Visual Preference Inference: An Image Sequence-Based Preference Reasoning in Tabletop Object Manipulation
Proposes CoVR method leveraging visual residuals and large multimodal models for preference inference, achieving 72% success in simulation.
Key Findings
Methodology
This paper introduces Chain-of-Visual-Residuals (CoVR), combining Visual Reasoning Descriptors (VRD) and Preference Reasoning Descriptors (PRD). Using GPT-4V, the approach converts consecutive images into natural language descriptions capturing semantic and geometric changes. These descriptions form a visual residual chain, which, together with the image sequence, feeds into a preference inference module. The system employs template-based prompts to chain reasoning steps, enabling dynamic scene understanding and preference prediction. Experiments demonstrate superior performance over baseline models in both simulation and real-world tasks, with success rates exceeding 70%.
Key Results
- In simulation, CoVR achieved 72% success in visual residual prediction, outperforming naive models (40%). It accurately identified spatial and semantic preferences, with over 85% success in object rearrangement tasks, especially in color and shape recognition.
- In real-world experiments with tabletop objects, the model reached 63% success in preference inference, significantly better than baseline models (28%), confirming robustness in practical scenarios.
- Ablation studies showed that VRD and PRD modules contribute over 30% improvements individually, validating the effectiveness of the visual residual chain approach.
Significance
This work advances the integration of multimodal large models into robotic perception, enabling continuous scene understanding and human-like preference inference. It addresses the challenge of dynamic scene comprehension, crucial for natural human-robot interaction, and paves the way for autonomous systems capable of personalized assistance in industrial, service, and domestic environments.
Technical Contribution
The key technical innovation is the visual residual chain mechanism, which transforms sequential image changes into descriptive language, facilitating continuous reasoning. The combination of VRD and PRD within a multimodal large language model framework enables end-to-end preference inference without manual feature engineering, representing a significant step beyond prior static or handcrafted approaches.
Novelty
This is the first work to incorporate a chain-of-residuals approach for dynamic scene understanding in preference inference tasks, leveraging multimodal models to interpret continuous visual changes. Unlike previous static feature-based methods, it enables real-time, end-to-end reasoning about user preferences from raw image sequences, offering a new paradigm in robotic perception.
Limitations
- The approach relies heavily on the quality and continuity of image sequences; viewpoint changes or occlusions can impair description accuracy, leading to incorrect preference inference.
- Current computational costs are high, limiting real-time deployment in resource-constrained robots.
- Handling highly complex scenes with multiple objects and interactions remains challenging, requiring further model enhancements.
Future Work
Future directions include integrating human feedback to correct and refine preference models, expanding multimodal inputs such as tactile or auditory cues, and optimizing model architectures for real-time applications. Additionally, extending the framework to multi-agent scenarios and more complex environments will broaden its applicability.
AI Executive Summary
Understanding human preferences is fundamental for developing robots that interact naturally and effectively with people. Traditional approaches relied on manually crafted features, which often failed to capture the subtle and dynamic aspects of scene changes. This paper introduces the Visual Preference Inference (VPI) task, which leverages continuous image sequences to automatically infer user preferences through advanced visual reasoning.
The core innovation is the Chain-of-Visual-Residuals (CoVR) method, which employs a two-stage process: Visual Reasoning Descriptors (VRD) convert image pairs into descriptive language capturing semantic and geometric changes, while Preference Reasoning Descriptors (PRD) use these descriptions to infer user preferences. This chain-based approach allows the model to understand scene evolution over time, addressing the limitations of static feature-based methods.
Experiments conducted in both simulated and real-world tabletop environments demonstrate the effectiveness of the approach. In object rearrangement and semantic grouping tasks, the model achieves success rates above 70%, significantly outperforming baseline methods. Notably, in real-world object preference recognition, the success rate reaches 63%, validating its practical applicability.
This work marks a significant step forward in robotic perception, enabling systems to interpret human preferences dynamically and accurately. By integrating multimodal large models with a novel residual chaining mechanism, it opens new avenues for personalized, adaptive robot behavior. Future work will focus on incorporating human feedback, reducing computational costs, and expanding to more complex multi-object scenarios, ultimately aiming for robots that learn and adapt seamlessly in everyday environments.
Deep Analysis
Background
随着机器人在工业、服务和家庭中的应用不断扩大，理解人类偏好成为提升交互自然性和效率的关键。早期研究多依赖手工特征设计，如运动轨迹、速度等，难以捕捉场景中的微妙变化。近年来，随着多模态大模型的发展，视觉理解能力显著增强，特别是在复杂多目标关系理解方面取得突破。代表性工作如GPT-4V、3D-Aware模型等，已在静态场景中表现优异，但连续场景中的偏好推断仍面临挑战，主要因动态变化难以捕捉，缺乏有效的连续推理机制。
Core Problem
核心问题在于如何从连续图像序列中自动、准确地推断出用户偏好，尤其是在场景动态变化、物体关系复杂的情况下。传统方法多依赖预定义特征或单一模态信息，难以适应多变环境。现有模型在理解连续场景中的细微变化、捕获对象关系演变方面表现不足，限制了其在实际机器人操作中的应用。解决这一问题需要引入更强的连续推理机制，结合多模态信息，实现端到端的偏好识别。
Innovation
本研究的创新点包括：1）引入视觉残差链机制，将连续图像的变化转化为描述对象属性和关系变化的自然语言描述，增强模型对场景动态的敏感性；2）结合多模态大模型（如GPT-4V）实现端到端连续场景理解，避免手工特征设计；3）提出模板化的VRD和PRD机制，逐步链式推理，提升偏好推断的准确性和泛化能力。这些创新使得模型能在复杂、多变的场景中实现高效偏好识别，突破了传统静态特征依赖的限制。
Methodology
- �� 输入连续图像序列，利用预训练多模态模型（GPT-4V）进行特征提取。
- �� 通过模板化提示，将每对连续图像转化为描述对象属性和关系变化的自然语言（VRD），包括语义、几何和描述三部分。
- �� 利用链式结构，将连续图像的描述逐步连接，形成视觉残差链，捕获场景中的动态变化。
- �� 将视觉残差链与原始图像序列结合，输入偏好推断模块（PRD），结合预定义偏好集合或开放式偏好，推断用户偏好。
- �� 在模拟和真实环境中进行多场景测试，评估偏好推断的准确率和鲁棒性。
Experiments
采用模拟环境中的块、多边形和日常物品操控任务，使用GPT-4V作为视觉推理基础模型。对比线性偏好提取器、无VRD模型等多种基线，采用成功率（SRVRD、SRPRD）作为评估指标。实验设计包括不同偏好类型（空间、语义）和场景复杂度，进行多次重复验证。超参数设置包括图像对的提示模板、模型推理深度等。通过消融实验验证VRD和PRD的贡献，分析模型在不同场景中的表现差异。
Results
实验证明，CoVR模型在视觉推理任务中成功率达72%，优于无VRD模型的40%。偏好推断成功率在多对象操控中达85%，在空间关系和颜色偏好识别中表现优异。真实场景中偏好识别成功率达63%，优于对比模型。模型在复杂场景中的动态理解能力得到验证，展现出强大的泛化能力和实用潜力。
Applications
该方法可应用于工业机器人、服务机器人、智能制造等场景，实现更自然的人机交互。只需连续图像输入，无需手工特征设计，便能实现偏好识别。未来可结合人类反馈，优化偏好适应性，推动机器人自主学习和个性化定制。
Limitations & Outlook
模型对连续图像序列的质量敏感，视角变化或遮挡可能影响描述准确性。复杂场景下推理能力仍有限，需引入更强的场景理解机制。计算成本较高，实时应用仍需优化。未来需解决多模态融合和多目标场景的挑战。
Plain Language Accessible to non-experts
想象你在厨房里做饭，每次你都用不同的碗、盘子和食材。机器人就像你的助手，它需要知道你喜欢什么样的食材、喜欢把菜放在哪个位置。这个研究就像教机器人观察你每次做饭的过程，然后学会你喜欢的菜的摆放和搭配方式。它通过看一系列图片，理解你喜欢的颜色、形状和摆放规律，就像你告诉朋友你喜欢的菜谱一样。这样，机器人就能根据你的偏好，自动帮你整理厨房或准备食材，变得越来越聪明，懂得你的喜好。这个方法特别厉害，因为它不用提前告诉机器人具体偏好，而是让它自己从观察中学会，像一个聪明的厨师助手一样，帮你做出你喜欢的饭菜。
ELI14 Explained like you're 14
想象你在玩一款游戏，你的朋友会观察你每次操作的动作，然后猜出你喜欢用哪个角色、喜欢什么样的装备。这个研究就像让机器人变成你的游戏伙伴，它通过看你玩了几次后，学会你喜欢的角色和策略。它会看你每次移动的角色、用的技能，然后用一种特别的方式把这些动作变成描述，比如“你喜欢用火焰技能攻击”，或者“你喜欢站在角落里观察”。接着，机器人会根据这些描述，猜出你最喜欢的玩法，甚至帮你安排下一步的策略。这样，机器人变得越来越懂你，能和你一起玩得更开心。这个方法的厉害之处在于，它不用你直接告诉它喜欢什么，而是通过观察你的动作，自己学会了你的偏好，就像一个聪明的朋友一样。
Abstract
In robotic object manipulation, human preferences can often be influenced by the visual attributes of objects, such as color and shape. These properties play a crucial role in operating a robot to interact with objects and align with human intention. In this paper, we focus on the problem of inferring underlying human preferences from a sequence of raw visual observations in tabletop manipulation environments with a variety of object types, named Visual Preference Inference (VPI). To facilitate visual reasoning in the context of manipulation, we introduce the Chain-of-Visual-Residuals (CoVR) method. CoVR employs a prompting mechanism that describes the difference between the consecutive images (i.e., visual residuals) and incorporates such texts with a sequence of images to infer the user's preference. This approach significantly enhances the ability to understand and adapt to dynamic changes in its visual environment during manipulation tasks. Furthermore, we incorporate such texts along with a sequence of images to infer the user's preferences. Our method outperforms baseline methods in terms of extracting human preferences from visual sequences in both simulation and real-world environments. Code and videos are available at: \href{https://joonhyung-lee.github.io/vpi/}{https://joonhyung-lee.github.io/vpi/}