autoencoder's role in effectively integrating explanations, significantly boosting prediction accuracy, especially in ambiguous scenarios.
- The system's predictions closely match human scores with high correlation coefficients (PCC, CCC), validating its effectiveness.
Significance
This work advances socially aware robotics by integrating LLM reasoning with human explanations, enabling robots to generate more natural, trustworthy behaviors. It addresses longstanding issues of interpretability and personalization, paving the way for robots to operate seamlessly in human environments like homes and public spaces, with broad implications for industry and academia.
Technical Contribution
Introduces a bidirectional conditional autoencoder framework that fuses LLM outputs with human explanations, enhancing interpretability and accuracy. Implements scene uncertainty classification to adaptively decide when to rely on LLM predictions or explanations. Demonstrates end-to-end behavior scoring and explanation generation, outperforming baselines on multiple metrics, and providing a new paradigm for socially aware robot behavior generation.
Novelty
First to integrate large language model reasoning with human explanations via a bidirectional autoencoder, creating a closed-loop system for behavior appropriateness scoring and explanation generation. Unlike prior work that relies solely on scene features or unidirectional models, this approach dynamically adjusts behaviors based on social context and user preferences, filling a critical gap in robot social intelligence.
Limitations
- The reliance on large pre-trained models incurs high computational costs, limiting deployment in resource-constrained environments.
- Handling highly dynamic or complex multi-party scenarios remains challenging, requiring further multimodal integration.
- The quality and consistency of human explanations significantly influence system performance, necessitating standardized explanation collection methods.
Future Work
Future efforts will incorporate multimodal data (visual, auditory) to improve scene understanding, develop online learning for personalization, and optimize real-time performance. Extending the framework to more diverse social contexts and scaling to larger datasets will facilitate broader deployment in real-world applications.
AI Executive Summary
In complex human environments, robots must navigate social norms while accommodating individual preferences. Traditional approaches often rely on static rules or unidirectional models, which struggle to adapt to diverse and ambiguous social scenarios. This limitation hampers the deployment of robots in settings like homes, hospitals, or public spaces, where nuanced social understanding is essential.
The proposed GRACE system addresses these challenges by integrating large language models, such as GPT-4, with human explanations through a bidirectional autoencoder architecture. The system first assesses scene certainty using unsupervised clustering based on human agreement, enabling it to decide whether to rely solely on LLM predictions or to fuse explanations for improved decision-making. In uncertain scenes, the autoencoder combines LLM outputs with human-provided reasons, refining behavior scores and generating coherent explanations that align with social norms.
Experimental results on the MannersDB+ dataset demonstrate that GRACE achieves a significant reduction in prediction error (RMSE of 0.45), outperforming baseline models by over 20%. It also effectively generates human-like explanations, enhancing system transparency and trustworthiness. The approach proves robust across multiple robot platforms, indicating strong generalization capabilities.
This research marks a step forward in socially aware robotics, enabling machines to behave more naturally and explainably in human environments. While computational costs and scene complexity pose ongoing challenges, future work will focus on multimodal data integration, online personalization, and real-time deployment, paving the way for more intelligent, trustworthy robots in daily life.
Deep Analysis
Background
Robots在社会环境中的应用不断扩大，早期研究多集中于导航与任务执行，代表性工作包括Tjomsland等的贝叶斯网络预测、Churamani的联邦学习方法。近年来，随着大语言模型的崛起，研究开始利用其推理能力提升社会行为理解，但多依赖单向预测，缺乏对个性化偏好的考虑。现有系统在融合社会规范与用户偏好方面仍有限，尤其在解释性和适应性方面表现不足。
Core Problem
核心问题在于如何让机器人在复杂场景中合理判断行为的社会适宜性，同时兼顾个性化偏好。传统方法多依赖静态规则或单向模型，难以应对场景模糊或偏好多样的情况。现有系统缺乏有效融合人类解释的机制，导致行为缺乏透明度和个性化，限制了机器人在人类环境中的自然交互能力。这些挑战亟需创新模型架构和融合策略。
Innovation
本研究提出基于双向条件自编码器的融合框架，首次将大语言模型（如GPT-4）生成的常识推理与人类解释结合，提升行为适宜性预测的准确性与解释能力。引入场景不确定性分类机制，有效识别模糊场景，确保在不同场景下采用不同策略。系统实现了端到端的行为评分与解释生成，突破了传统单向预测的限制，为机器人社会行为生成提供了新思路。
Methodology
- �� 通过场景特征提取，使用无监督聚类（K-means++)判断场景确定性。
- �� 利用多种机器学习模型（如SVM、随机森林）进行场景不确定性分类。
- �� 在确定场景中，直接调用LLMs（如GPT-4）进行行为适宜性评分。
- �� 在不确定场景中，采用条件自编码器融合LLM预测与人类解释，进行评分修正与解释生成。
- �� 输入包括场景描述、模型预测、人工评分与解释，编码后通过共享潜在空间进行重建。
- �� 训练目标结合均方误差（MSE）与二元交叉熵（BCE），优化评分与解释的准确性。
- �� 实验采用MannersDB+数据集，评估指标包括RMSE、PCC、CCC，比较多种基线模型。
Experiments
采用MannersDB+数据集，包含多机器人、多场景、多行为的社会适应性标注。评估指标包括行为适宜性评分的RMSE、相关系数，以及解释生成的合理性。对比基线包括传统ML模型、单向LLM预测和自编码器修正模型。通过交叉验证确保模型稳健性，调优超参数如潜在空间维度和损失权重。还进行消融实验验证模型各组成部分的贡献。
Results
GRACE在MannersDB+上实现RMSE为0.45，优于单纯LLM（0.52）和传统ML模型（0.58）。在不确定场景中，性能提升达15%，且能生成合理解释，增强透明度。多机器人测试显示系统具有良好的泛化能力。消融实验确认自编码器结构在融合解释中的关键作用，整体性能优越，验证了模型设计的有效性。
Applications
该系统可应用于家庭助理、公共服务机器人等场景，提升其社会适应性和信任度。实现个性化行为调整，满足不同用户偏好。未来可结合多模态信息（视觉、声音）进一步丰富场景理解，推动机器人在复杂社会环境中的广泛应用。
Limitations & Outlook
系统依赖大规模预训练模型，计算成本高，难以在资源有限设备上部署。对极端复杂或动态变化场景的适应性仍有限，需引入多模态信息增强理解能力。人类解释的质量直接影响系统表现，未来需标准化解释采集流程。
Plain Language Accessible to non-experts
想象你在厨房做饭，机器人就像你的助手。它知道哪些动作在某些情况下是合适的，比如在朋友来访时不要大声喧哗，或者在家里有人睡觉时不要打扫。它通过学习很多人的建议和理由，知道什么时候可以做什么，什么时候不可以。比如，它会记得，‘如果有小孩在附近，就要注意安全’，或者‘如果大家都在休息，就不要打扰’。这样，机器人就能像一个懂事的朋友一样，既遵守规则，又考虑你的偏好，帮你做事，大家都觉得舒服。它还会告诉你为什么这么做，比如说‘因为有小孩在旁边，所以我选择轻声操作’，让你觉得它很贴心。这就像一个聪明又会解释的助手，能让家里变得更温馨、更有序。
ELI14 Explained like you're 14
想象你有个超级聪明的朋友，他不仅知道很多事情，还会告诉你为什么这么做。比如，你在学校遇到问题，他会用简单的话告诉你：‘你应该多复习数学，因为考试快到了’。这个朋友还能根据你的情况，给出特别的建议。比如，你喜欢玩游戏，他会提醒你：‘玩太久会伤眼睛，要注意休息’。这就像是一个懂你、会说话的助手，不仅帮你做事，还会解释原因，让你明白。这个研究就像是在教机器人怎么变成这样的朋友，让它在家里或公共场合都能表现得既懂规矩，又会说话，大家都喜欢它。
Glossary
Large Language Model (大规模语言模型)
一种基于深度学习的模型，能理解和生成自然语言，像GPT-4。它在大量文本上训练，具有强大的推理和知识能力。
用于提取常识知识和行为评分预测。
条件自编码器
一种神经网络结构，能在输入条件下学习数据的潜在表示，进行重建和生成。它结合输入信息，优化融合效果。
在融合人类解释与模型预测中应用。
场景不确定性分类
判断场景中人类意见是否一致的过程，基于场景特征和人类评分的方差。
决定采用纯模型预测还是融合解释。
行为适宜性评分
评估机器人行为是否符合社会规范的数值指标，范围通常为1-5。
作为系统输出的核心评价指标。
MannersDB+数据集
包含多机器人、多场景、多行为的社会适应性标注数据集，用于训练和评估机器人行为生成模型。
本研究的主要实验数据源。
Open Questions Unanswered questions from this research
- 1 如何进一步提升模型在极端复杂场景中的适应性，尤其是在多模态信息融合和实时处理方面仍存在挑战。
- 2 人类解释的质量与一致性对系统性能影响巨大，需探索标准化和自动化的解释采集与评估机制。
Abstract
When operating in human environments, robots need to handle complex tasks while both adhering to social norms and accommodating individual preferences. For instance, based on common sense knowledge, a household robot can predict that it should avoid vacuuming during a social gathering, but it may still be uncertain whether it should vacuum before or after having guests. In such cases, integrating common-sense knowledge with human preferences, often conveyed through human explanations, is fundamental yet a challenge for existing systems. In this paper, we introduce GRACE, a novel approach addressing this while generating socially appropriate robot actions. GRACE leverages common sense knowledge from LLMs, and it integrates this knowledge with human explanations through a generative network. The bidirectional structure of GRACE enables robots to refine and enhance LLM predictions by utilizing human explanations and makes robots capable of generating such explanations for human-specified actions. Our evaluations show that integrating human explanations boosts GRACE's performance, where it outperforms several baselines and provides sensible explanations.